diff --git a/ADK-Crash-Course/1-basic-agent/README.md b/ADK-Crash-Course/1-basic-agent/README.md
new file mode 100644
index 000000000..dc59b5bfe
--- /dev/null
+++ b/ADK-Crash-Course/1-basic-agent/README.md
@@ -0,0 +1,124 @@
+# Basic ADK Agent Example
+
+## What is an ADK Agent?
+
+The `LlmAgent` (often aliased simply as `Agent`) is a core component in ADK that acts as the "thinking" part of your application. It leverages the power of a Large Language Model (LLM) for:
+- Reasoning
+- Understanding natural language
+- Making decisions
+- Generating responses
+- Interacting with tools
+
+Unlike deterministic workflow agents that follow predefined paths, an `LlmAgent`'s behavior is non-deterministic. It uses the LLM to interpret instructions and context, deciding dynamically how to proceed, which tools to use (if any), or whether to transfer control to another agent.
+
+## Required Agent Structure
+
+For ADK to discover and run your agents properly (especially with `adk web`), your project must follow a specific structure:
+
+```
+parent_folder/
+    agent_folder/         # This is your agent's package directory
+        __init__.py       # Must import agent.py
+        agent.py          # Must define root_agent
+        .env              # Environment variables
+```
+
+### Essential Components:
+
+1. **`__init__.py`**
+   - Must import the agent module: `from . import agent`
+   - This makes your agent discoverable by ADK
+
+2. **`agent.py`**
+   - Must define a variable named `root_agent`
+   - This is the entry point that ADK uses to find your agent
+
+3. **Command Location**
+   - Always run `adk` commands from the parent directory, not from inside the agent directory
+   - Example: Run `adk web` from the parent folder that contains your agent folder
+
+This structure ensures that ADK can automatically discover and load your agent when running commands like `adk web` or `adk run`.
+
+## Key Components
+
+### 1. Identity (`name` and `description`)
+- **name** (Required): A unique string identifier for your agent
+- **description** (Optional, but recommended): A concise summary of the agent's capabilities. Used for other agents to determine if they should route a task to this agent.
+
+### 2. Model (`model`)
+- Specifies which LLM powers the agent (e.g., "gemini-2.0-flash")
+- Affects the agent's capabilities, cost, and performance
+
+### 3. Instructions (`instruction`)
+The most critical parameter for shaping your agent's behavior. It defines:
+- Core task or goal
+- Personality or persona
+- Behavioral constraints
+- How to use available tools
+- Desired output format
+
+### 4. Tools (`tools`)
+Optional capabilities beyond the LLM's built-in knowledge, allowing the agent to:
+- Interact with external systems
+- Perform calculations
+- Fetch real-time data
+- Execute specific actions
+
+## Getting Started
+
+This example uses the same virtual environment created in the root directory. Make sure you have:
+
+1. Activated the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Set up your API key:
+   - Rename `.env.example` to `.env` in the greeting_agent folder
+   - Add your Google API key to the `GOOGLE_API_KEY` variable in the `.env` file
+
+## Running the Example
+
+To run this basic agent example, you'll use the ADK CLI tool which provides several ways to interact with your agent:
+
+1. Navigate to the 1-basic-agent directory containing your agent folder.
+2. Start the interactive web UI:
+```bash
+adk web
+```
+
+3. Access the web UI by opening the URL shown in your terminal (typically http://localhost:8000)
+
+4. Select your agent from the dropdown menu in the top-left corner of the UI
+
+5. Start chatting with your agent in the textbox at the bottom of the screen
+
+### Troubleshooting
+
+If your agent doesn't appear in the dropdown menu:
+- Make sure you're running `adk web` from the parent directory (1-basic-agent), not from inside the agent directory
+- Check that your `__init__.py` properly imports the agent module
+- Verify that `agent.py` defines a variable named `root_agent`
+
+### Alternative Run Methods
+
+The ADK CLI tool provides several options:
+
+- **`adk web`**: Launches an interactive web UI for testing your agent with a chat interface
+- **`adk run [agent_name]`**: Runs your agent directly in the terminal
+- **`adk api_server`**: Starts a FastAPI server to test API requests to your agent
+
+### Example Prompts to Try
+
+- "How do you say hello in Spanish?"
+- "What's a formal greeting in Japanese?"
+- "Tell me how to greet someone in French"
+
+You can exit the conversation or stop the server by pressing `Ctrl+C` in your terminal.
+
+This example demonstrates a simple agent that responds to greeting-related queries, showing the fundamentals of agent creation with ADK.
diff --git a/ADK-Crash-Course/1-basic-agent/greeting_agent/__init__.py b/ADK-Crash-Course/1-basic-agent/greeting_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/1-basic-agent/greeting_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/1-basic-agent/greeting_agent/agent.py b/ADK-Crash-Course/1-basic-agent/greeting_agent/agent.py
new file mode 100644
index 000000000..5e0e7fce6
--- /dev/null
+++ b/ADK-Crash-Course/1-basic-agent/greeting_agent/agent.py
@@ -0,0 +1,12 @@
+from google.adk.agents import Agent
+
+root_agent = Agent(
+    name="greeting_agent",
+    # https://ai.google.dev/gemini-api/docs/models
+    model= "gemini-2.0-flash-exp",# "gemini-2.0-flash-live-preview-04-09",  #"gemini-2.0-flash-exp", 
+    description="Greeting agent",
+    instruction="""
+    You are an interesting and engaging conversationalist. you will ask the users name and instegate a conversation with 
+    user.
+    """,
+)
diff --git a/ADK-Crash-Course/10-sequential-agent/README.md b/ADK-Crash-Course/10-sequential-agent/README.md
new file mode 100644
index 000000000..bb256ff1e
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/README.md
@@ -0,0 +1,140 @@
+# Sequential Agents in ADK
+
+This example demonstrates how to implement a Sequential Agent in the Agent Development Kit (ADK). The main agent in this example, `lead_qualification_agent`, is a Sequential Agent that executes sub-agents in a predefined order, with each agent's output feeding into the next agent in the sequence.
+
+## What are Sequential Agents?
+
+Sequential Agents are workflow agents in ADK that:
+
+1. **Execute in a Fixed Order**: Sub-agents run one after another in the exact sequence they are specified
+2. **Pass Data Between Agents**: Using state management to pass information from one sub-agent to the next
+3. **Create Processing Pipelines**: Perfect for scenarios where each step depends on the previous step's output
+
+Use Sequential Agents when you need a deterministic, step-by-step workflow where the execution order matters.
+
+## Lead Qualification Pipeline Example
+
+In this example, we've created `lead_qualification_agent` as a Sequential Agent that implements a lead qualification pipeline for sales teams. This Sequential Agent orchestrates three specialized sub-agents:
+
+1. **Lead Validator Agent**: Checks if the lead information is complete enough for qualification
+   - Validates for required information like contact details and interest
+   - Outputs a simple "valid" or "invalid" with a reason
+
+2. **Lead Scorer Agent**: Scores valid leads on a scale of 1-10
+   - Analyzes factors like urgency, decision-making authority, budget, and timeline
+   - Provides a numeric score with a brief justification
+
+3. **Action Recommender Agent**: Suggests next steps based on the validation and score
+   - For invalid leads: Recommends what information to gather
+   - For low-scoring leads (1-3): Suggests nurturing actions
+   - For medium-scoring leads (4-7): Suggests qualifying actions
+   - For high-scoring leads (8-10): Suggests sales actions
+
+### How It Works
+
+The `lead_qualification_agent` Sequential Agent orchestrates this process by:
+
+1. Running the Validator first to determine if the lead is complete
+2. Running the Scorer next (which can access validation results via state)
+3. Running the Recommender last (which can access both validation and scoring results)
+
+The output of each sub-agent is stored in the session state using the `output_key` parameter:
+- `validation_status`
+- `lead_score`
+- `action_recommendation`
+
+## Project Structure
+
+```
+9-sequential-agent/
+│
+├── lead_qualification_agent/       # Main Sequential Agent package
+│   ├── __init__.py                 # Package initialization
+│   ├── agent.py                    # Sequential Agent definition (root_agent)
+│   │
+│   └── subagents/                  # Sub-agents folder
+│       ├── __init__.py             # Sub-agents initialization
+│       │
+│       ├── validator/              # Lead validation agent
+│       │   ├── __init__.py
+│       │   └── agent.py
+│       │
+│       ├── scorer/                 # Lead scoring agent
+│       │   ├── __init__.py
+│       │   └── agent.py
+│       │
+│       └── recommender/            # Action recommendation agent
+│           ├── __init__.py
+│           └── agent.py
+│
+├── .env.example                    # Environment variables example
+└── README.md                       # This documentation
+```
+
+## Getting Started
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Copy the `.env.example` file to `.env` and add your Google API key:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Example
+
+```bash
+cd 9-sequential-agent
+adk web
+```
+
+Then select "lead_qualification_agent" from the dropdown menu in the web UI.
+
+## Example Interactions
+
+Try these example interactions:
+
+### Qualified Lead Example:
+```
+Lead Information:
+Name: Sarah Johnson
+Email: sarah.j@techinnovate.com
+Phone: 555-123-4567
+Company: Tech Innovate Solutions
+Position: CTO
+Interest: Looking for an AI solution to automate customer support
+Budget: $50K-100K available for the right solution
+Timeline: Hoping to implement within next quarter
+Notes: Currently using a competitor's product but unhappy with performance
+```
+
+### Unqualified Lead Example:
+```
+Lead Information:
+Name: John Doe
+Email: john@gmail.com
+Interest: Something with AI maybe
+Notes: Met at conference, seemed interested but was vague about needs
+```
+
+## How Sequential Agents Compare to Other Workflow Agents
+
+ADK offers different types of workflow agents for different needs:
+
+- **Sequential Agents**: For strict, ordered execution (like this example)
+- **Loop Agents**: For repeated execution of sub-agents based on conditions
+- **Parallel Agents**: For concurrent execution of independent sub-agents
+
+## Additional Resources
+
+- [ADK Sequential Agents Documentation](https://google.github.io/adk-docs/agents/workflow-agents/sequential-agents/)
+- [Full Code Development Pipeline Example](https://google.github.io/adk-docs/agents/workflow-agents/sequential-agents/#full-example-code-development-pipeline) 
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/.env.example b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/.env.example
new file mode 100644
index 000000000..9b0ce4e74
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/.env.example
@@ -0,0 +1,2 @@
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=your_api_key_here 
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/__init__.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/agent.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/agent.py
new file mode 100644
index 000000000..d7dcce036
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/agent.py
@@ -0,0 +1,21 @@
+"""
+Sequential Agent with a Minimal Callback
+
+This example demonstrates a lead qualification pipeline with a minimal
+before_agent_callback that only initializes state once at the beginning.
+"""
+
+from google.adk.agents import SequentialAgent
+
+from .subagents.recommender import action_recommender_agent
+from .subagents.scorer import lead_scorer_agent
+
+# Import the subagents
+from .subagents.validator import lead_validator_agent
+
+# Create the sequential agent with minimal callback
+root_agent = SequentialAgent(
+    name="LeadQualificationPipeline",
+    sub_agents=[lead_validator_agent, lead_scorer_agent, action_recommender_agent],
+    description="A pipeline that validates, scores, and recommends actions for sales leads",
+)
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/__init__.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/__init__.py
new file mode 100644
index 000000000..fdc1384d0
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/__init__.py
@@ -0,0 +1,3 @@
+"""Subagents for the lead qualification pipeline."""
+
+from . import recommender, scorer, validator
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/recommender/__init__.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/recommender/__init__.py
new file mode 100644
index 000000000..7f22e5bfb
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/recommender/__init__.py
@@ -0,0 +1,3 @@
+"""Recommender agent for lead qualification."""
+
+from .agent import action_recommender_agent
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/recommender/agent.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/recommender/agent.py
new file mode 100644
index 000000000..6664ce5ce
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/recommender/agent.py
@@ -0,0 +1,36 @@
+"""
+Action Recommender Agent
+
+This agent is responsible for recommending appropriate next actions
+based on the lead validation and scoring results.
+"""
+
+from google.adk.agents import LlmAgent
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Create the recommender agent
+action_recommender_agent = LlmAgent(
+    name="ActionRecommenderAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are an Action Recommendation AI.
+    
+    Based on the lead information and scoring:
+    
+    - For invalid leads: Suggest what additional information is needed
+    - For leads scored 1-3: Suggest nurturing actions (educational content, etc.)
+    - For leads scored 4-7: Suggest qualifying actions (discovery call, needs assessment)
+    - For leads scored 8-10: Suggest sales actions (demo, proposal, etc.)
+    
+    Format your response as a complete recommendation to the sales team.
+    
+    Lead Score:
+    {lead_score}
+
+    Lead Validation Status:
+    {validation_status}
+    """,
+    description="Recommends next actions based on lead qualification.",
+    output_key="action_recommendation",
+)
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/scorer/__init__.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/scorer/__init__.py
new file mode 100644
index 000000000..d33b60173
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/scorer/__init__.py
@@ -0,0 +1,3 @@
+"""Scorer agent for lead qualification."""
+
+from .agent import lead_scorer_agent
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/scorer/agent.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/scorer/agent.py
new file mode 100644
index 000000000..42e0b7706
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/scorer/agent.py
@@ -0,0 +1,32 @@
+"""
+Lead Scorer Agent
+
+This agent is responsible for scoring a lead's qualification level
+based on various criteria.
+"""
+
+from google.adk.agents import LlmAgent
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Create the scorer agent
+lead_scorer_agent = LlmAgent(
+    name="LeadScorerAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are a Lead Scoring AI.
+    
+    Analyze the lead information and assign a qualification score from 1-10 based on:
+    - Expressed need (urgency/clarity of problem)
+    - Decision-making authority
+    - Budget indicators
+    - Timeline indicators
+    
+    Output ONLY a numeric score and ONE sentence justification.
+    
+    Example output: '8: Decision maker with clear budget and immediate need'
+    Example output: '3: Vague interest with no timeline or budget mentioned'
+    """,
+    description="Scores qualified leads on a scale of 1-10.",
+    output_key="lead_score",
+)
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/validator/__init__.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/validator/__init__.py
new file mode 100644
index 000000000..7eec99e47
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/validator/__init__.py
@@ -0,0 +1,3 @@
+"""Validator agent for lead qualification."""
+
+from .agent import lead_validator_agent
diff --git a/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/validator/agent.py b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/validator/agent.py
new file mode 100644
index 000000000..9d576d1fa
--- /dev/null
+++ b/ADK-Crash-Course/10-sequential-agent/lead_qualification_agent/subagents/validator/agent.py
@@ -0,0 +1,32 @@
+"""
+Lead Validator Agent
+
+This agent is responsible for validating if a lead has all the necessary information
+for qualification.
+"""
+
+from google.adk.agents import LlmAgent
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Create the validator agent
+lead_validator_agent = LlmAgent(
+    name="LeadValidatorAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are a Lead Validation AI.
+    
+    Examine the lead information provided by the user and determine if it's complete enough for qualification.
+    A complete lead should include:
+    - Contact information (name, email or phone)
+    - Some indication of interest or need
+    - Company or context information if applicable
+    
+    Output ONLY 'valid' or 'invalid' with a single reason if invalid.
+    
+    Example valid output: 'valid'
+    Example invalid output: 'invalid: missing contact information'
+    """,
+    description="Validates lead information for completeness.",
+    output_key="validation_status",
+)
diff --git a/ADK-Crash-Course/11-parallel-agent/README.md b/ADK-Crash-Course/11-parallel-agent/README.md
new file mode 100644
index 000000000..cd63175ac
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/README.md
@@ -0,0 +1,154 @@
+# Parallel Agents in ADK
+
+This example demonstrates how to implement a Parallel Agent in the Agent Development Kit (ADK). The main agent in this example, `system_monitor_agent`, uses a Parallel Agent to gather system information concurrently and then synthesizes it into a comprehensive system health report.
+
+## What are Parallel Agents?
+
+Parallel Agents are workflow agents in ADK that:
+
+1. **Execute Concurrently**: Sub-agents run simultaneously rather than sequentially
+2. **Operate Independently**: Each sub-agent works independently without sharing state during execution
+3. **Improve Performance**: Dramatically speed up workflows where tasks can be performed in parallel
+
+Use Parallel Agents when you need to execute multiple independent tasks efficiently and time is a critical factor.
+
+## System Monitoring Example
+
+In this example, we've created a system monitoring application that uses a Parallel Agent to gather system information. The workflow consists of:
+
+1. **Parallel System Information Gathering**: Using a `ParallelAgent` to concurrently collect data about:
+   - CPU usage and statistics
+   - Memory utilization
+   - Disk space and usage
+
+2. **Sequential Report Synthesis**: After parallel data collection, a synthesizer agent combines all information into a comprehensive report
+
+### Sub-Agents
+
+1. **CPU Info Agent**: Collects and analyzes CPU information
+   - Retrieves core counts, usage statistics, and performance metrics
+   - Identifies potential performance issues (high CPU usage)
+
+2. **Memory Info Agent**: Gathers memory usage information
+   - Collects total, used, and available memory
+   - Analyzes memory pressure and swap usage
+
+3. **Disk Info Agent**: Analyzes disk space and usage
+   - Reports on total, used, and free disk space
+   - Identifies disks that are running low on space
+
+4. **System Report Synthesizer**: Combines all gathered information into a comprehensive system health report
+   - Creates an executive summary of system health
+   - Organizes component-specific information into sections
+   - Provides recommendations based on system metrics
+
+### How It Works
+
+The architecture combines both parallel and sequential workflow patterns:
+
+1. First, the `system_info_gatherer` Parallel Agent runs all three information agents concurrently
+2. Then, the `system_report_synthesizer` uses the collected data to generate a final report
+
+This hybrid approach demonstrates how to combine workflow agent types for optimal performance and logical flow.
+
+## Project Structure
+
+```
+10-parallel-agent/
+│
+├── system_monitor_agent/          # Main System Monitor Agent package
+│   ├── __init__.py                # Package initialization
+│   ├── agent.py                   # Agent definitions (root_agent)
+│   │
+│   └── subagents/                 # Sub-agents folder
+│       ├── __init__.py            # Sub-agents initialization
+│       │
+│       ├── cpu_info_agent/        # CPU information agent
+│       │   ├── __init__.py
+│       │   ├── agent.py
+│       │   └── tools.py           # CPU info collection tools
+│       │
+│       ├── memory_info_agent/     # Memory information agent
+│       │   ├── __init__.py
+│       │   ├── agent.py
+│       │   └── tools.py           # Memory info collection tools
+│       │
+│       ├── disk_info_agent/       # Disk information agent
+│       │   ├── __init__.py
+│       │   ├── agent.py
+│       │   └── tools.py           # Disk info collection tools
+│       │
+│       └── synthesizer_agent/     # Report synthesizing agent
+│           ├── __init__.py
+│           └── agent.py
+│
+├── .env.example                   # Environment variables example
+└── README.md                      # This documentation
+```
+
+## Getting Started
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Copy the `.env.example` file to `.env` and add your Google API key:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Example
+
+```bash
+cd 10-parallel-agent
+adk web
+```
+
+Then select "system_monitor_agent" from the dropdown menu in the web UI.
+
+## Example Interactions
+
+Try these example prompts:
+
+```
+Check my system health
+```
+
+```
+Provide a comprehensive system report with recommendations
+```
+
+```
+Is my system running out of memory or disk space?
+```
+
+## Key Concepts: Independent Execution
+
+One key aspect of Parallel Agents is that **sub-agents run independently without sharing state during execution**. In this example:
+
+1. Each information gathering agent operates in isolation
+2. The results from each agent are collected after parallel execution completes
+3. The synthesizer agent then uses these collected results to create the final report
+
+This approach is ideal for scenarios where tasks are completely independent and don't require interaction during execution.
+
+## How Parallel Agents Compare to Other Workflow Agents
+
+ADK offers different types of workflow agents for different needs:
+
+- **Sequential Agents**: For strict, ordered execution where each step depends on previous outputs
+- **Loop Agents**: For repeated execution of sub-agents based on conditions
+- **Parallel Agents**: For concurrent execution of independent sub-agents (like this example)
+
+## Additional Resources
+
+- [ADK Parallel Agents Documentation](https://google.github.io/adk-docs/agents/workflow-agents/parallel-agents/)
+- [Full Example: Parallel Web Research](https://google.github.io/adk-docs/agents/workflow-agents/parallel-agents/#full-example-parallel-web-research) 
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/__init__.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/__init__.py
new file mode 100644
index 000000000..d5e4b8e11
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/__init__.py
@@ -0,0 +1,8 @@
+"""
+System Monitor Agent Package
+
+This package provides a system monitoring agent that gathers system information
+and produces a comprehensive system health report.
+"""
+
+from .agent import root_agent
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/agent.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/agent.py
new file mode 100644
index 000000000..739fa9300
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/agent.py
@@ -0,0 +1,26 @@
+"""
+System Monitor Root Agent
+
+This module defines the root agent for the system monitoring application.
+It uses a parallel agent for system information gathering and a sequential
+pipeline for the overall flow.
+"""
+
+from google.adk.agents import ParallelAgent, SequentialAgent
+
+from .subagents.cpu_info_agent import cpu_info_agent
+from .subagents.disk_info_agent import disk_info_agent
+from .subagents.memory_info_agent import memory_info_agent
+from .subagents.synthesizer_agent import system_report_synthesizer
+
+# --- 1. Create Parallel Agent to gather information concurrently ---
+system_info_gatherer = ParallelAgent(
+    name="system_info_gatherer",
+    sub_agents=[cpu_info_agent, memory_info_agent, disk_info_agent],
+)
+
+# --- 2. Create Sequential Pipeline to gather info in parallel, then synthesize ---
+root_agent = SequentialAgent(
+    name="system_monitor_agent",
+    sub_agents=[system_info_gatherer, system_report_synthesizer],
+)
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/__init__.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/__init__.py
new file mode 100644
index 000000000..48f0b0eed
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/__init__.py
@@ -0,0 +1,3 @@
+"""Subagents for the system monitor pipeline."""
+
+from . import cpu_info_agent, disk_info_agent, memory_info_agent, synthesizer_agent
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/__init__.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/__init__.py
new file mode 100644
index 000000000..f4e37c401
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/__init__.py
@@ -0,0 +1,3 @@
+"""CPU info agent for system monitoring."""
+
+from .agent import cpu_info_agent
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/agent.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/agent.py
new file mode 100644
index 000000000..d4ff91290
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/agent.py
@@ -0,0 +1,40 @@
+"""
+CPU Information Agent
+
+This agent is responsible for gathering and analyzing CPU information.
+"""
+
+from google.adk.agents import LlmAgent
+
+from .tools import get_cpu_info
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# CPU Information Agent
+cpu_info_agent = LlmAgent(
+    name="CpuInfoAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are a CPU Information Agent.
+    
+    When asked for system information, you should:
+    1. Use the 'get_cpu_info' tool to gather CPU data
+    2. Analyze the returned dictionary data
+    3. Format this information into a concise, clear section of a system report
+    
+    The tool will return a dictionary with:
+    - result: Core CPU information
+    - stats: Key statistical data about CPU usage
+    - additional_info: Context about the data collection
+    
+    Format your response as a well-structured report section with:
+    - CPU core information (physical vs logical)
+    - CPU usage statistics
+    - Any performance concerns (high usage > 80%)
+    
+    IMPORTANT: You MUST call the get_cpu_info tool. Do not make up information.
+    """,
+    description="Gathers and analyzes CPU information",
+    tools=[get_cpu_info],
+    output_key="cpu_info",
+)
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/tools.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/tools.py
new file mode 100644
index 000000000..1ab05a872
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/cpu_info_agent/tools.py
@@ -0,0 +1,60 @@
+"""
+CPU Information Tool
+
+This module provides a tool for gathering CPU information.
+"""
+
+import time
+from typing import Any, Dict
+
+import psutil
+
+
+def get_cpu_info() -> Dict[str, Any]:
+    """
+    Gather CPU information including core count and usage.
+
+    Returns:
+        Dict[str, Any]: Dictionary with CPU information structured for ADK
+    """
+    try:
+        # Get CPU information
+        cpu_info = {
+            "physical_cores": psutil.cpu_count(logical=False),
+            "logical_cores": psutil.cpu_count(logical=True),
+            "cpu_usage_per_core": [
+                f"Core {i}: {percentage:.1f}%"
+                for i, percentage in enumerate(
+                    psutil.cpu_percent(interval=1, percpu=True)
+                )
+            ],
+            "avg_cpu_usage": f"{psutil.cpu_percent(interval=1):.1f}%",
+        }
+
+        # Calculate some stats for the result summary
+        avg_usage = float(cpu_info["avg_cpu_usage"].strip("%"))
+        high_usage = avg_usage > 80
+
+        # Format for ADK tool return structure
+        return {
+            "result": cpu_info,
+            "stats": {
+                "physical_cores": cpu_info["physical_cores"],
+                "logical_cores": cpu_info["logical_cores"],
+                "avg_usage_percentage": avg_usage,
+                "high_usage_alert": high_usage,
+            },
+            "additional_info": {
+                "data_format": "dictionary",
+                "collection_timestamp": time.time(),
+                "performance_concern": (
+                    "High CPU usage detected" if high_usage else None
+                ),
+            },
+        }
+    except Exception as e:
+        return {
+            "result": {"error": f"Failed to gather CPU information: {str(e)}"},
+            "stats": {"success": False},
+            "additional_info": {"error_type": str(type(e).__name__)},
+        }
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/__init__.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/__init__.py
new file mode 100644
index 000000000..008cf6aae
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/__init__.py
@@ -0,0 +1,3 @@
+"""Disk info agent for system monitoring."""
+
+from .agent import disk_info_agent
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/agent.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/agent.py
new file mode 100644
index 000000000..b51585da4
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/agent.py
@@ -0,0 +1,40 @@
+"""
+Disk Information Agent
+
+This agent is responsible for gathering and analyzing disk information.
+"""
+
+from google.adk.agents import LlmAgent
+
+from .tools import get_disk_info
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Disk Information Agent
+disk_info_agent = LlmAgent(
+    name="DiskInfoAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are a Disk Information Agent.
+    
+    When asked for system information, you should:
+    1. Use the 'get_disk_info' tool to gather disk data
+    2. Analyze the returned dictionary data
+    3. Format this information into a concise, clear section of a system report
+    
+    The tool will return a dictionary with:
+    - result: Core disk information including partitions
+    - stats: Key statistical data about storage usage
+    - additional_info: Context about the data collection
+    
+    Format your response as a well-structured report section with:
+    - Partition information
+    - Storage capacity and usage
+    - Any storage concerns (high usage > 85%)
+    
+    IMPORTANT: You MUST call the get_disk_info tool. Do not make up information.
+    """,
+    description="Gathers and analyzes disk information",
+    tools=[get_disk_info],
+    output_key="disk_info",
+)
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/tools.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/tools.py
new file mode 100644
index 000000000..61622a84c
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/disk_info_agent/tools.py
@@ -0,0 +1,84 @@
+"""
+Disk Information Tool
+
+This module provides a tool for gathering disk information.
+"""
+
+import time
+from typing import Any, Dict
+
+import psutil
+
+
+def get_disk_info() -> Dict[str, Any]:
+    """
+    Gather disk information including partitions and usage.
+
+    Returns:
+        Dict[str, Any]: Dictionary with disk information structured for ADK
+    """
+    try:
+        # Get disk information
+        disk_info = {"partitions": []}
+        partitions_over_threshold = []
+        total_space = 0
+        used_space = 0
+
+        for partition in psutil.disk_partitions():
+            try:
+                partition_usage = psutil.disk_usage(partition.mountpoint)
+
+                # Track high usage partitions
+                if partition_usage.percent > 85:
+                    partitions_over_threshold.append(
+                        f"{partition.mountpoint} ({partition_usage.percent:.1f}%)"
+                    )
+
+                # Add to totals
+                total_space += partition_usage.total
+                used_space += partition_usage.used
+
+                disk_info["partitions"].append(
+                    {
+                        "device": partition.device,
+                        "mountpoint": partition.mountpoint,
+                        "filesystem_type": partition.fstype,
+                        "total_size": f"{partition_usage.total / (1024 ** 3):.2f} GB",
+                        "used": f"{partition_usage.used / (1024 ** 3):.2f} GB",
+                        "free": f"{partition_usage.free / (1024 ** 3):.2f} GB",
+                        "percentage": f"{partition_usage.percent:.1f}%",
+                    }
+                )
+            except (PermissionError, FileNotFoundError):
+                # Some partitions may not be accessible
+                pass
+
+        # Calculate overall disk stats
+        overall_usage_percent = (
+            (used_space / total_space * 100) if total_space > 0 else 0
+        )
+
+        # Format for ADK tool return structure
+        return {
+            "result": disk_info,
+            "stats": {
+                "partition_count": len(disk_info["partitions"]),
+                "total_space_gb": total_space / (1024**3),
+                "used_space_gb": used_space / (1024**3),
+                "overall_usage_percent": overall_usage_percent,
+                "partitions_with_high_usage": len(partitions_over_threshold),
+            },
+            "additional_info": {
+                "data_format": "dictionary",
+                "collection_timestamp": time.time(),
+                "high_usage_partitions": (
+                    partitions_over_threshold if partitions_over_threshold else None
+                ),
+            },
+        }
+    except Exception as e:
+        return {
+            "result": {"error": f"Failed to gather disk information: {str(e)}"},
+            "stats": {"success": False},
+            "additional_info": {"error_type": str(type(e).__name__)},
+        }
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/__init__.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/__init__.py
new file mode 100644
index 000000000..f66041554
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/__init__.py
@@ -0,0 +1,3 @@
+"""Memory info agent for system monitoring."""
+
+from .agent import memory_info_agent
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/agent.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/agent.py
new file mode 100644
index 000000000..f4444bf2c
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/agent.py
@@ -0,0 +1,41 @@
+"""
+Memory Information Agent
+
+This agent is responsible for gathering and analyzing memory information.
+"""
+
+from google.adk.agents import LlmAgent
+
+from .tools import get_memory_info
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Memory Information Agent
+memory_info_agent = LlmAgent(
+    name="MemoryInfoAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are a Memory Information Agent.
+    
+    When asked for system information, you should:
+    1. Use the 'get_memory_info' tool to gather memory data
+    2. Analyze the returned dictionary data
+    3. Format this information into a concise, clear section of a system report
+    
+    The tool will return a dictionary with:
+    - result: Core memory information
+    - stats: Key statistical data about memory usage
+    - additional_info: Context about the data collection
+    
+    Format your response as a well-structured report section with:
+    - Total and available memory
+    - Memory usage statistics
+    - Swap memory information
+    - Any performance concerns (high usage > 80%)
+    
+    IMPORTANT: You MUST call the get_memory_info tool. Do not make up information.
+    """,
+    description="Gathers and analyzes memory information",
+    tools=[get_memory_info],
+    output_key="memory_info",
+)
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/tools.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/tools.py
new file mode 100644
index 000000000..472644395
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/memory_info_agent/tools.py
@@ -0,0 +1,64 @@
+"""
+Memory Information Tool
+
+This module provides a tool for gathering memory information.
+"""
+
+import time
+from typing import Any, Dict
+
+import psutil
+
+
+def get_memory_info() -> Dict[str, Any]:
+    """
+    Gather memory information including RAM and swap usage.
+
+    Returns:
+        Dict[str, Any]: Dictionary with memory information structured for ADK
+    """
+    try:
+        # Get memory information
+        memory = psutil.virtual_memory()
+        swap = psutil.swap_memory()
+
+        memory_info = {
+            "total_memory": f"{memory.total / (1024 ** 3):.2f} GB",
+            "available_memory": f"{memory.available / (1024 ** 3):.2f} GB",
+            "used_memory": f"{memory.used / (1024 ** 3):.2f} GB",
+            "memory_percentage": f"{memory.percent:.1f}%",
+            "swap_total": f"{swap.total / (1024 ** 3):.2f} GB",
+            "swap_used": f"{swap.used / (1024 ** 3):.2f} GB",
+            "swap_percentage": f"{swap.percent:.1f}%",
+        }
+
+        # Calculate stats
+        memory_usage = memory.percent
+        swap_usage = swap.percent
+        high_memory_usage = memory_usage > 80
+        high_swap_usage = swap_usage > 80
+
+        # Format for ADK tool return structure
+        return {
+            "result": memory_info,
+            "stats": {
+                "memory_usage_percentage": memory_usage,
+                "swap_usage_percentage": swap_usage,
+                "total_memory_gb": memory.total / (1024**3),
+                "available_memory_gb": memory.available / (1024**3),
+            },
+            "additional_info": {
+                "data_format": "dictionary",
+                "collection_timestamp": time.time(),
+                "performance_concern": (
+                    "High memory usage detected" if high_memory_usage else None
+                ),
+                "swap_concern": "High swap usage detected" if high_swap_usage else None,
+            },
+        }
+    except Exception as e:
+        return {
+            "result": {"error": f"Failed to gather memory information: {str(e)}"},
+            "stats": {"success": False},
+            "additional_info": {"error_type": str(type(e).__name__)},
+        }
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/synthesizer_agent/__init__.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/synthesizer_agent/__init__.py
new file mode 100644
index 000000000..e52e8d335
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/synthesizer_agent/__init__.py
@@ -0,0 +1,3 @@
+"""System report synthesizer agent for system monitoring."""
+
+from .agent import system_report_synthesizer
diff --git a/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/synthesizer_agent/agent.py b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/synthesizer_agent/agent.py
new file mode 100644
index 000000000..e458c7717
--- /dev/null
+++ b/ADK-Crash-Course/11-parallel-agent/system_monitor_agent/subagents/synthesizer_agent/agent.py
@@ -0,0 +1,33 @@
+"""
+System Report Synthesizer Agent
+
+This agent is responsible for synthesizing information from other agents
+to create a comprehensive system health report.
+"""
+
+from google.adk.agents import LlmAgent
+
+# --- Constants ---
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# System Report Synthesizer Agent
+system_report_synthesizer = LlmAgent(
+    name="SystemReportSynthesizer",
+    model=GEMINI_MODEL,
+    instruction="""You are a System Report Synthesizer.
+    
+    Your task is to create a comprehensive system health report by combining information from:
+    - CPU information: {cpu_info}
+    - Memory information: {memory_info}
+    - Disk information: {disk_info}
+    
+    Create a well-formatted report with:
+    1. An executive summary at the top with overall system health status
+    2. Sections for each component with their respective information
+    3. Recommendations based on any concerning metrics
+    
+    Use markdown formatting to make the report readable and professional.
+    Highlight any concerning values and provide practical recommendations.
+    """,
+    description="Synthesizes all system information into a comprehensive report",
+)
diff --git a/ADK-Crash-Course/12-loop-agent/README.md b/ADK-Crash-Course/12-loop-agent/README.md
new file mode 100644
index 000000000..d6722907d
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/README.md
@@ -0,0 +1,95 @@
+# LinkedIn Post Generator Loop Agent
+
+This example demonstrates the use of a Sequential and Loop Agent pattern in the Agent Development Kit (ADK) to generate and refine a LinkedIn post.
+
+## Overview
+
+The LinkedIn Post Generator uses a sequential pipeline with a loop component to:
+1. Generate an initial LinkedIn post
+2. Iteratively refine the post until quality requirements are met
+
+This demonstrates several key patterns:
+1. **Sequential Pipeline**: A multi-step workflow with distinct stages
+2. **Iterative Refinement**: Using a loop to repeatedly refine content
+3. **Automatic Quality Checking**: Validating content against specific criteria
+4. **Feedback-Driven Refinement**: Improving content based on specific feedback
+5. **Loop Exit Tool**: Using a tool to terminate the loop when quality requirements are met
+
+## Architecture
+
+The system is composed of the following components:
+
+### Root Sequential Agent
+
+`LinkedInPostGenerationPipeline` - A SequentialAgent that orchestrates the overall process:
+1. First runs the initial post generator
+2. Then executes the refinement loop
+
+### Initial Post Generator
+
+`InitialPostGenerator` - An LlmAgent that creates the first draft of the LinkedIn post with no prior context.
+
+### Refinement Loop
+
+`PostRefinementLoop` - A LoopAgent that executes a two-stage refinement process:
+1. First runs the reviewer to evaluate the post and possibly exit the loop
+2. Then runs the refiner to improve the post if the loop continues
+
+### Sub-Agents Inside the Refinement Loop
+
+1. **Post Reviewer** (`PostReviewer`) - Reviews posts for quality and provides feedback or exits the loop if requirements are met
+2. **Post Refiner** (`PostRefiner`) - Refines the post based on feedback to improve quality
+
+### Tools
+
+1. **Character Counter** - Validates post length against requirements (used by the Reviewer)
+2. **Exit Loop** - Terminates the loop when all quality criteria are satisfied (used by the Reviewer)
+
+## Loop Control with Exit Tool
+
+A key design pattern in this example is the use of an `exit_loop` tool to control when the loop terminates. The Post Reviewer has two responsibilities:
+
+1. **Quality Evaluation**: Checks if the post meets all requirements
+2. **Loop Control**: Calls the exit_loop tool when the post passes all quality checks
+
+When the exit_loop tool is called:
+1. It sets `tool_context.actions.escalate = True`
+2. This signals to the LoopAgent that it should stop iterating
+
+This approach follows ADK best practices by:
+1. Separating initial generation from refinement
+2. Giving the quality reviewer direct control over loop termination
+3. Using a dedicated agent for post refinement
+4. Using a tool to manage the loop control flow
+
+## Usage
+
+To run this example:
+
+```bash
+cd 11-loop-agent
+adk web
+```
+
+Then in the web interface, enter a prompt like:
+"Generate a LinkedIn post about what I've learned from @aiwithbrandon's Agent Development Kit tutorial."
+
+The system will:
+1. Generate an initial LinkedIn post
+2. Review the post for quality and compliance with requirements
+3. If the post meets all requirements, exit the loop
+4. Otherwise, provide feedback and refine the post
+5. Continue this process until a satisfactory post is created or max iterations reached
+6. Return the final post
+
+## Example Input
+
+```
+Generate a LinkedIn post about what I've learned from @aiwithbrandon's Agent Development Kit tutorial.
+```
+
+## Loop Termination
+
+The loop terminates in one of two ways:
+1. When the post meets all quality requirements (reviewer calls the exit_loop tool)
+2. After reaching the maximum number of iterations (10)
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/__init__.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/__init__.py
new file mode 100644
index 000000000..0ab858786
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/__init__.py
@@ -0,0 +1,8 @@
+"""
+LinkedIn Post Generator Agent Package
+
+This package provides a LinkedIn post generator system with automated review and feedback.
+It uses a loop agent for iterative refinement until quality requirements are met.
+"""
+
+from .agent import root_agent
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/agent.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/agent.py
new file mode 100644
index 000000000..a95810747
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/agent.py
@@ -0,0 +1,33 @@
+"""
+LinkedIn Post Generator Root Agent
+
+This module defines the root agent for the LinkedIn post generation application.
+It uses a sequential agent with an initial post generator followed by a refinement loop.
+"""
+
+from google.adk.agents import LoopAgent, SequentialAgent
+
+from .subagents.post_generator import initial_post_generator
+from .subagents.post_refiner import post_refiner
+from .subagents.post_reviewer import post_reviewer
+
+# Create the Refinement Loop Agent
+refinement_loop = LoopAgent(
+    name="PostRefinementLoop",
+    max_iterations=10,
+    sub_agents=[
+        post_reviewer,
+        post_refiner,
+    ],
+    description="Iteratively reviews and refines a LinkedIn post until quality requirements are met",
+)
+
+# Create the Sequential Pipeline
+root_agent = SequentialAgent(
+    name="LinkedInPostGenerationPipeline",
+    sub_agents=[
+        initial_post_generator,  # Step 1: Generate initial post
+        refinement_loop,  # Step 2: Review and refine in a loop
+    ],
+    description="Generates and refines a LinkedIn post through an iterative review process",
+)
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/__init__.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/__init__.py
new file mode 100644
index 000000000..b443a9259
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/__init__.py
@@ -0,0 +1,9 @@
+"""
+LinkedIn Post Agent Subagents Package
+
+This package provides all subagents used in the LinkedIn post generation system.
+"""
+
+from .post_generator import initial_post_generator
+from .post_refiner import post_refiner
+from .post_reviewer import post_reviewer
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_generator/__init__.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_generator/__init__.py
new file mode 100644
index 000000000..3b97027b4
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_generator/__init__.py
@@ -0,0 +1,7 @@
+"""
+LinkedIn Post Generator Agent Package
+
+This package provides an agent for generating the initial LinkedIn post.
+"""
+
+from .agent import initial_post_generator
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_generator/agent.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_generator/agent.py
new file mode 100644
index 000000000..c9b2810a2
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_generator/agent.py
@@ -0,0 +1,53 @@
+"""
+LinkedIn Post Generator Agent
+
+This agent generates the initial LinkedIn post before refinement.
+"""
+
+from google.adk.agents.llm_agent import LlmAgent
+
+# Constants
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Define the Initial Post Generator Agent
+initial_post_generator = LlmAgent(
+    name="InitialPostGenerator",
+    model=GEMINI_MODEL,
+    instruction="""You are a LinkedIn Post Generator.
+
+    Your task is to create a LinkedIn post about an Agent Development Kit (ADK) tutorial by @aiwithbrandon.
+    
+    ## CONTENT REQUIREMENTS
+    Ensure the post includes:
+    1. Excitement about learning from the tutorial
+    2. Specific aspects of ADK learned:
+       - Basic agent implementation (basic-agent)
+       - Tool integration (tool-agent)
+       - Using LiteLLM (litellm-agent)
+       - Managing sessions and memory
+       - Persistent storage capabilities
+       - Multi-agent orchestration
+       - Stateful multi-agent systems
+       - Callback systems
+       - Sequential agents for pipeline workflows
+       - Parallel agents for concurrent operations
+       - Loop agents for iterative refinement
+    3. Brief statement about improving AI applications
+    4. Mention/tag of @aiwithbrandon
+    5. Clear call-to-action for connections
+    
+    ## STYLE REQUIREMENTS
+    - Professional and conversational tone
+    - Between 1000-1500 characters
+    - NO emojis
+    - NO hashtags
+    - Show genuine enthusiasm
+    - Highlight practical applications
+    
+    ## OUTPUT INSTRUCTIONS
+    - Return ONLY the post content
+    - Do not add formatting markers or explanations
+    """,
+    description="Generates the initial LinkedIn post to start the refinement process",
+    output_key="current_post",
+)
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_refiner/__init__.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_refiner/__init__.py
new file mode 100644
index 000000000..4b5e2a19d
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_refiner/__init__.py
@@ -0,0 +1,7 @@
+"""
+LinkedIn Post Refiner Agent Package
+
+This package provides an agent for refining LinkedIn posts based on feedback.
+"""
+
+from .agent import post_refiner
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_refiner/agent.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_refiner/agent.py
new file mode 100644
index 000000000..76be65626
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_refiner/agent.py
@@ -0,0 +1,50 @@
+"""
+LinkedIn Post Refiner Agent
+
+This agent refines LinkedIn posts based on review feedback.
+"""
+
+from google.adk.agents.llm_agent import LlmAgent
+
+# Constants
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Define the Post Refiner Agent
+post_refiner = LlmAgent(
+    name="PostRefinerAgent",
+    model=GEMINI_MODEL,
+    instruction="""You are a LinkedIn Post Refiner.
+
+    Your task is to refine a LinkedIn post based on review feedback.
+    
+    ## INPUTS
+    **Current Post:**
+    {current_post}
+    
+    **Review Feedback:**
+    {review_feedback}
+    
+    ## TASK
+    Carefully apply the feedback to improve the post.
+    - Maintain the original tone and theme of the post
+    - Ensure all content requirements are met:
+      1. Excitement about learning from the tutorial
+      2. Specific aspects of ADK learned (at least 4)
+      3. Brief statement about improving AI applications
+      4. Mention/tag of @aiwithbrandon
+      5. Clear call-to-action for connections
+    - Adhere to style requirements:
+      - Professional and conversational tone
+      - Between 1000-1500 characters
+      - NO emojis
+      - NO hashtags
+      - Show genuine enthusiasm
+      - Highlight practical applications
+    
+    ## OUTPUT INSTRUCTIONS
+    - Output ONLY the refined post content
+    - Do not add explanations or justifications
+    """,
+    description="Refines LinkedIn posts based on feedback to improve quality",
+    output_key="current_post",
+)
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/__init__.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/__init__.py
new file mode 100644
index 000000000..29a569fbf
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/__init__.py
@@ -0,0 +1,7 @@
+"""
+LinkedIn Post Reviewer Agent Package
+
+This package provides an agent for reviewing and validating LinkedIn posts.
+"""
+
+from .agent import post_reviewer
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/agent.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/agent.py
new file mode 100644
index 000000000..9f291ead9
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/agent.py
@@ -0,0 +1,60 @@
+"""
+LinkedIn Post Reviewer Agent
+
+This agent reviews LinkedIn posts for quality and provides feedback.
+"""
+
+from google.adk.agents.llm_agent import LlmAgent
+
+from .tools import count_characters, exit_loop
+
+# Constants
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Define the Post Reviewer Agent
+post_reviewer = LlmAgent(
+    name="PostReviewer",
+    model=GEMINI_MODEL,
+    instruction="""You are a LinkedIn Post Quality Reviewer.
+
+    Your task is to evaluate the quality of a LinkedIn post about Agent Development Kit (ADK).
+    
+    ## EVALUATION PROCESS
+    1. Use the count_characters tool to check the post's length.
+       Pass the post text directly to the tool.
+    
+    2. If the length check fails (tool result is "fail"), provide specific feedback on what needs to be fixed.
+       Use the tool's message as a guideline, but add your own professional critique.
+    
+    3. If length check passes, evaluate the post against these criteria:
+       - REQUIRED ELEMENTS:
+         1. Mentions @aiwithbrandon
+         2. Lists multiple ADK capabilities (at least 4)
+         3. Has a clear call-to-action
+         4. Includes practical applications
+         5. Shows genuine enthusiasm
+       
+       - STYLE REQUIREMENTS:
+         1. NO emojis
+         2. NO hashtags
+         3. Professional tone
+         4. Conversational style
+         5. Clear and concise writing
+    
+    ## OUTPUT INSTRUCTIONS
+    IF the post fails ANY of the checks above:
+      - Return concise, specific feedback on what to improve
+      
+    ELSE IF the post meets ALL requirements:
+      - Call the exit_loop function
+      - Return "Post meets all requirements. Exiting the refinement loop."
+      
+    Do not embellish your response. Either provide feedback on what to improve OR call exit_loop and return the completion message.
+    
+    ## POST TO REVIEW
+    {current_post}
+    """,
+    description="Reviews post quality and provides feedback on what to improve or exits the loop if requirements are met",
+    tools=[count_characters, exit_loop],
+    output_key="review_feedback",
+)
diff --git a/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/tools.py b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/tools.py
new file mode 100644
index 000000000..0faaefab5
--- /dev/null
+++ b/ADK-Crash-Course/12-loop-agent/linkedin_post_agent/subagents/post_reviewer/tools.py
@@ -0,0 +1,79 @@
+"""
+Tools for LinkedIn Post Reviewer Agent
+
+This module provides tools for analyzing and validating LinkedIn posts.
+"""
+
+from typing import Any, Dict
+
+from google.adk.tools.tool_context import ToolContext
+
+
+def count_characters(text: str, tool_context: ToolContext) -> Dict[str, Any]:
+    """
+    Tool to count characters in the provided text and provide length-based feedback.
+    Updates review_status in the state based on length requirements.
+
+    Args:
+        text: The text to analyze for character count
+        tool_context: Context for accessing and updating session state
+
+    Returns:
+        Dict[str, Any]: Dictionary containing:
+            - result: 'fail' or 'pass'
+            - char_count: number of characters in text
+            - message: feedback message about the length
+    """
+    char_count = len(text)
+    MIN_LENGTH = 1000
+    MAX_LENGTH = 1500
+
+    print("\n----------- TOOL DEBUG -----------")
+    print(f"Checking text length: {char_count} characters")
+    print("----------------------------------\n")
+
+    if char_count < MIN_LENGTH:
+        chars_needed = MIN_LENGTH - char_count
+        tool_context.state["review_status"] = "fail"
+        return {
+            "result": "fail",
+            "char_count": char_count,
+            "chars_needed": chars_needed,
+            "message": f"Post is too short. Add {chars_needed} more characters to reach minimum length of {MIN_LENGTH}.",
+        }
+    elif char_count > MAX_LENGTH:
+        chars_to_remove = char_count - MAX_LENGTH
+        tool_context.state["review_status"] = "fail"
+        return {
+            "result": "fail",
+            "char_count": char_count,
+            "chars_to_remove": chars_to_remove,
+            "message": f"Post is too long. Remove {chars_to_remove} characters to meet maximum length of {MAX_LENGTH}.",
+        }
+    else:
+        tool_context.state["review_status"] = "pass"
+        return {
+            "result": "pass",
+            "char_count": char_count,
+            "message": f"Post length is good ({char_count} characters).",
+        }
+
+
+def exit_loop(tool_context: ToolContext) -> Dict[str, Any]:
+    """
+    Call this function ONLY when the post meets all quality requirements,
+    signaling the iterative process should end.
+
+    Args:
+        tool_context: Context for tool execution
+
+    Returns:
+        Empty dictionary
+    """
+    print("\n----------- EXIT LOOP TRIGGERED -----------")
+    print("Post review completed successfully")
+    print("Loop will exit now")
+    print("------------------------------------------\n")
+
+    tool_context.actions.escalate = True
+    return {}
diff --git a/ADK-Crash-Course/2-tool-agent/README.md b/ADK-Crash-Course/2-tool-agent/README.md
new file mode 100644
index 000000000..f3caeb73c
--- /dev/null
+++ b/ADK-Crash-Course/2-tool-agent/README.md
@@ -0,0 +1,141 @@
+# Tool Agent Example
+
+## What is a Tool Agent?
+
+A Tool Agent extends the basic ADK agent by incorporating tools that allow the agent to perform actions beyond just generating text responses. Tools enable agents to interact with external systems, retrieve information, and perform specific functions to accomplish tasks more effectively.
+
+In this example, we demonstrate how to build an agent that can use built-in tools (like Google Search) and custom function tools to enhance its capabilities.
+
+## Key Components
+
+### 1. Built-in Tools
+ADK provides several built-in tools that you can use with your agents:
+
+- **Google Search**: Allows your agent to search the web for information
+- **Code Execution**: Enables your agent to run code snippets
+- **Vertex AI Search**: Lets your agent search through your own data
+
+**Important Note**: Currently, for each root agent or single agent, only one built-in tool is supported. See the [ADK documentation](https://google.github.io/adk-docs/tools/built-in-tools/#use-built-in-tools-with-other-tools) for more details.
+
+### 2. Custom Function Tools
+You can create your own tools by defining Python functions. These custom tools extend your agent's capabilities to perform specific tasks.
+
+#### Best Practices for Custom Function Tools:
+
+- **Parameters**: Define your function parameters using standard JSON-serializable types (string, integer, list, dictionary)
+- **No Default Values**: Default values are not currently supported in ADK
+- **Return Type**: The preferred return type is a dictionary
+  - If you don't return a dictionary, ADK will wrap it into a dictionary `{"result": ...}`
+  - Best practice format: `{"status": "success", "error_message": None, "result": "..."}`
+- **Docstrings**: The function's docstring serves as the tool's description and is sent to the LLM
+  - Focus on clarity so the LLM understands how to use the tool effectively
+
+## Limitations
+
+When working with built-in tools in ADK, there are several important limitations to be aware of:
+
+### Single Built-in Tool Restriction
+
+**Currently, for each root agent or single agent, only one built-in tool is supported.**
+
+For example, this approach using two built-in tools within a single agent is **not** currently supported:
+
+```python
+root_agent = Agent(
+    name="RootAgent",
+    model="gemini-2.0-flash",
+    description="Root Agent",
+    tools=[built_in_code_execution, google_search],  # NOT SUPPORTED
+)
+```
+
+### Built-in Tools vs. Custom Tools
+
+**You cannot mix built-in tools with custom function tools in the same agent.**
+
+For example, this approach is **not** currently supported:
+
+```python
+def get_current_time() -> dict:
+    """Get the current time in the format YYYY-MM-DD HH:MM:SS"""
+    return {
+        "current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    }
+
+root_agent = Agent(
+    name="RootAgent",
+    model="gemini-2.0-flash",
+    description="Root Agent",
+    tools=[google_search, get_current_time],  # NOT SUPPORTED
+)
+```
+
+To use both types of tools, you would need to use the Agent Tool approach described in the Multi-Agent example.
+
+## Implementation Example
+
+### Understanding the Code
+
+The agent.py file defines a tool agent that can use Google Search to find information on the web. The agent is configured with:
+
+1. A name and description
+2. The Gemini model to use
+3. Instructions that tell the agent how to behave and what tools it can use
+4. The tools it can access (in this case, google_search)
+
+The file also includes a commented-out example of a custom function tool `get_current_time()` that could be uncommented to explore custom tool functionality.
+
+### Getting Started
+
+This example uses the same virtual environment created in the root directory. Make sure you have:
+
+1. Activated the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Set up your API key:
+   - Rename `.env.example` to `.env` in the tool_agent folder
+   - Add your Google API key to the `GOOGLE_API_KEY` variable in the `.env` file
+
+### Running the Example
+
+To run the tool agent example:
+
+1. Navigate to the 2-tool-agent directory containing your agent folder.
+
+2. Start the interactive web UI:
+```bash
+adk web
+```
+
+3. Access the web UI by opening the URL shown in your terminal (typically http://localhost:8000)
+
+4. Select the "tool_agent" from the dropdown menu in the top-left corner of the UI
+
+5. Start chatting with your agent in the textbox at the bottom of the screen
+
+The ADK CLI tool provides several options:
+
+- **`adk web`**: Launches an interactive web UI for testing your agent with a chat interface
+- **`adk run tool_agent`**: Runs your agent directly in the terminal
+- **`adk api_server`**: Starts a FastAPI server to test API requests to your agent
+
+### Example Prompts to Try
+
+- "Search for recent news about artificial intelligence"
+- "Find information about Google's Agent Development Kit"
+- "What are the latest advancements in quantum computing?"
+
+You can exit the conversation or stop the server by pressing `Ctrl+C` in your terminal.
+
+## Additional Resources
+
+- [Types of tools](https://google.github.io/adk-docs/tools/#full-example-tavily-search)
+- [ADK Function Tools Documentation](https://google.github.io/adk-docs/tools/function-tools/)
+- [ADK Built-in Tools Documentation](https://google.github.io/adk-docs/tools/built-in-tools/)
diff --git a/ADK-Crash-Course/2-tool-agent/tool_agent/__init__.py b/ADK-Crash-Course/2-tool-agent/tool_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/2-tool-agent/tool_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/2-tool-agent/tool_agent/agent.py b/ADK-Crash-Course/2-tool-agent/tool_agent/agent.py
new file mode 100644
index 000000000..8530fb879
--- /dev/null
+++ b/ADK-Crash-Course/2-tool-agent/tool_agent/agent.py
@@ -0,0 +1,23 @@
+from google.adk.agents import Agent
+from google.adk.tools import google_search
+
+# def get_current_time() -> dict:
+#     """
+#     Get the current time in the format YYYY-MM-DD HH:MM:SS
+#     """
+#     return {
+#         "current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+#     }
+
+root_agent = Agent(
+    name="tool_agent",
+    model= "gemini-2.0-flash", # "gemini-2.0-flash-exp",  # "gemini-2.0-flash-live-preview-04-09" does not work with google search tool
+    description="Tool agent",
+    instruction="""
+    You are a helpful assistant that can use the following tools:
+    - google_search
+    """,
+    tools=[google_search],
+    # tools=[get_current_time],
+    # tools=[google_search, get_current_time], # <--- Doesn't work
+)
diff --git a/ADK-Crash-Course/3-litellm-agent/README.md b/ADK-Crash-Course/3-litellm-agent/README.md
new file mode 100644
index 000000000..2c56d0bfb
--- /dev/null
+++ b/ADK-Crash-Course/3-litellm-agent/README.md
@@ -0,0 +1,128 @@
+# LiteLLM Agent Example
+
+## What is LiteLLM?
+
+LiteLLM is a Python library that provides a unified interface for interacting with multiple Large Language Model (LLM) providers through a single, consistent API. It serves as an adapter that allows you to:
+
+- Use the same code to access 100+ different LLMs from providers like OpenAI, Anthropic, Google, AWS Bedrock, and more
+- Standardize inputs and outputs across different LLM providers
+- Track costs, manage API keys, and handle errors consistently
+- Implement fallbacks and load balancing across different models
+
+In essence, LiteLLM acts as a unified wrapper that makes it easy to switch between different LLM providers without changing your application code.
+
+## Why Use LiteLLM with ADK?
+
+The Agent Development Kit (ADK) is designed to be model-agnostic, meaning it can work with various LLM providers. LiteLLM enhances this capability by:
+
+1. **Provider Flexibility**: Easily switch between LLM providers (OpenAI, Anthropic, etc.) without changing your agent code
+2. **Cost Optimization**: Choose the most cost-effective model for your specific use case
+3. **Model Exploration**: Experiment with different models to find the best performance for your task
+4. **Future-Proofing**: As new models are released, you can quickly adopt them without major code changes
+
+This example demonstrates how to use LiteLLM with ADK to create an agent powered by models through OpenRouter rather than Google's Gemini models.
+
+## Limitations When Using Non-Google Models
+
+When using LiteLLM to integrate non-Google models with ADK, there are some important limitations to be aware of:
+
+1. **No Access to Google Built-in Tools**: Non-Google models (like OpenAI, Anthropic, etc.) cannot use ADK's built-in Google tools such as:
+   - Google Search
+   - Code Execution
+   - Vertex AI Search
+
+2. **Custom Function Tools Only**: When using non-Google models, you can only use custom function tools (like the `get_dad_joke()` function in this example).
+
+
+These limitations exist because built-in tools are specifically designed to work with Google's models and infrastructure. However, you can still create powerful agents using custom function tools and the wide variety of models available through LiteLLM.
+
+## Getting Started
+
+This example uses the same virtual environment created in the root directory. Make sure you have:
+
+1. Activated the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Set up your OpenRouter API key:
+   - Create an account at [OpenRouter](https://openrouter.ai/) if you don't have one
+   - Generate an API key at https://openrouter.ai/keys
+   - Rename `.env.example` to `.env` in the openrouter_dad_joke_agent folder
+   - Add your OpenRouter API key to the `OPENROUTER_API_KEY` variable in the `.env` file
+
+## Understanding the Code
+
+This example demonstrates:
+
+1. How to use the `LiteLlm` model adapter with ADK
+2. How to connect to models through OpenRouter (specifically Claude 3.5 Sonnet)
+3. How to create a simple agent with a custom tool
+
+The agent is configured to tell dad jokes using a custom function tool `get_dad_joke()` and powered by Anthropic's Claude 3.5 Sonnet model through OpenRouter instead of Google's Gemini.
+
+## Running the Example
+
+To run the LiteLLM agent example:
+
+1. Navigate to the 3-litellm-agent directory containing your agent folder.
+
+2. Start the interactive web UI:
+```bash
+adk web
+```
+
+3. Access the web UI by opening the URL shown in your terminal (typically http://localhost:8000)
+
+4. Select the "openrouter_dad_joke_agent" from the dropdown menu in the top-left corner of the UI
+
+5. Start chatting with your agent in the textbox at the bottom of the screen
+
+### Example Prompts to Try
+
+- "Tell me a dad joke"
+
+You can exit the conversation or stop the server by pressing `Ctrl+C` in your terminal.
+
+## Modifying the Example
+
+You can easily modify this example to use different models from different providers through OpenRouter by changing the `LiteLlm` configuration. For example:
+
+```python
+# To use Claude 3.5 Sonnet from Anthropic through OpenRouter
+model = LiteLlm(
+    model="openrouter/anthropic/claude-3-5-sonnet",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+# To use GPT-4o from OpenAI through OpenRouter
+model = LiteLlm(
+    model="openrouter/openai/gpt-4o",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+# To use Llama 3 70B from Meta through OpenRouter
+model = LiteLlm(
+    model="openrouter/meta-llama/meta-llama-3-70b-instruct",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+# To use Mistral Large through OpenRouter
+model = LiteLlm(
+    model="openrouter/mistral/mistral-large-latest",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+```
+
+## Additional Resources
+
+- [Google ADK LiteLLM Integration Documentation](https://google.github.io/adk-docs/tutorials/agent-team/#step-2-going-multi-model-with-litellm-optional)
+- [LiteLLM Documentation](https://docs.litellm.ai/docs/)
+- [LiteLLM Supported Providers](https://docs.litellm.ai/docs/providers)
+- [OpenRouter Documentation](https://openrouter.ai/docs)
+- [Anthropic Claude Models Overview](https://docs.anthropic.com/en/docs/about-claude/models/all-models)
diff --git a/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/.env.example b/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/.env.example
new file mode 100644
index 000000000..ba47868dc
--- /dev/null
+++ b/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/.env.example
@@ -0,0 +1 @@
+OPENROUTER_API_KEY=...
diff --git a/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/__init__.py b/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/agent.py b/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/agent.py
new file mode 100644
index 000000000..b73b829ee
--- /dev/null
+++ b/ADK-Crash-Course/3-litellm-agent/dad_joke_agent/agent.py
@@ -0,0 +1,33 @@
+import os
+import random
+
+from google.adk.agents import Agent
+from google.adk.models.lite_llm import LiteLlm
+
+# https://docs.litellm.ai/docs/providers/openrouter
+model = LiteLlm(
+    model="openrouter/openai/gpt-4.1",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+
+def get_dad_joke():
+    jokes = [
+        "Why did the chicken cross the road? To get to the other side!",
+        "What do you call a belt made of watches? A waist of time.",
+        "What do you call fake spaghetti? An impasta!",
+        "Why did the scarecrow win an award? Because he was outstanding in his field!",
+    ]
+    return random.choice(jokes)
+
+
+root_agent = Agent(
+    name="dad_joke_agent",
+    model=model,
+    description="Dad joke agent",
+    instruction="""
+    You are a helpful assistant that can tell dad jokes. 
+    Only use the tool `get_dad_joke` to tell jokes.
+    """,
+    tools=[get_dad_joke],
+)
diff --git a/ADK-Crash-Course/4-structured-outputs/README.md b/ADK-Crash-Course/4-structured-outputs/README.md
new file mode 100644
index 000000000..217345e02
--- /dev/null
+++ b/ADK-Crash-Course/4-structured-outputs/README.md
@@ -0,0 +1,124 @@
+# Structured Outputs in ADK
+
+This example demonstrates how to implement structured outputs in the Agent Development Kit (ADK) using Pydantic models. The main agent in this example, `email_generator`, uses the `output_schema` parameter to ensure its responses conform to a specific structured format.
+
+## What are Structured Outputs?
+
+ADK allows you to define structured data formats for agent inputs and outputs using Pydantic models:
+
+1. **Controlled Output Format**: Using `output_schema` ensures the LLM produces responses in a consistent JSON structure
+2. **Data Validation**: Pydantic validates that all required fields are present and correctly formatted
+3. **Improved Downstream Processing**: Structured outputs are easier to handle in downstream applications or by other agents
+
+Use structured outputs when you need guaranteed format consistency for integration with other systems or agents.
+
+## Email Generator Example
+
+In this example, we've created an email generator agent that produces structured output with:
+
+1. **Email Subject**: A concise, relevant subject line
+2. **Email Body**: Well-formatted email content with greeting, paragraphs, and signature
+
+The agent uses a Pydantic model called `EmailContent` to define this structure, ensuring every response follows the same format.
+
+### Output Schema Definition
+
+The Pydantic model defines exactly what fields are required and includes descriptions for each:
+
+```python
+class EmailContent(BaseModel):
+    """Schema for email content with subject and body."""
+    
+    subject: str = Field(
+        description="The subject line of the email. Should be concise and descriptive."
+    )
+    body: str = Field(
+        description="The main content of the email. Should be well-formatted with proper greeting, paragraphs, and signature."
+    )
+```
+
+### How It Works
+
+1. The user provides a description of the email they need
+2. The LLM agent processes this request and generates both a subject and body
+3. The agent formats its response as a JSON object matching the `EmailContent` schema
+4. ADK validates the response against the schema before returning it
+5. The structured output is stored in the session state under the specified `output_key`
+
+## Important Limitations
+
+When using `output_schema`:
+
+1. **No Tool Usage**: Agents with an output schema cannot use tools during their execution
+2. **Direct JSON Response**: The LLM must produce a JSON response matching the schema as its final output
+3. **Clear Instructions**: The agent's instructions must explicitly guide the LLM to produce properly formatted JSON
+
+## Project Structure
+
+```
+4-structured-outputs/
+│
+├── email_agent/                   # Email Generator Agent package
+│   └── agent.py                   # Agent definition with output schema
+│
+└── README.md                      # This documentation
+```
+
+## Getting Started
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Create a `.env` file and add your Google API key:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Example
+
+```bash
+cd 4-structured-outputs
+adk web
+```
+
+Then select "email_generator" from the dropdown menu in the web UI.
+
+## Example Interactions
+
+Try these example prompts:
+
+```
+Write a professional email to my team about the upcoming project deadline that has been extended by two weeks.
+```
+
+```
+Draft an email to a client explaining that we need additional information before we can proceed with their order.
+```
+
+```
+Create an email to schedule a meeting with the marketing department to discuss the new product launch strategy.
+```
+
+## Key Concepts: Structured Data Exchange
+
+Structured outputs are part of ADK's broader support for structured data exchange, which includes:
+
+1. **input_schema**: Define expected input format (not used in this example)
+2. **output_schema**: Define required output format (used in this example)
+3. **output_key**: Store the result in session state for use by other agents (used in this example)
+
+This pattern enables reliable data passing between agents and integration with external systems that expect consistent data formats.
+
+## Additional Resources
+
+- [ADK Structured Data Documentation](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key)
+- [Pydantic Documentation](https://docs.pydantic.dev/latest/) 
diff --git a/ADK-Crash-Course/4-structured-outputs/email_agent/.env.example b/ADK-Crash-Course/4-structured-outputs/email_agent/.env.example
new file mode 100644
index 000000000..2a345870f
--- /dev/null
+++ b/ADK-Crash-Course/4-structured-outputs/email_agent/.env.example
@@ -0,0 +1,2 @@
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=your_api_key_here
diff --git a/ADK-Crash-Course/4-structured-outputs/email_agent/__init__.py b/ADK-Crash-Course/4-structured-outputs/email_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/4-structured-outputs/email_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/4-structured-outputs/email_agent/agent.py b/ADK-Crash-Course/4-structured-outputs/email_agent/agent.py
new file mode 100644
index 000000000..d01d0fb66
--- /dev/null
+++ b/ADK-Crash-Course/4-structured-outputs/email_agent/agent.py
@@ -0,0 +1,45 @@
+from google.adk.agents import LlmAgent
+from pydantic import BaseModel, Field
+
+
+# --- Define Output Schema ---
+class EmailContent(BaseModel):
+    subject: str = Field(
+        description="The subject line of the email. Should be concise and descriptive."
+    )
+    body: str = Field(
+        description="The main content of the email. Should be well-formatted with proper greeting, paragraphs, and signature."
+    )
+
+
+# --- Create Email Generator Agent ---
+root_agent = LlmAgent(
+    name="email_agent",
+    model="gemini-2.0-flash",
+    instruction="""
+        You are an Email Generation Assistant.
+        Your task is to generate a professional email based on the user's request.
+
+        GUIDELINES:
+        - Create an appropriate subject line (concise and relevant)
+        - Write a well-structured email body with:
+            * Professional greeting
+            * Clear and concise main content
+            * Appropriate closing
+            * Your name as signature
+        - Suggest relevant attachments if applicable (empty list if none needed)
+        - Email tone should match the purpose (formal for business, friendly for colleagues)
+        - Keep emails concise but complete
+
+        IMPORTANT: Your response MUST be valid JSON matching this structure:
+        {
+            "subject": "Subject line here",
+            "body": "Email body here with proper paragraphs and formatting",
+        }
+
+        DO NOT include any explanations or additional text outside the JSON response.
+    """,
+    description="Generates professional emails with structured subject and body",
+    output_schema=EmailContent,
+    output_key="email",
+)
diff --git a/ADK-Crash-Course/5-sessions-and-state/README.md b/ADK-Crash-Course/5-sessions-and-state/README.md
new file mode 100644
index 000000000..6e7334570
--- /dev/null
+++ b/ADK-Crash-Course/5-sessions-and-state/README.md
@@ -0,0 +1,140 @@
+# Sessions and State Management in ADK
+
+This example demonstrates how to create and manage stateful sessions in the Agent Development Kit (ADK), enabling your agents to maintain context and remember user information across interactions.
+
+## What Are Sessions in ADK?
+
+Sessions in ADK provide a way to:
+
+1. **Maintain State**: Store and access user data, preferences, and other information between interactions
+2. **Track Conversation History**: Automatically record and retrieve message history
+3. **Personalize Responses**: Use stored information to create more contextual and personalized agent experiences
+
+Unlike simple conversational agents that forget previous interactions, stateful agents can build relationships with users over time by remembering important details and preferences.
+
+## Example Overview
+
+This directory contains a basic stateful session example that demonstrates:
+
+- Creating a session with user preferences
+- Using template variables to access session state in agent instructions
+- Running the agent with a session to maintain context
+
+The example uses a simple question-answering agent that responds based on stored user information in the session state.
+
+## Project Structure
+
+```
+5-sessions-and-state/
+│
+├── basic_stateful_session.py      # Main example script
+│
+└── question_answering_agent/      # Agent implementation
+    ├── __init__.py
+    └── agent.py                   # Agent definition with template variables
+```
+
+## Getting Started
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Create a `.env` file and add your Google API key:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Example
+
+Run the example to see a stateful session in action:
+
+```bash
+python basic_stateful_session.py
+```
+
+This will:
+1. Create a new session with user information
+2. Initialize the agent with access to that session
+3. Process a user query about the stored preferences
+4. Display the agent's response based on the session data
+
+## Key Components
+
+### Session Service
+
+The example uses the `InMemorySessionService` which stores sessions in memory:
+
+```python
+session_service = InMemorySessionService()
+```
+
+### Initial State
+
+Sessions are created with an initial state containing user information:
+
+```python
+initial_state = {
+    "user_name": "Brandon Hancock",
+    "user_preferences": """
+        I like to play Pickleball, Disc Golf, and Tennis.
+        My favorite food is Mexican.
+        My favorite TV show is Game of Thrones.
+        Loves it when people like and subscribe to his YouTube channel.
+    """,
+}
+```
+
+### Creating a Session
+
+The example creates a session with a unique identifier:
+
+```python
+stateful_session = session_service.create_session(
+    app_name=APP_NAME,
+    user_id=USER_ID,
+    session_id=SESSION_ID,
+    state=initial_state,
+)
+```
+
+### Accessing State in Agent Instructions
+
+The agent accesses session state using template variables in its instructions:
+
+```python
+instruction="""
+You are a helpful assistant that answers questions about the user's preferences.
+
+Here is some information about the user:
+Name: 
+{user_name}
+Preferences: 
+{user_preferences}
+"""
+```
+
+### Running with Sessions
+
+Sessions are integrated with the `Runner` to maintain state between interactions:
+
+```python
+runner = Runner(
+    agent=question_answering_agent,
+    app_name=APP_NAME,
+    session_service=session_service,
+)
+```
+
+## Additional Resources
+
+- [Google ADK Sessions Documentation](https://google.github.io/adk-docs/sessions/session/)
+- [State Management in ADK](https://google.github.io/adk-docs/sessions/state/)
diff --git a/ADK-Crash-Course/5-sessions-and-state/basic_stateful_session.py b/ADK-Crash-Course/5-sessions-and-state/basic_stateful_session.py
new file mode 100644
index 000000000..81d0a0382
--- /dev/null
+++ b/ADK-Crash-Course/5-sessions-and-state/basic_stateful_session.py
@@ -0,0 +1,65 @@
+import uuid
+
+from dotenv import load_dotenv
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.genai import types
+from question_answering_agent import question_answering_agent
+
+load_dotenv()
+
+
+# Create a new session service to store state
+session_service_stateful = InMemorySessionService()
+
+initial_state = {
+    "user_name": "Brandon Hancock",
+    "user_preferences": """
+        I like to play Pickleball, Disc Golf, and Tennis.
+        My favorite food is Mexican.
+        My favorite TV show is Game of Thrones.
+        Loves it when people like and subscribe to his YouTube channel.
+    """,
+}
+
+# Create a NEW session
+APP_NAME = "Brandon Bot"
+USER_ID = "brandon_hancock"
+SESSION_ID = str(uuid.uuid4())
+stateful_session = session_service_stateful.create_session(
+    app_name=APP_NAME,
+    user_id=USER_ID,
+    session_id=SESSION_ID,
+    state=initial_state,
+)
+print("CREATED NEW SESSION:")
+print(f"\tSession ID: {SESSION_ID}")
+
+runner = Runner(
+    agent=question_answering_agent,
+    app_name=APP_NAME,
+    session_service=session_service_stateful,
+)
+
+new_message = types.Content(
+    role="user", parts=[types.Part(text="What is Brandon's favorite TV show?")]
+)
+
+for event in runner.run(
+    user_id=USER_ID,
+    session_id=SESSION_ID,
+    new_message=new_message,
+):
+    if event.is_final_response():
+        if event.content and event.content.parts:
+            print(f"Final Response: {event.content.parts[0].text}")
+
+print("==== Session Event Exploration ====")
+session = session_service_stateful.get_session(
+    app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
+)
+
+# Log final Session state
+print("=== Final Session State ===")
+for key, value in session.state.items():
+    print(f"{key}: {value}")
diff --git a/ADK-Crash-Course/5-sessions-and-state/question_answering_agent/__init__.py b/ADK-Crash-Course/5-sessions-and-state/question_answering_agent/__init__.py
new file mode 100644
index 000000000..70ebb1a00
--- /dev/null
+++ b/ADK-Crash-Course/5-sessions-and-state/question_answering_agent/__init__.py
@@ -0,0 +1 @@
+from .agent import question_answering_agent
diff --git a/ADK-Crash-Course/5-sessions-and-state/question_answering_agent/agent.py b/ADK-Crash-Course/5-sessions-and-state/question_answering_agent/agent.py
new file mode 100644
index 000000000..39bf20e0c
--- /dev/null
+++ b/ADK-Crash-Course/5-sessions-and-state/question_answering_agent/agent.py
@@ -0,0 +1,17 @@
+from google.adk.agents import Agent
+
+# Create the root agent
+question_answering_agent = Agent(
+    name="question_answering_agent",
+    model="gemini-2.0-flash",
+    description="Question answering agent",
+    instruction="""
+    You are a helpful assistant that answers questions about the user's preferences.
+
+    Here is some information about the user:
+    Name: 
+    {user_name}
+    Preferences: 
+    {user_preferences}
+    """,
+)
diff --git a/ADK-Crash-Course/6-persistent-storage/README.md b/ADK-Crash-Course/6-persistent-storage/README.md
new file mode 100644
index 000000000..f38d82311
--- /dev/null
+++ b/ADK-Crash-Course/6-persistent-storage/README.md
@@ -0,0 +1,180 @@
+# Persistent Storage in ADK
+
+This example demonstrates how to implement persistent storage for your ADK agents, allowing them to remember information and maintain conversation history across multiple sessions, application restarts, and even server deployments.
+
+## What is Persistent Storage in ADK?
+
+In previous examples, we used `InMemorySessionService` which stores session data only in memory - this data is lost when the application stops. For real-world applications, you'll often need your agents to remember user information and conversation history long-term. This is where persistent storage comes in.
+
+ADK provides the `DatabaseSessionService` that allows you to store session data in a SQL database, ensuring:
+
+1. **Long-term Memory**: Information persists across application restarts
+2. **Consistent User Experiences**: Users can continue conversations where they left off
+3. **Multi-user Support**: Different users' data remains separate and secure
+4. **Scalability**: Works with production databases for high-scale deployments
+
+This example shows how to implement a reminder agent that remembers your name and todos across different conversations using an SQLite database.
+
+## Project Structure
+
+```
+5-persistent-storage/
+│
+├── memory_agent/               # Agent package
+│   ├── __init__.py             # Required for ADK to discover the agent
+│   └── agent.py                # Agent definition with reminder tools
+│
+├── main.py                     # Application entry point with database session setup
+├── utils.py                    # Utility functions for terminal UI and agent interaction
+├── .env                        # Environment variables
+├── my_agent_data.db            # SQLite database file (created when first run)
+└── README.md                   # This documentation
+```
+
+## Key Components
+
+### 1. DatabaseSessionService
+
+The core component that provides persistence is the `DatabaseSessionService`, which is initialized with a database URL:
+
+```python
+from google.adk.sessions import DatabaseSessionService
+
+db_url = "sqlite:///./my_agent_data.db"
+session_service = DatabaseSessionService(db_url=db_url)
+```
+
+This service allows ADK to:
+- Store session data in a SQLite database file
+- Retrieve previous sessions for a user
+- Automatically manage database schemas
+
+### 2. Session Management
+
+The example demonstrates proper session management:
+
+```python
+# Check for existing sessions for this user
+existing_sessions = session_service.list_sessions(
+    app_name=APP_NAME,
+    user_id=USER_ID,
+)
+
+# If there's an existing session, use it, otherwise create a new one
+if existing_sessions and len(existing_sessions.sessions) > 0:
+    # Use the most recent session
+    SESSION_ID = existing_sessions.sessions[0].id
+    print(f"Continuing existing session: {SESSION_ID}")
+else:
+    # Create a new session with initial state
+    session_service.create_session(
+        app_name=APP_NAME,
+        user_id=USER_ID,
+        session_id=SESSION_ID,
+        state=initialize_state(),
+    )
+```
+
+### 3. State Management with Tools
+
+The agent includes tools that update the persistent state:
+
+```python
+def add_reminder(reminder: str, tool_context: ToolContext) -> dict:
+    # Get current reminders from state
+    reminders = tool_context.state.get("reminders", [])
+    
+    # Add the new reminder
+    reminders.append(reminder)
+    
+    # Update state with the new list of reminders
+    tool_context.state["reminders"] = reminders
+    
+    return {
+        "action": "add_reminder",
+        "reminder": reminder,
+        "message": f"Added reminder: {reminder}",
+    }
+```
+
+Each change to `tool_context.state` is automatically saved to the database.
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.9+
+- Google API Key for Gemini models
+- SQLite (included with Python)
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Make sure your Google API key is set in the `.env` file:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Example
+
+To run the persistent storage example:
+
+```bash
+python main.py
+```
+
+This will:
+1. Connect to the SQLite database (or create it if it doesn't exist)
+2. Check for previous sessions for the user
+3. Start a conversation with the memory agent
+4. Save all interactions to the database
+
+### Example Interactions
+
+Try these interactions to test the agent's persistent memory:
+
+1. **First run:**
+   - "What's my name?"
+   - "My name is John"
+   - "Add a reminder to buy groceries"
+   - "Add another reminder to finish the report"
+   - "What are my reminders?"
+   - Exit the program with "exit"
+
+2. **Second run:**
+   - "What's my name?"
+   - "What reminders do I have?"
+   - "Update my second reminder to submit the report by Friday"
+   - "Delete the first reminder"
+   
+The agent will remember your name and reminders between runs!
+
+## Using Database Storage in Production
+
+While this example uses SQLite for simplicity, `DatabaseSessionService` supports various database backends through SQLAlchemy:
+
+- PostgreSQL: `postgresql://user:password@localhost/dbname`
+- MySQL: `mysql://user:password@localhost/dbname`
+- MS SQL Server: `mssql://user:password@localhost/dbname`
+
+For production use:
+1. Choose a database system that meets your scalability needs
+2. Configure connection pooling for efficiency
+3. Implement proper security for database credentials
+4. Consider database backups for critical agent data
+
+## Additional Resources
+
+- [ADK Sessions Documentation](https://google.github.io/adk-docs/sessions/session/)
+- [Session Service Implementations](https://google.github.io/adk-docs/sessions/session/#sessionservice-implementations)
+- [State Management in ADK](https://google.github.io/adk-docs/sessions/state/)
+- [SQLAlchemy Documentation](https://docs.sqlalchemy.org/) for advanced database configuration 
diff --git a/ADK-Crash-Course/6-persistent-storage/main.py b/ADK-Crash-Course/6-persistent-storage/main.py
new file mode 100644
index 000000000..2a72c946c
--- /dev/null
+++ b/ADK-Crash-Course/6-persistent-storage/main.py
@@ -0,0 +1,79 @@
+import asyncio
+
+from dotenv import load_dotenv
+from google.adk.runners import Runner
+from google.adk.sessions import DatabaseSessionService
+from memory_agent.agent import memory_agent
+from utils import call_agent_async
+
+load_dotenv()
+
+# ===== PART 1: Initialize Persistent Session Service =====
+# Using SQLite database for persistent storage
+db_url = "sqlite:///./my_agent_data.db"
+session_service = DatabaseSessionService(db_url=db_url)
+
+
+# ===== PART 2: Define Initial State =====
+# This will only be used when creating a new session
+initial_state = {
+    "user_name": "Brandon Hancock",
+    "reminders": [],
+}
+
+
+async def main_async():
+    # Setup constants
+    APP_NAME = "Memory Agent"
+    USER_ID = "aiwithbrandon"
+
+    # ===== PART 3: Session Management - Find or Create =====
+    # Check for existing sessions for this user
+    existing_sessions = session_service.list_sessions(
+        app_name=APP_NAME,
+        user_id=USER_ID,
+    )
+
+    # If there's an existing session, use it, otherwise create a new one
+    if existing_sessions and len(existing_sessions.sessions) > 0:
+        # Use the most recent session
+        SESSION_ID = existing_sessions.sessions[0].id
+        print(f"Continuing existing session: {SESSION_ID}")
+    else:
+        # Create a new session with initial state
+        new_session = session_service.create_session(
+            app_name=APP_NAME,
+            user_id=USER_ID,
+            state=initial_state,
+        )
+        SESSION_ID = new_session.id
+        print(f"Created new session: {SESSION_ID}")
+
+    # ===== PART 4: Agent Runner Setup =====
+    # Create a runner with the memory agent
+    runner = Runner(
+        agent=memory_agent,
+        app_name=APP_NAME,
+        session_service=session_service,
+    )
+
+    # ===== PART 5: Interactive Conversation Loop =====
+    print("\nWelcome to Memory Agent Chat!")
+    print("Your reminders will be remembered across conversations.")
+    print("Type 'exit' or 'quit' to end the conversation.\n")
+
+    while True:
+        # Get user input
+        user_input = input("You: ")
+
+        # Check if user wants to exit
+        if user_input.lower() in ["exit", "quit"]:
+            print("Ending conversation. Your data has been saved to the database.")
+            break
+
+        # Process the user query through the agent
+        await call_agent_async(runner, USER_ID, SESSION_ID, user_input)
+
+
+if __name__ == "__main__":
+    asyncio.run(main_async())
diff --git a/ADK-Crash-Course/6-persistent-storage/memory_agent/__init__.py b/ADK-Crash-Course/6-persistent-storage/memory_agent/__init__.py
new file mode 100644
index 000000000..97cb942ad
--- /dev/null
+++ b/ADK-Crash-Course/6-persistent-storage/memory_agent/__init__.py
@@ -0,0 +1 @@
+# This file makes the memory_agent directory a Python package
diff --git a/ADK-Crash-Course/6-persistent-storage/memory_agent/agent.py b/ADK-Crash-Course/6-persistent-storage/memory_agent/agent.py
new file mode 100644
index 000000000..a10428176
--- /dev/null
+++ b/ADK-Crash-Course/6-persistent-storage/memory_agent/agent.py
@@ -0,0 +1,230 @@
+from google.adk.agents import Agent
+from google.adk.tools.tool_context import ToolContext
+
+
+def add_reminder(reminder: str, tool_context: ToolContext) -> dict:
+    """Add a new reminder to the user's reminder list.
+
+    Args:
+        reminder: The reminder text to add
+        tool_context: Context for accessing and updating session state
+
+    Returns:
+        A confirmation message
+    """
+    print(f"--- Tool: add_reminder called for '{reminder}' ---")
+
+    # Get current reminders from state
+    reminders = tool_context.state.get("reminders", [])
+
+    # Add the new reminder
+    reminders.append(reminder)
+
+    # Update state with the new list of reminders
+    tool_context.state["reminders"] = reminders
+
+    return {
+        "action": "add_reminder",
+        "reminder": reminder,
+        "message": f"Added reminder: {reminder}",
+    }
+
+
+def view_reminders(tool_context: ToolContext) -> dict:
+    """View all current reminders.
+
+    Args:
+        tool_context: Context for accessing session state
+
+    Returns:
+        The list of reminders
+    """
+    print("--- Tool: view_reminders called ---")
+
+    # Get reminders from state
+    reminders = tool_context.state.get("reminders", [])
+
+    return {"action": "view_reminders", "reminders": reminders, "count": len(reminders)}
+
+
+def update_reminder(index: int, updated_text: str, tool_context: ToolContext) -> dict:
+    """Update an existing reminder.
+
+    Args:
+        index: The 1-based index of the reminder to update
+        updated_text: The new text for the reminder
+        tool_context: Context for accessing and updating session state
+
+    Returns:
+        A confirmation message
+    """
+    print(
+        f"--- Tool: update_reminder called for index {index} with '{updated_text}' ---"
+    )
+
+    # Get current reminders from state
+    reminders = tool_context.state.get("reminders", [])
+
+    # Check if the index is valid
+    if not reminders or index < 1 or index > len(reminders):
+        return {
+            "action": "update_reminder",
+            "status": "error",
+            "message": f"Could not find reminder at position {index}. Currently there are {len(reminders)} reminders.",
+        }
+
+    # Update the reminder (adjusting for 0-based indices)
+    old_reminder = reminders[index - 1]
+    reminders[index - 1] = updated_text
+
+    # Update state with the modified list
+    tool_context.state["reminders"] = reminders
+
+    return {
+        "action": "update_reminder",
+        "index": index,
+        "old_text": old_reminder,
+        "updated_text": updated_text,
+        "message": f"Updated reminder {index} from '{old_reminder}' to '{updated_text}'",
+    }
+
+
+def delete_reminder(index: int, tool_context: ToolContext) -> dict:
+    """Delete a reminder.
+
+    Args:
+        index: The 1-based index of the reminder to delete
+        tool_context: Context for accessing and updating session state
+
+    Returns:
+        A confirmation message
+    """
+    print(f"--- Tool: delete_reminder called for index {index} ---")
+
+    # Get current reminders from state
+    reminders = tool_context.state.get("reminders", [])
+
+    # Check if the index is valid
+    if not reminders or index < 1 or index > len(reminders):
+        return {
+            "action": "delete_reminder",
+            "status": "error",
+            "message": f"Could not find reminder at position {index}. Currently there are {len(reminders)} reminders.",
+        }
+
+    # Remove the reminder (adjusting for 0-based indices)
+    deleted_reminder = reminders.pop(index - 1)
+
+    # Update state with the modified list
+    tool_context.state["reminders"] = reminders
+
+    return {
+        "action": "delete_reminder",
+        "index": index,
+        "deleted_reminder": deleted_reminder,
+        "message": f"Deleted reminder {index}: '{deleted_reminder}'",
+    }
+
+
+def update_user_name(name: str, tool_context: ToolContext) -> dict:
+    """Update the user's name.
+
+    Args:
+        name: The new name for the user
+        tool_context: Context for accessing and updating session state
+
+    Returns:
+        A confirmation message
+    """
+    print(f"--- Tool: update_user_name called with '{name}' ---")
+
+    # Get current name from state
+    old_name = tool_context.state.get("user_name", "")
+
+    # Update the name in state
+    tool_context.state["user_name"] = name
+
+    return {
+        "action": "update_user_name",
+        "old_name": old_name,
+        "new_name": name,
+        "message": f"Updated your name to: {name}",
+    }
+
+
+# Create a simple persistent agent
+memory_agent = Agent(
+    name="memory_agent",
+    model="gemini-2.0-flash",
+    description="A smart reminder agent with persistent memory",
+    instruction="""
+    You are a friendly reminder assistant that remembers users across conversations.
+    
+    The user's information is stored in state:
+    - User's name: {user_name}
+    - Reminders: {reminders}
+    
+    You can help users manage their reminders with the following capabilities:
+    1. Add new reminders
+    2. View existing reminders
+    3. Update reminders
+    4. Delete reminders
+    5. Update the user's name
+    
+    Always be friendly and address the user by name. If you don't know their name yet,
+    use the update_user_name tool to store it when they introduce themselves.
+    
+    **REMINDER MANAGEMENT GUIDELINES:**
+    
+    When dealing with reminders, you need to be smart about finding the right reminder:
+    
+    1. When the user asks to update or delete a reminder but doesn't provide an index:
+       - If they mention the content of the reminder (e.g., "delete my meeting reminder"), 
+         look through the reminders to find a match
+       - If you find an exact or close match, use that index
+       - Never clarify which reminder the user is referring to, just use the first match
+       - If no match is found, list all reminders and ask the user to specify
+    
+    2. When the user mentions a number or position:
+       - Use that as the index (e.g., "delete reminder 2" means index=2)
+       - Remember that indexing starts at 1 for the user
+    
+    3. For relative positions:
+       - Handle "first", "last", "second", etc. appropriately
+       - "First reminder" = index 1
+       - "Last reminder" = the highest index
+       - "Second reminder" = index 2, and so on
+    
+    4. For viewing:
+       - Always use the view_reminders tool when the user asks to see their reminders
+       - Format the response in a numbered list for clarity
+       - If there are no reminders, suggest adding some
+    
+    5. For addition:
+       - Extract the actual reminder text from the user's request
+       - Remove phrases like "add a reminder to" or "remind me to"
+       - Focus on the task itself (e.g., "add a reminder to buy milk" → add_reminder("buy milk"))
+    
+    6. For updates:
+       - Identify both which reminder to update and what the new text should be
+       - For example, "change my second reminder to pick up groceries" → update_reminder(2, "pick up groceries")
+    
+    7. For deletions:
+       - Confirm deletion when complete and mention which reminder was removed
+       - For example, "I've deleted your reminder to 'buy milk'"
+    
+    Remember to explain that you can remember their information across conversations.
+
+    IMPORTANT:
+    - use your best judgement to determine which reminder the user is referring to. 
+    - You don't have to be 100% correct, but try to be as close as possible.
+    - Never ask the user to clarify which reminder they are referring to.
+    """,
+    tools=[
+        add_reminder,
+        view_reminders,
+        update_reminder,
+        delete_reminder,
+        update_user_name,
+    ],
+)
diff --git a/ADK-Crash-Course/6-persistent-storage/utils.py b/ADK-Crash-Course/6-persistent-storage/utils.py
new file mode 100644
index 000000000..110e17a62
--- /dev/null
+++ b/ADK-Crash-Course/6-persistent-storage/utils.py
@@ -0,0 +1,153 @@
+from google.genai import types
+
+
+# ANSI color codes for terminal output
+class Colors:
+    RESET = "\033[0m"
+    BOLD = "\033[1m"
+    UNDERLINE = "\033[4m"
+
+    # Foreground colors
+    BLACK = "\033[30m"
+    RED = "\033[31m"
+    GREEN = "\033[32m"
+    YELLOW = "\033[33m"
+    BLUE = "\033[34m"
+    MAGENTA = "\033[35m"
+    CYAN = "\033[36m"
+    WHITE = "\033[37m"
+
+    # Background colors
+    BG_BLACK = "\033[40m"
+    BG_RED = "\033[41m"
+    BG_GREEN = "\033[42m"
+    BG_YELLOW = "\033[43m"
+    BG_BLUE = "\033[44m"
+    BG_MAGENTA = "\033[45m"
+    BG_CYAN = "\033[46m"
+    BG_WHITE = "\033[47m"
+
+
+def display_state(
+    session_service, app_name, user_id, session_id, label="Current State"
+):
+    """Display the current session state in a formatted way."""
+    try:
+        session = session_service.get_session(
+            app_name=app_name, user_id=user_id, session_id=session_id
+        )
+
+        # Format the output with clear sections
+        print(f"\n{'-' * 10} {label} {'-' * 10}")
+
+        # Handle the user name
+        user_name = session.state.get("user_name", "Unknown")
+        print(f"👤 User: {user_name}")
+
+        # Handle reminders
+        reminders = session.state.get("reminders", [])
+        if reminders:
+            print("📝 Reminders:")
+            for idx, reminder in enumerate(reminders, 1):
+                print(f"  {idx}. {reminder}")
+        else:
+            print("📝 Reminders: None")
+
+        print("-" * (22 + len(label)))
+    except Exception as e:
+        print(f"Error displaying state: {e}")
+
+
+async def process_agent_response(event):
+    """Process and display agent response events."""
+    # Log basic event info
+    print(f"Event ID: {event.id}, Author: {event.author}")
+
+    # Check for specific parts first
+    has_specific_part = False
+    if event.content and event.content.parts:
+        for part in event.content.parts:
+            if hasattr(part, "executable_code") and part.executable_code:
+                # Access the actual code string via .code
+                print(
+                    f"  Debug: Agent generated code:\n```python\n{part.executable_code.code}\n```"
+                )
+                has_specific_part = True
+            elif hasattr(part, "code_execution_result") and part.code_execution_result:
+                # Access outcome and output correctly
+                print(
+                    f"  Debug: Code Execution Result: {part.code_execution_result.outcome} - Output:\n{part.code_execution_result.output}"
+                )
+                has_specific_part = True
+            elif hasattr(part, "tool_response") and part.tool_response:
+                # Print tool response information
+                print(f"  Tool Response: {part.tool_response.output}")
+                has_specific_part = True
+            # Also print any text parts found in any event for debugging
+            elif hasattr(part, "text") and part.text and not part.text.isspace():
+                print(f"  Text: '{part.text.strip()}'")
+
+    # Check for final response after specific parts
+    final_response = None
+    if event.is_final_response():
+        if (
+            event.content
+            and event.content.parts
+            and hasattr(event.content.parts[0], "text")
+            and event.content.parts[0].text
+        ):
+            final_response = event.content.parts[0].text.strip()
+            # Use colors and formatting to make the final response stand out
+            print(
+                f"\n{Colors.BG_BLUE}{Colors.WHITE}{Colors.BOLD}╔══ AGENT RESPONSE ═════════════════════════════════════════{Colors.RESET}"
+            )
+            print(f"{Colors.CYAN}{Colors.BOLD}{final_response}{Colors.RESET}")
+            print(
+                f"{Colors.BG_BLUE}{Colors.WHITE}{Colors.BOLD}╚═════════════════════════════════════════════════════════════{Colors.RESET}\n"
+            )
+        else:
+            print(
+                f"\n{Colors.BG_RED}{Colors.WHITE}{Colors.BOLD}==> Final Agent Response: [No text content in final event]{Colors.RESET}\n"
+            )
+
+    return final_response
+
+
+async def call_agent_async(runner, user_id, session_id, query):
+    """Call the agent asynchronously with the user's query."""
+    content = types.Content(role="user", parts=[types.Part(text=query)])
+    print(
+        f"\n{Colors.BG_GREEN}{Colors.BLACK}{Colors.BOLD}--- Running Query: {query} ---{Colors.RESET}"
+    )
+    final_response_text = None
+
+    # Display state before processing
+    display_state(
+        runner.session_service,
+        runner.app_name,
+        user_id,
+        session_id,
+        "State BEFORE processing",
+    )
+
+    try:
+        async for event in runner.run_async(
+            user_id=user_id, session_id=session_id, new_message=content
+        ):
+            # Process each event and get the final response if available
+            response = await process_agent_response(event)
+            if response:
+                final_response_text = response
+    except Exception as e:
+        print(f"Error during agent call: {e}")
+
+    # Display state after processing the message
+    display_state(
+        runner.session_service,
+        runner.app_name,
+        user_id,
+        session_id,
+        "State AFTER processing",
+    )
+
+    return final_response_text
diff --git a/ADK-Crash-Course/7-multi-agent/README.md b/ADK-Crash-Course/7-multi-agent/README.md
new file mode 100644
index 000000000..e8411b2dd
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/README.md
@@ -0,0 +1,228 @@
+# Multi-Agent Systems in ADK
+
+This example demonstrates how to create a multi-agent system in ADK, where specialized agents collaborate to handle complex tasks, each focusing on their area of expertise.
+
+## What is a Multi-Agent System?
+
+A Multi-Agent System is an advanced pattern in the Agent Development Kit (ADK) that allows multiple specialized agents to work together to handle complex tasks. Each agent can focus on a specific domain or functionality, and they can collaborate through delegation and communication to solve problems that would be difficult for a single agent.
+
+## Project Structure Requirements
+
+For multi-agent systems to work properly with ADK, your project must follow a specific structure:
+
+```
+parent_folder/
+├── root_agent_folder/           # Main agent package (e.g., "manager")
+│   ├── __init__.py              # Must import agent.py
+│   ├── agent.py                 # Must define root_agent
+│   ├── .env                     # Environment variables
+│   └── sub_agents/              # Directory for all sub-agents
+│       ├── __init__.py          # Empty or imports sub-agents
+│       ├── agent_1_folder/      # Sub-agent package
+│       │   ├── __init__.py      # Must import agent.py
+│       │   └── agent.py         # Must define an agent variable
+│       ├── agent_2_folder/
+│       │   ├── __init__.py
+│       │   └── agent.py
+│       └── ...
+```
+
+### Essential Structure Components:
+
+1. **Root Agent Package**
+   - Must have the standard agent structure (like in the basic agent example)
+   - The `agent.py` file must define a `root_agent` variable
+
+2. **Sub-agents Directory**
+   - Typically organized as a directory called `sub_agents` inside the root agent folder
+   - Each sub-agent should be in its own directory following the same structure as regular agents
+
+3. **Importing Sub-agents**
+   - Root agent must import sub-agents to use them:
+   ```python
+   from .sub_agents.funny_nerd.agent import funny_nerd
+   from .sub_agents.stock_analyst.agent import stock_analyst
+   ```
+
+4. **Command Location**
+   - Always run `adk web` from the parent directory (`6-multi-agent`), not from inside any agent directory
+
+This structure ensures that ADK can discover and correctly load all agents in the hierarchy.
+
+## Multi-Agent Architecture Options
+
+ADK offers two primary approaches to building multi-agent systems:
+
+### 1. Sub-Agent Delegation Model
+
+Using the `sub_agents` parameter, the root agent can fully delegate tasks to specialized agents:
+
+```python
+root_agent = Agent(
+    name="manager",
+    model="gemini-2.0-flash",
+    description="Manager agent",
+    instruction="You are a manager agent that delegates tasks to specialized agents...",
+    sub_agents=[stock_analyst, funny_nerd],
+)
+```
+
+**Characteristics:**
+- Complete delegation - sub-agent takes over the entire response
+- The sub-agent decision is final and takes control of the conversation
+- Root agent acts as a "router" determining which specialist should handle the query
+
+### 2. Agent-as-a-Tool Model
+
+Using the `AgentTool` wrapper, agents can be used as tools by other agents:
+
+```python
+from google.adk.tools.agent_tool import AgentTool
+
+root_agent = Agent(
+    name="manager",
+    model="gemini-2.0-flash",
+    description="Manager agent",
+    instruction="You are a manager agent that uses specialized agents as tools...",
+    tools=[
+        AgentTool(news_analyst),
+        get_current_time,
+    ],
+)
+```
+
+**Characteristics:**
+- Sub-agent returns results to the root agent
+- Root agent maintains control and can incorporate the sub-agent's response into its own
+- Multiple tool calls can be made to different agent tools in a single response
+- Gives the root agent more flexibility in how it uses the results
+
+## Limitations When Using Multi-Agents
+
+### Sub-agent Restrictions
+
+**Built-in tools cannot be used within a sub-agent.**
+
+For example, this approach using built-in tools within sub-agents is **not** currently supported:
+
+```python
+search_agent = Agent(
+    model='gemini-2.0-flash',
+    name='SearchAgent',
+    instruction="You're a specialist in Google Search",
+    tools=[google_search],  # Built-in tool
+)
+coding_agent = Agent(
+    model='gemini-2.0-flash',
+    name='CodeAgent',
+    instruction="You're a specialist in Code Execution",
+    tools=[built_in_code_execution],  # Built-in tool
+)
+root_agent = Agent(
+    name="RootAgent",
+    model="gemini-2.0-flash",
+    description="Root Agent",
+    sub_agents=[
+        search_agent,  # NOT SUPPORTED
+        coding_agent   # NOT SUPPORTED
+    ],
+)
+```
+
+### Workaround Using Agent Tools
+
+To use multiple built-in tools or to combine built-in tools with other tools, you can use the `AgentTool` approach:
+
+```python
+from google.adk.tools import agent_tool
+
+search_agent = Agent(
+    model='gemini-2.0-flash',
+    name='SearchAgent',
+    instruction="You're a specialist in Google Search",
+    tools=[google_search],
+)
+coding_agent = Agent(
+    model='gemini-2.0-flash',
+    name='CodeAgent',
+    instruction="You're a specialist in Code Execution",
+    tools=[built_in_code_execution],
+)
+root_agent = Agent(
+    name="RootAgent",
+    model="gemini-2.0-flash",
+    description="Root Agent",
+    tools=[
+        agent_tool.AgentTool(agent=search_agent), 
+        agent_tool.AgentTool(agent=coding_agent)
+    ],
+)
+```
+
+This approach wraps agents as tools, allowing the root agent to delegate to specialized agents that each use a single built-in tool.
+
+## Our Multi-Agent Example
+
+This example implements a manager agent that works with three specialized agents:
+
+1. **Stock Analyst** (Sub-agent): Provides financial information and stock market insights
+2. **Funny Nerd** (Sub-agent): Creates nerdy jokes about technical topics
+3. **News Analyst** (Agent Tool): Gives summaries of current technology news
+
+The manager agent routes queries to the appropriate specialist based on the content of the user's request.
+
+## Getting Started
+
+This example uses the same virtual environment created in the root directory. Make sure you have:
+
+1. Activated the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Set up your API key:
+   - Rename `.env.example` to `.env` in the manager folder
+   - Add your Google API key to the `GOOGLE_API_KEY` variable in the `.env` file
+
+## Running the Example
+
+To run the multi-agent example:
+
+1. Navigate to the 6-multi-agent directory containing your agent folders.
+
+2. Start the interactive web UI:
+```bash
+adk web
+```
+
+3. Access the web UI by opening the URL shown in your terminal (typically http://localhost:8000)
+
+4. Select the "manager" agent from the dropdown menu in the top-left corner of the UI
+
+5. Start chatting with your agent in the textbox at the bottom of the screen
+
+### Troubleshooting
+
+If your multi-agent setup doesn't appear properly in the dropdown menu:
+- Make sure you're running `adk web` from the parent directory (6-multi-agent)
+- Verify that each agent's `__init__.py` properly imports its respective `agent.py`
+- Check that the root agent properly imports all sub-agents
+
+### Example Prompts to Try
+
+- "Can you tell me about the stock market today?"
+- "Tell me something funny about programming"
+- "What's the latest tech news?"
+- "What time is it right now?"
+
+You can exit the conversation or stop the server by pressing `Ctrl+C` in your terminal.
+
+## Additional Resources
+
+- [ADK Multi-Agent Systems Documentation](https://google.github.io/adk-docs/agents/multi-agent-systems/)
+- [Agent Tools Documentation](https://google.github.io/adk-docs/tools/function-tools/#3-agent-as-a-tool)
diff --git a/ADK-Crash-Course/7-multi-agent/manager/__init__.py b/ADK-Crash-Course/7-multi-agent/manager/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/manager/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/7-multi-agent/manager/agent.py b/ADK-Crash-Course/7-multi-agent/manager/agent.py
new file mode 100644
index 000000000..552b83c4e
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/manager/agent.py
@@ -0,0 +1,32 @@
+from google.adk.agents import Agent
+from google.adk.tools.agent_tool import AgentTool
+
+from .sub_agents.funny_nerd.agent import funny_nerd
+from .sub_agents.news_analyst.agent import news_analyst
+from .sub_agents.stock_analyst.agent import stock_analyst
+from .tools.tools import get_current_time
+
+root_agent = Agent(
+    name="manager",
+    model="gemini-2.0-flash",
+    description="Manager agent",
+    instruction="""
+    You are a manager agent that is responsible for overseeing the work of the other agents.
+
+    Always delegate the task to the appropriate agent. Use your best judgement 
+    to determine which agent to delegate to.
+
+    You are responsible for delegating tasks to the following agent:
+    - stock_analyst
+    - funny_nerd
+
+    You also have access to the following tools:
+    - news_analyst
+    - get_current_time
+    """,
+    sub_agents=[stock_analyst, funny_nerd],
+    tools=[
+        AgentTool(news_analyst),
+        get_current_time,
+    ],
+)
diff --git a/ADK-Crash-Course/7-multi-agent/manager/sub_agents/__init__.py b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/ADK-Crash-Course/7-multi-agent/manager/sub_agents/funny_nerd/agent.py b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/funny_nerd/agent.py
new file mode 100644
index 000000000..aa49918d1
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/funny_nerd/agent.py
@@ -0,0 +1,63 @@
+from google.adk.agents import Agent
+from google.adk.tools.tool_context import ToolContext
+
+
+def get_nerd_joke(topic: str, tool_context: ToolContext) -> dict:
+    """Get a nerdy joke about a specific topic."""
+    print(f"--- Tool: get_nerd_joke called for topic: {topic} ---")
+
+    # Example jokes - in a real implementation, you might want to use an API
+    jokes = {
+        "python": "Why don't Python programmers like to use inheritance? Because they don't like to inherit anything!",
+        "javascript": "Why did the JavaScript developer go broke? Because he used up all his cache!",
+        "java": "Why do Java developers wear glasses? Because they can't C#!",
+        "programming": "Why do programmers prefer dark mode? Because light attracts bugs!",
+        "math": "Why was the equal sign so humble? Because he knew he wasn't less than or greater than anyone else!",
+        "physics": "Why did the photon check a hotel? Because it was travelling light!",
+        "chemistry": "Why did the acid go to the gym? To become a buffer solution!",
+        "biology": "Why did the cell go to therapy? Because it had too many issues!",
+        "default": "Why did the computer go to the doctor? Because it had a virus!",
+    }
+
+    joke = jokes.get(topic.lower(), jokes["default"])
+
+    # Update state with the last joke topic
+    tool_context.state["last_joke_topic"] = topic
+
+    return {"status": "success", "joke": joke, "topic": topic}
+
+
+# Create the funny nerd agent
+funny_nerd = Agent(
+    name="funny_nerd",
+    model="gemini-2.0-flash",
+    description="An agent that tells nerdy jokes about various topics.",
+    instruction="""
+    You are a funny nerd agent that tells nerdy jokes about various topics.
+    
+    When asked to tell a joke:
+    1. Use the get_nerd_joke tool to fetch a joke about the requested topic
+    2. If no specific topic is mentioned, ask the user what kind of nerdy joke they'd like to hear
+    3. Format the response to include both the joke and a brief explanation if needed
+    
+    Available topics include:
+    - python
+    - javascript
+    - java
+    - programming
+    - math
+    - physics
+    - chemistry
+    - biology
+    
+    Example response format:
+    "Here's a nerdy joke about <TOPIC>:
+    <JOKE>
+    
+    Explanation: {brief explanation if needed}"
+
+    If the user asks about anything else, 
+    you should delegate the task to the manager agent.
+    """,
+    tools=[get_nerd_joke],
+)
diff --git a/ADK-Crash-Course/7-multi-agent/manager/sub_agents/news_analyst/agent.py b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/news_analyst/agent.py
new file mode 100644
index 000000000..181412f0d
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/news_analyst/agent.py
@@ -0,0 +1,16 @@
+from google.adk.agents import Agent
+from google.adk.tools import google_search
+
+news_analyst = Agent(
+    name="news_analyst",
+    model="gemini-2.0-flash",
+    description="News analyst agent",
+    instruction="""
+    You are a helpful assistant that can analyze news articles and provide a summary of the news.
+
+    When asked about news, you should use the google_search tool to search for the news.
+
+    If the user ask for news using a relative time, you should use the get_current_time tool to get the current time to use in the search query.
+    """,
+    tools=[google_search],
+)
diff --git a/ADK-Crash-Course/7-multi-agent/manager/sub_agents/stock_analyst/agent.py b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/stock_analyst/agent.py
new file mode 100644
index 000000000..f495ac711
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/manager/sub_agents/stock_analyst/agent.py
@@ -0,0 +1,59 @@
+from datetime import datetime
+
+import yfinance as yf
+from google.adk.agents import Agent
+
+
+def get_stock_price(ticker: str) -> dict:
+    """Retrieves current stock price and saves to session state."""
+    print(f"--- Tool: get_stock_price called for {ticker} ---")
+
+    try:
+        # Fetch stock data
+        stock = yf.Ticker(ticker)
+        current_price = stock.info.get("currentPrice")
+
+        if current_price is None:
+            return {
+                "status": "error",
+                "error_message": f"Could not fetch price for {ticker}",
+            }
+
+        # Get current timestamp
+        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        return {
+            "status": "success",
+            "ticker": ticker,
+            "price": current_price,
+            "timestamp": current_time,
+        }
+
+    except Exception as e:
+        return {
+            "status": "error",
+            "error_message": f"Error fetching stock data: {str(e)}",
+        }
+
+
+# Create the root agent
+stock_analyst = Agent(
+    name="stock_analyst",
+    model="gemini-2.0-flash",
+    description="An agent that can look up stock prices and track them over time.",
+    instruction="""
+    You are a helpful stock market assistant that helps users track their stocks of interest.
+    
+    When asked about stock prices:
+    1. Use the get_stock_price tool to fetch the latest price for the requested stock(s)
+    2. Format the response to show each stock's current price and the time it was fetched
+    3. If a stock price couldn't be fetched, mention this in your response
+    
+    Example response format:
+    "Here are the current prices for your stocks:
+    - GOOG: $175.34 (updated at 2024-04-21 16:30:00)
+    - TSLA: $156.78 (updated at 2024-04-21 16:30:00)
+    - META: $123.45 (updated at 2024-04-21 16:30:00)"
+    """,
+    tools=[get_stock_price],
+)
diff --git a/ADK-Crash-Course/7-multi-agent/manager/tools/tools.py b/ADK-Crash-Course/7-multi-agent/manager/tools/tools.py
new file mode 100644
index 000000000..3ea2ef607
--- /dev/null
+++ b/ADK-Crash-Course/7-multi-agent/manager/tools/tools.py
@@ -0,0 +1,10 @@
+from datetime import datetime
+
+
+def get_current_time() -> dict:
+    """
+    Get the current time in the format YYYY-MM-DD HH:MM:SS
+    """
+    return {
+        "current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    }
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/README.md b/ADK-Crash-Course/8-stateful-multi-agent/README.md
new file mode 100644
index 000000000..605bae832
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/README.md
@@ -0,0 +1,220 @@
+# Stateful Multi-Agent Systems in ADK
+
+This example demonstrates how to create a stateful multi-agent system in ADK, combining the power of persistent state management with specialized agent delegation. This approach creates intelligent agent systems that remember user information across interactions while leveraging specialized domain expertise.
+
+## What is a Stateful Multi-Agent System?
+
+A Stateful Multi-Agent System combines two powerful patterns:
+
+1. **State Management**: Persisting information about users and conversations across interactions
+2. **Multi-Agent Architecture**: Distributing tasks among specialized agents based on their expertise
+
+The result is a sophisticated agent ecosystem that can:
+- Remember user information and interaction history
+- Route queries to the most appropriate specialized agent
+- Provide personalized responses based on past interactions
+- Maintain context across multiple agent delegates
+
+This example implements a customer service system for an online course platform, where specialized agents handle different aspects of customer support while sharing a common state.
+
+## Project Structure
+
+```
+7-stateful-multi-agent/
+│
+├── customer_service_agent/         # Main agent package
+│   ├── __init__.py                 # Required for ADK discovery
+│   ├── agent.py                    # Root agent definition
+│   └── sub_agents/                 # Specialized agents
+│       ├── course_support_agent/   # Handles course content questions
+│       ├── order_agent/            # Manages order history and refunds
+│       ├── policy_agent/           # Answers policy questions
+│       └── sales_agent/            # Handles course purchases
+│
+├── main.py                         # Application entry point with session setup
+├── utils.py                        # Helper functions for state management
+├── .env                            # Environment variables
+└── README.md                       # This documentation
+```
+
+## Key Components
+
+### 1. Session Management
+
+The example uses `InMemorySessionService` to store session state:
+
+```python
+session_service = InMemorySessionService()
+
+def initialize_state():
+    """Initialize the session state with default values."""
+    return {
+        "user_name": "Brandon Hancock",
+        "purchased_courses": [""],
+        "interaction_history": [],
+    }
+
+# Create a new session with initial state
+session_service.create_session(
+    app_name=APP_NAME,
+    user_id=USER_ID,
+    session_id=SESSION_ID,
+    state=initialize_state(),
+)
+```
+
+### 2. State Sharing Across Agents
+
+All agents in the system can access the same session state, enabling:
+- Root agent to track interaction history
+- Sales agent to update purchased courses
+- Course support agent to check if user has purchased specific courses
+- All agents to personalize responses based on user information
+
+### 3. Multi-Agent Delegation
+
+The customer service agent routes queries to specialized sub-agents:
+
+```python
+customer_service_agent = Agent(
+    name="customer_service",
+    model="gemini-2.0-flash",
+    description="Customer service agent for AI Developer Accelerator community",
+    instruction="""
+    You are the primary customer service agent for the AI Developer Accelerator community.
+    Your role is to help users with their questions and direct them to the appropriate specialized agent.
+    
+    # ... detailed instructions ...
+    
+    """,
+    sub_agents=[policy_agent, sales_agent, course_support_agent, order_agent],
+    tools=[get_current_time],
+)
+```
+
+## How It Works
+
+1. **Initial Session Creation**:
+   - A new session is created with user information and empty interaction history
+   - Session state is initialized with default values
+
+2. **Conversation Tracking**:
+   - Each user message is added to `interaction_history` in the state
+   - Agents can review past interactions to maintain context
+
+3. **Query Routing**:
+   - The root agent analyzes the user query and decides which specialist should handle it
+   - Specialized agents receive the full state context when delegated to
+
+4. **State Updates**:
+   - When a user purchases a course, the sales agent updates `purchased_courses`
+   - These updates are available to all agents for future interactions
+
+5. **Personalized Responses**:
+   - Agents tailor responses based on purchase history and previous interactions
+   - Different paths are taken based on what the user has already purchased
+
+## Getting Started
+
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Make sure your Google API key is set in the `.env` file:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Example
+
+To run the stateful multi-agent example:
+
+```bash
+python main.py
+```
+
+This will:
+1. Initialize a new session with default state
+2. Start an interactive conversation with the customer service agent
+3. Track all interactions in the session state
+4. Allow specialized agents to handle specific queries
+
+### Example Conversation Flow
+
+Try this conversation flow to test the system:
+
+1. **Start with a general query**:
+   - "What courses do you offer?"
+   - (Root agent will route to sales agent)
+
+2. **Ask about purchasing**:
+   - "I want to buy the AI Marketing Platform course"
+   - (Sales agent will process the purchase and update state)
+
+3. **Ask about course content**:
+   - "Can you tell me about the content in the AI Marketing Platform course?"
+   - (Root agent will route to course support agent, which now has access)
+
+4. **Ask about refunds**:
+   - "What's your refund policy?"
+   - (Root agent will route to policy agent)
+
+Notice how the system remembers your purchase across different specialized agents!
+
+## Advanced Features
+
+### 1. Interaction History Tracking
+
+The system maintains a history of interactions to provide context:
+
+```python
+# Update interaction history with the user's query
+add_user_query_to_history(
+    session_service, APP_NAME, USER_ID, SESSION_ID, user_input
+)
+```
+
+### 2. Dynamic Access Control
+
+The system implements conditional access to certain agents:
+
+```
+3. Course Support Agent
+   - For questions about course content
+   - Only available for courses the user has purchased
+   - Check if "ai_marketing_platform" is in the purchased courses before directing here
+```
+
+### 3. State-Based Personalization
+
+All agents tailor responses based on session state:
+
+```
+Tailor your responses based on the user's purchase history and previous interactions.
+When the user hasn't purchased any courses yet, encourage them to explore the AI Marketing Platform.
+When the user has purchased courses, offer support for those specific courses.
+```
+
+## Production Considerations
+
+For a production implementation, consider:
+
+1. **Persistent Storage**: Replace `InMemorySessionService` with `DatabaseSessionService` to persist state across application restarts
+2. **User Authentication**: Implement proper user authentication to securely identify users
+3. **Error Handling**: Add robust error handling for agent failures and state corruption
+4. **Monitoring**: Implement logging and monitoring to track system performance
+
+## Additional Resources
+
+- [ADK Sessions Documentation](https://google.github.io/adk-docs/sessions/session/)
+- [ADK Multi-Agent Systems Documentation](https://google.github.io/adk-docs/agents/multi-agent-systems/)
+- [State Management in ADK](https://google.github.io/adk-docs/sessions/state/)
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/__init__.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/agent.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/agent.py
new file mode 100644
index 000000000..cb10e0d93
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/agent.py
@@ -0,0 +1,80 @@
+from google.adk.agents import Agent
+
+from .sub_agents.course_support_agent.agent import course_support_agent
+from .sub_agents.order_agent.agent import order_agent
+from .sub_agents.policy_agent.agent import policy_agent
+from .sub_agents.sales_agent.agent import sales_agent
+
+# Create the root customer service agent
+customer_service_agent = Agent(
+    name="customer_service",
+    model="gemini-2.0-flash",
+    description="Customer service agent for AI Developer Accelerator community",
+    instruction="""
+    You are the primary customer service agent for the AI Developer Accelerator community.
+    Your role is to help users with their questions and direct them to the appropriate specialized agent.
+
+    **Core Capabilities:**
+
+    1. Query Understanding & Routing
+       - Understand user queries about policies, course purchases, course support, and orders
+       - Direct users to the appropriate specialized agent
+       - Maintain conversation context using state
+
+    2. State Management
+       - Track user interactions in state['interaction_history']
+       - Monitor user's purchased courses in state['purchased_courses']
+         - Course information is stored as objects with "id" and "purchase_date" properties
+       - Use state to provide personalized responses
+
+    **User Information:**
+    <user_info>
+    Name: {user_name}
+    </user_info>
+
+    **Purchase Information:**
+    <purchase_info>
+    Purchased Courses: {purchased_courses}
+    </purchase_info>
+
+    **Interaction History:**
+    <interaction_history>
+    {interaction_history}
+    </interaction_history>
+
+    You have access to the following specialized agents:
+
+    1. Policy Agent
+       - For questions about community guidelines, course policies, refunds
+       - Direct policy-related queries here
+
+    2. Sales Agent
+       - For questions about purchasing the AI Marketing Platform course
+       - Handles course purchases and updates state
+       - Course price: $149
+
+    3. Course Support Agent
+       - For questions about course content
+       - Only available for courses the user has purchased
+       - Check if a course with id "ai_marketing_platform" exists in the purchased courses before directing here
+
+    4. Order Agent
+       - For checking purchase history and processing refunds
+       - Shows courses user has bought
+       - Can process course refunds (30-day money-back guarantee)
+       - References the purchased courses information
+
+    Tailor your responses based on the user's purchase history and previous interactions.
+    When the user hasn't purchased any courses yet, encourage them to explore the AI Marketing Platform.
+    When the user has purchased courses, offer support for those specific courses.
+
+    When users express dissatisfaction or ask for a refund:
+    - Direct them to the Order Agent, which can process refunds
+    - Mention our 30-day money-back guarantee policy
+
+    Always maintain a helpful and professional tone. If you're unsure which agent to delegate to,
+    ask clarifying questions to better understand the user's needs.
+    """,
+    sub_agents=[policy_agent, sales_agent, course_support_agent, order_agent],
+    tools=[],
+)
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/course_support_agent/__init__.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/course_support_agent/__init__.py
new file mode 100644
index 000000000..80558d743
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/course_support_agent/__init__.py
@@ -0,0 +1,3 @@
+from .agent import course_support_agent
+
+__all__ = ["course_support_agent"]
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/course_support_agent/agent.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/course_support_agent/agent.py
new file mode 100644
index 000000000..b5bd36acf
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/course_support_agent/agent.py
@@ -0,0 +1,143 @@
+from google.adk.agents import Agent
+
+# Create the course support agent
+course_support_agent = Agent(
+    name="course_support",
+    model="gemini-2.0-flash",
+    description="Course support agent for the AI Marketing Platform course",
+    instruction="""
+    You are the course support agent for the Fullstack AI Marketing Platform course.
+    Your role is to help users with questions about course content and sections.
+
+    <user_info>
+    Name: {user_name}
+    </user_info>
+
+    <purchase_info>
+    Purchased Courses: {purchased_courses}
+    </purchase_info>
+
+    Before helping:
+    - Check if the user owns the AI Marketing Platform course
+    - Course information is stored as objects with "id" and "purchase_date" properties
+    - Look for a course with id "ai_marketing_platform" in the purchased courses
+    - Only provide detailed help if they own the course
+    - If they don't own the course, direct them to the sales agent
+    - If they do own the course, you can mention when they purchased it (from the purchase_date property)
+
+    Course Sections:
+    1. Introduction
+       - Course Overview
+       - Tech Stack Introduction
+       - Project Goals
+
+    2. Problem, Solution, & Technical Design
+       - Market Analysis
+       - Architecture Overview
+       - Tech Stack Selection
+
+    3. Models & Views - How To Think
+       - Data Modeling
+       - View Structure
+       - Component Design
+
+    4. Setup Environment
+       - Development Tools
+       - Configuration
+       - Dependencies
+
+    5. Create Projects
+       - Project Structure
+       - Initial Setup
+       - Basic Configuration
+
+    6. Software Deployment Tools
+       - Deployment Options
+       - CI/CD Setup
+       - Monitoring
+
+    7. NextJS Crash Course
+       - Fundamentals
+       - Routing
+       - API Routes
+
+    8. Stub Out NextJS App
+       - Create app directory structure
+       - Setup initial layouts
+       - Configure NextJS routing
+       - Create placeholder components
+
+    9. Create Responsive Sidebar
+       - Design mobile-friendly sidebar
+       - Implement sidebar navigation
+       - Add responsive breakpoints
+       - Create menu toggling behavior
+
+    10. Setup Auth with Clerk
+       - Integrate Clerk authentication
+       - Create login/signup flows
+       - Configure protected routes
+       - Setup user session management
+
+    11. Setup Postgres Database & Blob Storage
+       - Configure database connections
+       - Create schema and migrations
+       - Setup file/image storage
+       - Implement data access patterns
+
+    12. Projects Build Out (List & Detail)
+       - Create projects listing page
+       - Implement project detail views
+       - Add CRUD operations for projects
+       - Create data fetching hooks
+
+    13. Asset Processing NextJS
+       - Client-side image optimization
+       - Asset loading strategies
+       - Implementing CDN integration
+       - Frontend caching mechanisms
+
+    14. Asset Processing Server
+       - Server-side image manipulation
+       - Batch processing workflows
+       - Compression and optimization
+       - Storage management solutions
+
+    15. Prompt Management
+       - Create prompt templates
+       - Build prompt versioning system
+       - Implement prompt testing tools
+       - Design prompt chaining capabilities
+
+    16. Fully Build Template (List & Detail)
+       - Create template management system
+       - Implement template editor
+       - Design template marketplace
+       - Add template sharing features
+
+    17. AI Content Generation
+       - Integrate AI generation capabilities
+       - Design content generation workflows
+       - Create output validation systems
+       - Implement feedback mechanisms
+
+    18. Setup Stripe + Block Free Users
+       - Integrate Stripe payment processing
+       - Create subscription management
+       - Implement payment webhooks
+       - Design feature access restrictions
+
+    19. Landing & Pricing Pages
+       - Design conversion-optimized landing pages
+       - Create pricing tier comparisons
+       - Implement checkout flows
+       - Add testimonials and social proof
+
+    When helping:
+    1. Direct users to specific sections
+    2. Explain concepts clearly
+    3. Provide context for how sections connect
+    4. Encourage hands-on practice
+    """,
+    tools=[],
+)
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/order_agent/agent.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/order_agent/agent.py
new file mode 100644
index 000000000..7b5ab7140
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/order_agent/agent.py
@@ -0,0 +1,134 @@
+from datetime import datetime
+
+from google.adk.agents import Agent
+from google.adk.tools.tool_context import ToolContext
+
+
+def get_current_time() -> dict:
+    """Get the current time in the format YYYY-MM-DD HH:MM:SS"""
+    return {
+        "current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+    }
+
+
+def refund_course(tool_context: ToolContext) -> dict:
+    """
+    Simulates refunding the AI Marketing Platform course.
+    Updates state by removing the course from purchased_courses.
+    """
+    course_id = "ai_marketing_platform"
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Get current purchased courses
+    current_purchased_courses = tool_context.state.get("purchased_courses", [])
+
+    # Check if user owns the course
+    course_ids = [
+        course["id"] for course in current_purchased_courses if isinstance(course, dict)
+    ]
+    if course_id not in course_ids:
+        return {
+            "status": "error",
+            "message": "You don't own this course, so it can't be refunded.",
+        }
+
+    # Create new list without the course to be refunded
+    new_purchased_courses = []
+    for course in current_purchased_courses:
+        # Skip empty entries or non-dict entries
+        if not course or not isinstance(course, dict):
+            continue
+        # Skip the course being refunded
+        if course.get("id") == course_id:
+            continue
+        # Keep all other courses
+        new_purchased_courses.append(course)
+
+    # Update purchased courses in state via assignment
+    tool_context.state["purchased_courses"] = new_purchased_courses
+
+    # Get current interaction history
+    current_interaction_history = tool_context.state.get("interaction_history", [])
+
+    # Create new interaction history with refund added
+    new_interaction_history = current_interaction_history.copy()
+    new_interaction_history.append(
+        {"action": "refund_course", "course_id": course_id, "timestamp": current_time}
+    )
+
+    # Update interaction history in state via assignment
+    tool_context.state["interaction_history"] = new_interaction_history
+
+    return {
+        "status": "success",
+        "message": """Successfully refunded the AI Marketing Platform course! 
+         Your $149 will be returned to your original payment method within 3-5 business days.""",
+        "course_id": course_id,
+        "timestamp": current_time,
+    }
+
+
+# Create the order agent
+order_agent = Agent(
+    name="order_agent",
+    model="gemini-2.0-flash",
+    description="Order agent for viewing purchase history and processing refunds",
+    instruction="""
+    You are the order agent for the AI Developer Accelerator community.
+    Your role is to help users view their purchase history, course access, and process refunds.
+
+    <user_info>
+    Name: {user_name}
+    </user_info>
+
+    <purchase_info>
+    Purchased Courses: {purchased_courses}
+    </purchase_info>
+
+    <interaction_history>
+    {interaction_history}
+    </interaction_history>
+
+    When users ask about their purchases:
+    1. Check their course list from the purchase info above
+       - Course information is stored as objects with "id" and "purchase_date" properties
+    2. Format the response clearly showing:
+       - Which courses they own
+       - When they were purchased (from the course.purchase_date property)
+
+    When users request a refund:
+    1. Verify they own the course they want to refund ("ai_marketing_platform")
+    2. If they own it:
+       - Use the refund_course tool to process the refund
+       - Confirm the refund was successful
+       - Remind them the money will be returned to their original payment method
+       - If it's been more than 30 days, inform them that they are not eligible for a refund
+    3. If they don't own it:
+       - Inform them they don't own the course, so no refund is needed
+
+    Course Information:
+    - ai_marketing_platform: "Fullstack AI Marketing Platform" ($149)
+
+    Example Response for Purchase History:
+    "Here are your purchased courses:
+    1. Fullstack AI Marketing Platform
+       - Purchased on: 2024-04-21 10:30:00
+       - Full lifetime access"
+
+    Example Response for Refund:
+    "I've processed your refund for the Fullstack AI Marketing Platform course.
+    Your $149 will be returned to your original payment method within 3-5 business days.
+    The course has been removed from your account."
+
+    If they haven't purchased any courses:
+    - Let them know they don't have any courses yet
+    - Suggest talking to the sales agent about the AI Marketing Platform course
+
+    Remember:
+    - Be clear and professional
+    - Mention our 30-day money-back guarantee if relevant
+    - Direct course questions to course support
+    - Direct purchase inquiries to sales
+    """,
+    tools=[refund_course, get_current_time],
+)
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/policy_agent/__init__.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/policy_agent/__init__.py
new file mode 100644
index 000000000..24ca8c079
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/policy_agent/__init__.py
@@ -0,0 +1,3 @@
+from .agent import policy_agent
+
+__all__ = ["policy_agent"]
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/policy_agent/agent.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/policy_agent/agent.py
new file mode 100644
index 000000000..72dcc4551
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/policy_agent/agent.py
@@ -0,0 +1,60 @@
+from google.adk.agents import Agent
+
+# Create the policy agent
+policy_agent = Agent(
+    name="policy_agent",
+    model="gemini-2.0-flash",
+    description="Policy agent for the AI Developer Accelerator community",
+    instruction="""
+    You are the policy agent for the AI Developer Accelerator community. Your role is to help users
+    understand our community guidelines and policies.
+
+    <user_info>
+    Name: {user_name}
+    </user_info>
+
+    Community Guidelines:
+    1. Promotions
+       - No self-promotion or advertising
+       - Focus on learning and growing together
+       - Share your work only in designated channels
+
+    2. Content Quality
+       - Provide detailed, helpful responses
+       - Include code examples when relevant
+       - Use proper formatting for code snippets
+
+    3. Behavior
+       - Be respectful and professional
+       - No politics or religion discussions
+       - Help maintain a positive learning environment
+
+    Course Policies:
+    1. Refund Policy
+       - 30-day money-back guarantee
+       - Full refund if you complete the course and aren't satisfied
+       - No questions asked
+
+    2. Course Access
+       - Lifetime access to course content
+       - 6 weeks of group support included
+       - Weekly coaching calls every Sunday
+
+    3. Code Usage
+       - You can use course code in your projects
+       - Credit not required but appreciated
+       - No reselling of course materials
+
+    Privacy Policy:
+    - We respect your privacy
+    - Your data is never sold
+    - Course progress is tracked for support purposes
+
+    When responding:
+    1. Be clear and direct
+    2. Quote relevant policy sections
+    3. Explain the reasoning behind policies
+    4. Direct complex issues to support
+    """,
+    tools=[],
+)
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/sales_agent/__init__.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/sales_agent/__init__.py
new file mode 100644
index 000000000..aaace87dc
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/sales_agent/__init__.py
@@ -0,0 +1,3 @@
+from .agent import sales_agent
+
+__all__ = ["sales_agent"]
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/sales_agent/agent.py b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/sales_agent/agent.py
new file mode 100644
index 000000000..279a94f38
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/customer_service_agent/sub_agents/sales_agent/agent.py
@@ -0,0 +1,112 @@
+from datetime import datetime
+
+from google.adk.agents import Agent
+from google.adk.tools.tool_context import ToolContext
+
+
+def purchase_course(tool_context: ToolContext) -> dict:
+    """
+    Simulates purchasing the AI Marketing Platform course.
+    Updates state with purchase information.
+    """
+    course_id = "ai_marketing_platform"
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    # Get current purchased courses
+    current_purchased_courses = tool_context.state.get("purchased_courses", [])
+
+    # Check if user already owns the course
+    course_ids = [
+        course["id"] for course in current_purchased_courses if isinstance(course, dict)
+    ]
+    if course_id in course_ids:
+        return {"status": "error", "message": "You already own this course!"}
+
+    # Create new list with the course added
+    new_purchased_courses = []
+    # Only include valid dictionary courses
+    for course in current_purchased_courses:
+        if isinstance(course, dict) and "id" in course:
+            new_purchased_courses.append(course)
+
+    # Add the new course as a dictionary with id and purchase_date
+    new_purchased_courses.append({"id": course_id, "purchase_date": current_time})
+
+    # Update purchased courses in state via assignment
+    tool_context.state["purchased_courses"] = new_purchased_courses
+
+    # Get current interaction history
+    current_interaction_history = tool_context.state.get("interaction_history", [])
+
+    # Create new interaction history with purchase added
+    new_interaction_history = current_interaction_history.copy()
+    new_interaction_history.append(
+        {"action": "purchase_course", "course_id": course_id, "timestamp": current_time}
+    )
+
+    # Update interaction history in state via assignment
+    tool_context.state["interaction_history"] = new_interaction_history
+
+    return {
+        "status": "success",
+        "message": "Successfully purchased the AI Marketing Platform course!",
+        "course_id": course_id,
+        "timestamp": current_time,
+    }
+
+
+# Create the sales agent
+sales_agent = Agent(
+    name="sales_agent",
+    model="gemini-2.0-flash",
+    description="Sales agent for the AI Marketing Platform course",
+    instruction="""
+    You are a sales agent for the AI Developer Accelerator community, specifically handling sales
+    for the Fullstack AI Marketing Platform course.
+
+    <user_info>
+    Name: {user_name}
+    </user_info>
+
+    <purchase_info>
+    Purchased Courses: {purchased_courses}
+    </purchase_info>
+
+    <interaction_history>
+    {interaction_history}
+    </interaction_history>
+
+    Course Details:
+    - Name: Fullstack AI Marketing Platform
+    - Price: $149
+    - Value Proposition: Learn to build AI-powered marketing automation apps
+    - Includes: 6 weeks of group support with weekly coaching calls
+
+    When interacting with users:
+    1. Check if they already own the course (check purchased_courses above)
+       - Course information is stored as objects with "id" and "purchase_date" properties
+       - The course id is "ai_marketing_platform"
+    2. If they own it:
+       - Remind them they have access
+       - Ask if they need help with any specific part
+       - Direct them to course support for content questions
+    
+    3. If they don't own it:
+       - Explain the course value proposition
+       - Mention the price ($149)
+       - If they want to purchase:
+           - Use the purchase_course tool
+           - Confirm the purchase
+           - Ask if they'd like to start learning right away
+
+    4. After any interaction:
+       - The state will automatically track the interaction
+       - Be ready to hand off to course support after purchase
+
+    Remember:
+    - Be helpful but not pushy
+    - Focus on the value and practical skills they'll gain
+    - Emphasize the hands-on nature of building a real AI application
+    """,
+    tools=[purchase_course],
+)
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/main.py b/ADK-Crash-Course/8-stateful-multi-agent/main.py
new file mode 100644
index 000000000..e60ff52f3
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/main.py
@@ -0,0 +1,86 @@
+import asyncio
+
+# Import the main customer service agent
+from customer_service_agent.agent import customer_service_agent
+from dotenv import load_dotenv
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from utils import add_user_query_to_history, call_agent_async
+
+load_dotenv()
+
+# ===== PART 1: Initialize In-Memory Session Service =====
+# Using in-memory storage for this example (non-persistent)
+session_service = InMemorySessionService()
+
+
+# ===== PART 2: Define Initial State =====
+# This will be used when creating a new session
+initial_state = {
+    "user_name": "Brandon Hancock",
+    "purchased_courses": [],
+    "interaction_history": [],
+}
+
+
+async def main_async():
+    # Setup constants
+    APP_NAME = "Customer Support"
+    USER_ID = "aiwithbrandon"
+
+    # ===== PART 3: Session Creation =====
+    # Create a new session with initial state
+    new_session = session_service.create_session(
+        app_name=APP_NAME,
+        user_id=USER_ID,
+        state=initial_state,
+    )
+    SESSION_ID = new_session.id
+    print(f"Created new session: {SESSION_ID}")
+
+    # ===== PART 4: Agent Runner Setup =====
+    # Create a runner with the main customer service agent
+    runner = Runner(
+        agent=customer_service_agent,
+        app_name=APP_NAME,
+        session_service=session_service,
+    )
+
+    # ===== PART 5: Interactive Conversation Loop =====
+    print("\nWelcome to Customer Service Chat!")
+    print("Type 'exit' or 'quit' to end the conversation.\n")
+
+    while True:
+        # Get user input
+        user_input = input("You: ")
+
+        # Check if user wants to exit
+        if user_input.lower() in ["exit", "quit"]:
+            print("Ending conversation. Goodbye!")
+            break
+
+        # Update interaction history with the user's query
+        add_user_query_to_history(
+            session_service, APP_NAME, USER_ID, SESSION_ID, user_input
+        )
+
+        # Process the user query through the agent
+        await call_agent_async(runner, USER_ID, SESSION_ID, user_input)
+
+    # ===== PART 6: State Examination =====
+    # Show final session state
+    final_session = session_service.get_session(
+        app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
+    )
+    print("\nFinal Session State:")
+    for key, value in final_session.state.items():
+        print(f"{key}: {value}")
+
+
+def main():
+    """Entry point for the application."""
+    asyncio.run(main_async())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ADK-Crash-Course/8-stateful-multi-agent/utils.py b/ADK-Crash-Course/8-stateful-multi-agent/utils.py
new file mode 100644
index 000000000..8dcf7b99f
--- /dev/null
+++ b/ADK-Crash-Course/8-stateful-multi-agent/utils.py
@@ -0,0 +1,278 @@
+from datetime import datetime
+
+from google.genai import types
+
+
+# ANSI color codes for terminal output
+class Colors:
+    RESET = "\033[0m"
+    BOLD = "\033[1m"
+    UNDERLINE = "\033[4m"
+
+    # Foreground colors
+    BLACK = "\033[30m"
+    RED = "\033[31m"
+    GREEN = "\033[32m"
+    YELLOW = "\033[33m"
+    BLUE = "\033[34m"
+    MAGENTA = "\033[35m"
+    CYAN = "\033[36m"
+    WHITE = "\033[37m"
+
+    # Background colors
+    BG_BLACK = "\033[40m"
+    BG_RED = "\033[41m"
+    BG_GREEN = "\033[42m"
+    BG_YELLOW = "\033[43m"
+    BG_BLUE = "\033[44m"
+    BG_MAGENTA = "\033[45m"
+    BG_CYAN = "\033[46m"
+    BG_WHITE = "\033[47m"
+
+
+def update_interaction_history(session_service, app_name, user_id, session_id, entry):
+    """Add an entry to the interaction history in state.
+
+    Args:
+        session_service: The session service instance
+        app_name: The application name
+        user_id: The user ID
+        session_id: The session ID
+        entry: A dictionary containing the interaction data
+            - requires 'action' key (e.g., 'user_query', 'agent_response')
+            - other keys are flexible depending on the action type
+    """
+    try:
+        # Get current session
+        session = session_service.get_session(
+            app_name=app_name, user_id=user_id, session_id=session_id
+        )
+
+        # Get current interaction history
+        interaction_history = session.state.get("interaction_history", [])
+
+        # Add timestamp if not already present
+        if "timestamp" not in entry:
+            entry["timestamp"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        # Add the entry to interaction history
+        interaction_history.append(entry)
+
+        # Create updated state
+        updated_state = session.state.copy()
+        updated_state["interaction_history"] = interaction_history
+
+        # Create a new session with updated state
+        session_service.create_session(
+            app_name=app_name,
+            user_id=user_id,
+            session_id=session_id,
+            state=updated_state,
+        )
+    except Exception as e:
+        print(f"Error updating interaction history: {e}")
+
+
+def add_user_query_to_history(session_service, app_name, user_id, session_id, query):
+    """Add a user query to the interaction history."""
+    update_interaction_history(
+        session_service,
+        app_name,
+        user_id,
+        session_id,
+        {
+            "action": "user_query",
+            "query": query,
+        },
+    )
+
+
+def add_agent_response_to_history(
+    session_service, app_name, user_id, session_id, agent_name, response
+):
+    """Add an agent response to the interaction history."""
+    update_interaction_history(
+        session_service,
+        app_name,
+        user_id,
+        session_id,
+        {
+            "action": "agent_response",
+            "agent": agent_name,
+            "response": response,
+        },
+    )
+
+
+def display_state(
+    session_service, app_name, user_id, session_id, label="Current State"
+):
+    """Display the current session state in a formatted way."""
+    try:
+        session = session_service.get_session(
+            app_name=app_name, user_id=user_id, session_id=session_id
+        )
+
+        # Format the output with clear sections
+        print(f"\n{'-' * 10} {label} {'-' * 10}")
+
+        # Handle the user name
+        user_name = session.state.get("user_name", "Unknown")
+        print(f"👤 User: {user_name}")
+
+        # Handle purchased courses
+        purchased_courses = session.state.get("purchased_courses", [])
+        if purchased_courses and any(purchased_courses):
+            print("📚 Courses:")
+            for course in purchased_courses:
+                if isinstance(course, dict):
+                    course_id = course.get("id", "Unknown")
+                    purchase_date = course.get("purchase_date", "Unknown date")
+                    print(f"  - {course_id} (purchased on {purchase_date})")
+                elif course:  # Handle string format for backward compatibility
+                    print(f"  - {course}")
+        else:
+            print("📚 Courses: None")
+
+        # Handle interaction history in a more readable way
+        interaction_history = session.state.get("interaction_history", [])
+        if interaction_history:
+            print("📝 Interaction History:")
+            for idx, interaction in enumerate(interaction_history, 1):
+                # Pretty format dict entries, or just show strings
+                if isinstance(interaction, dict):
+                    action = interaction.get("action", "interaction")
+                    timestamp = interaction.get("timestamp", "unknown time")
+
+                    if action == "user_query":
+                        query = interaction.get("query", "")
+                        print(f'  {idx}. User query at {timestamp}: "{query}"')
+                    elif action == "agent_response":
+                        agent = interaction.get("agent", "unknown")
+                        response = interaction.get("response", "")
+                        # Truncate very long responses for display
+                        if len(response) > 100:
+                            response = response[:97] + "..."
+                        print(f'  {idx}. {agent} response at {timestamp}: "{response}"')
+                    else:
+                        details = ", ".join(
+                            f"{k}: {v}"
+                            for k, v in interaction.items()
+                            if k not in ["action", "timestamp"]
+                        )
+                        print(
+                            f"  {idx}. {action} at {timestamp}"
+                            + (f" ({details})" if details else "")
+                        )
+                else:
+                    print(f"  {idx}. {interaction}")
+        else:
+            print("📝 Interaction History: None")
+
+        # Show any additional state keys that might exist
+        other_keys = [
+            k
+            for k in session.state.keys()
+            if k not in ["user_name", "purchased_courses", "interaction_history"]
+        ]
+        if other_keys:
+            print("🔑 Additional State:")
+            for key in other_keys:
+                print(f"  {key}: {session.state[key]}")
+
+        print("-" * (22 + len(label)))
+    except Exception as e:
+        print(f"Error displaying state: {e}")
+
+
+async def process_agent_response(event):
+    """Process and display agent response events."""
+    print(f"Event ID: {event.id}, Author: {event.author}")
+
+    # Check for specific parts first
+    has_specific_part = False
+    if event.content and event.content.parts:
+        for part in event.content.parts:
+            if hasattr(part, "text") and part.text and not part.text.isspace():
+                print(f"  Text: '{part.text.strip()}'")
+
+    # Check for final response after specific parts
+    final_response = None
+    if not has_specific_part and event.is_final_response():
+        if (
+            event.content
+            and event.content.parts
+            and hasattr(event.content.parts[0], "text")
+            and event.content.parts[0].text
+        ):
+            final_response = event.content.parts[0].text.strip()
+            # Use colors and formatting to make the final response stand out
+            print(
+                f"\n{Colors.BG_BLUE}{Colors.WHITE}{Colors.BOLD}╔══ AGENT RESPONSE ═════════════════════════════════════════{Colors.RESET}"
+            )
+            print(f"{Colors.CYAN}{Colors.BOLD}{final_response}{Colors.RESET}")
+            print(
+                f"{Colors.BG_BLUE}{Colors.WHITE}{Colors.BOLD}╚═════════════════════════════════════════════════════════════{Colors.RESET}\n"
+            )
+        else:
+            print(
+                f"\n{Colors.BG_RED}{Colors.WHITE}{Colors.BOLD}==> Final Agent Response: [No text content in final event]{Colors.RESET}\n"
+            )
+
+    return final_response
+
+
+async def call_agent_async(runner, user_id, session_id, query):
+    """Call the agent asynchronously with the user's query."""
+    content = types.Content(role="user", parts=[types.Part(text=query)])
+    print(
+        f"\n{Colors.BG_GREEN}{Colors.BLACK}{Colors.BOLD}--- Running Query: {query} ---{Colors.RESET}"
+    )
+    final_response_text = None
+    agent_name = None
+
+    # Display state before processing the message
+    display_state(
+        runner.session_service,
+        runner.app_name,
+        user_id,
+        session_id,
+        "State BEFORE processing",
+    )
+
+    try:
+        async for event in runner.run_async(
+            user_id=user_id, session_id=session_id, new_message=content
+        ):
+            # Capture the agent name from the event if available
+            if event.author:
+                agent_name = event.author
+
+            response = await process_agent_response(event)
+            if response:
+                final_response_text = response
+    except Exception as e:
+        print(f"{Colors.BG_RED}{Colors.WHITE}ERROR during agent run: {e}{Colors.RESET}")
+
+    # Add the agent response to interaction history if we got a final response
+    if final_response_text and agent_name:
+        add_agent_response_to_history(
+            runner.session_service,
+            runner.app_name,
+            user_id,
+            session_id,
+            agent_name,
+            final_response_text,
+        )
+
+    # Display state after processing the message
+    display_state(
+        runner.session_service,
+        runner.app_name,
+        user_id,
+        session_id,
+        "State AFTER processing",
+    )
+
+    print(f"{Colors.YELLOW}{'-' * 30}{Colors.RESET}")
+    return final_response_text
diff --git a/ADK-Crash-Course/9-callbacks/README.md b/ADK-Crash-Course/9-callbacks/README.md
new file mode 100644
index 000000000..b7fbc0e38
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/README.md
@@ -0,0 +1,332 @@
+# Callbacks in ADK
+
+This example demonstrates how to use callbacks in the Agent Development Kit (ADK) to intercept and modify agent behavior at different stages of execution. Callbacks provide powerful hooks into the agent's lifecycle, allowing you to add custom logic for monitoring, logging, content filtering, and result transformation.
+
+## What are Callbacks in ADK?
+
+Callbacks are functions that execute at specific points in an agent's execution flow. They allow you to:
+
+1. **Monitor and Log**: Track agent activity and performance metrics
+2. **Filter Content**: Block inappropriate requests or responses
+3. **Transform Data**: Modify inputs and outputs in the agent workflow
+4. **Implement Security Policies**: Enforce compliance and safety measures
+5. **Add Custom Logic**: Insert business-specific processing into the agent flow
+
+ADK provides several types of callbacks that can be attached to different components of your agent system.
+
+## Callback Parameters and Context
+
+Each type of callback provides access to specific context objects that contain valuable information about the current execution state. Understanding these parameters is key to building effective callbacks.
+
+### CallbackContext
+
+The `CallbackContext` object is provided to all callback types and contains:
+
+- **`agent_name`**: The name of the agent being executed
+- **`invocation_id`**: A unique identifier for the current agent invocation
+- **`state`**: Access to the session state, allowing you to read/write persistent data
+- **`app_name`**: The name of the application
+- **`user_id`**: The ID of the current user
+- **`session_id`**: The ID of the current session
+
+Example usage:
+```python
+def my_callback(callback_context: CallbackContext, ...):
+    # Access the state to store or retrieve data
+    user_name = callback_context.state.get("user_name", "Unknown")
+    
+    # Log the current agent and invocation
+    print(f"Agent {callback_context.agent_name} executing (ID: {callback_context.invocation_id})")
+```
+
+### ToolContext (for Tool Callbacks)
+
+The `ToolContext` object is provided to tool callbacks and contains:
+
+- **`agent_name`**: The name of the agent that initiated the tool call
+- **`state`**: Access to the session state, allowing tools to read/modify shared data
+- **`properties`**: Additional properties specific to the tool execution
+
+Example usage:
+```python
+def before_tool_callback(tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext):
+    # Record tool usage in state
+    tools_used = tool_context.state.get("tools_used", [])
+    tools_used.append(tool.name)
+    tool_context.state["tools_used"] = tools_used
+```
+
+### LlmRequest (for Model Callbacks)
+
+The `LlmRequest` object is provided to the before_model_callback and contains:
+
+- **`contents`**: List of Content objects representing the conversation history
+- **`generation_config`**: Configuration for the model generation
+- **`safety_settings`**: Safety settings for the model
+- **`tools`**: Tools provided to the model
+
+Example usage:
+```python
+def before_model_callback(callback_context: CallbackContext, llm_request: LlmRequest):
+    # Get the last user message for analysis
+    last_message = None
+    for content in reversed(llm_request.contents):
+        if content.role == "user" and content.parts:
+            last_message = content.parts[0].text
+            break
+            
+    # Analyze the user's message
+    if last_message and contains_sensitive_info(last_message):
+        # Return a response that bypasses the model call
+        return LlmResponse(...)
+```
+
+### LlmResponse (for Model Callbacks)
+
+The `LlmResponse` object is returned from the model and provided to the after_model_callback:
+
+- **`content`**: Content object containing the model's response
+- **`tool_calls`**: Any tool calls the model wants to make
+- **`usage_metadata`**: Metadata about the model usage (tokens, etc.)
+
+Example usage:
+```python
+def after_model_callback(callback_context: CallbackContext, llm_response: LlmResponse):
+    # Access the model's text response
+    if llm_response.content and llm_response.content.parts:
+        response_text = llm_response.content.parts[0].text
+        
+        # Modify the response
+        modified_text = transform_text(response_text)
+        llm_response.content.parts[0].text = modified_text
+        
+        return llm_response
+```
+
+## Types of Callbacks Demonstrated
+
+This project includes three examples of callback patterns:
+
+### 1. Agent Callbacks (`before_after_agent/`)
+- **Before Agent Callback**: Runs at the start of agent processing
+- **After Agent Callback**: Runs after the agent completes processing
+
+### 2. Model Callbacks (`before_after_model/`)
+- **Before Model Callback**: Intercepts requests before they reach the LLM
+- **After Model Callback**: Modifies responses after they come from the LLM
+
+### 3. Tool Callbacks (`before_after_tool/`)
+- **Before Tool Callback**: Modifies tool arguments or skips tool execution
+- **After Tool Callback**: Enhances tool responses with additional information
+
+## Project Structure
+
+```
+8-callbacks/
+│
+├── before_after_agent/           # Agent callback example
+│   ├── __init__.py               # Required for ADK discovery
+│   ├── agent.py                  # Agent with agent callbacks
+│   └── .env                      # Environment variables
+│
+├── before_after_model/           # Model callback example
+│   ├── __init__.py               # Required for ADK discovery
+│   ├── agent.py                  # Agent with model callbacks
+│   └── .env                      # Environment variables
+│
+├── before_after_tool/            # Tool callback example
+│   ├── __init__.py               # Required for ADK discovery
+│   ├── agent.py                  # Agent with tool callbacks
+│   └── .env                      # Environment variables
+│
+└── README.md                     # This documentation
+```
+
+## Example 1: Agent Callbacks
+
+The agent callbacks example demonstrates:
+
+1. **Request Logging**: Recording when requests start and finish
+2. **Performance Monitoring**: Measuring request duration
+3. **State Management**: Using session state to track request counts
+
+### Key Implementation Details
+
+```python
+def before_agent_callback(callback_context: CallbackContext) -> Optional[types.Content]:
+    # Get the session state
+    state = callback_context.state
+    
+    # Initialize request counter
+    if "request_counter" not in state:
+        state["request_counter"] = 1
+    else:
+        state["request_counter"] += 1
+        
+    # Store start time for duration calculation
+    state["request_start_time"] = datetime.now()
+    
+    # Log the request
+    logger.info("=== AGENT EXECUTION STARTED ===")
+    
+    return None  # Continue with normal agent processing
+
+def after_agent_callback(callback_context: CallbackContext) -> Optional[types.Content]:
+    # Get the session state
+    state = callback_context.state
+    
+    # Calculate request duration
+    duration = None
+    if "request_start_time" in state:
+        duration = (datetime.now() - state["request_start_time"]).total_seconds()
+        
+    # Log the completion
+    logger.info("=== AGENT EXECUTION COMPLETED ===")
+    
+    return None  # Continue with normal agent processing
+```
+
+### Testing Agent Callbacks
+
+Any interaction will demonstrate the agent callbacks, which log requests and measure duration.
+
+## Example 2: Model Callbacks
+
+The model callbacks example demonstrates:
+
+1. **Content Filtering**: Blocking inappropriate content before it reaches the model
+2. **Response Transformation**: Replacing negative words with more positive alternatives
+
+### Key Implementation Details
+
+```python
+def before_model_callback(
+    callback_context: CallbackContext, llm_request: LlmRequest
+) -> Optional[LlmResponse]:
+    # Check for inappropriate content
+    if last_user_message and "sucks" in last_user_message.lower():
+        # Return a response to skip the model call
+        return LlmResponse(
+            content=types.Content(
+                role="model",
+                parts=[
+                    types.Part(
+                        text="I cannot respond to messages containing inappropriate language..."
+                    )
+                ],
+            )
+        )
+    # Return None to proceed with normal model request
+    return None
+
+def after_model_callback(
+    callback_context: CallbackContext, llm_response: LlmResponse
+) -> Optional[LlmResponse]:
+    # Simple word replacements
+    replacements = {
+        "problem": "challenge",
+        "difficult": "complex",
+    }
+    # Perform replacements and return modified response
+```
+
+### Testing Model Callbacks
+
+To test content filtering in the before_model_callback:
+- "This website sucks, can you help me fix it?"
+- "Everything about this project sucks."
+
+To test word replacement in the after_model_callback:
+- "What's the biggest problem with machine learning today?"
+- "Why is debugging so difficult in complex systems?"
+- "I have a problem with my code that's very difficult to solve."
+
+## Example 3: Tool Callbacks
+
+The tool callbacks example demonstrates:
+
+1. **Argument Modification**: Transforming input arguments before tool execution
+2. **Request Blocking**: Preventing certain tool calls completely
+3. **Response Enhancement**: Adding additional context to tool responses
+4. **Error Handling**: Improving error messages for better user experience
+
+### Key Implementation Details
+
+```python
+def before_tool_callback(
+    tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext
+) -> Optional[Dict]:
+    # Modify arguments (e.g., convert "USA" to "United States")
+    if args.get("country", "").lower() == "merica":
+        args["country"] = "United States"
+        return None
+        
+    # Skip the call completely for restricted countries
+    if args.get("country", "").lower() == "restricted":
+        return {"result": "Access to this information has been restricted."}
+    
+    return None  # Proceed with normal tool call
+
+def after_tool_callback(
+    tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext, tool_response: Dict
+) -> Optional[Dict]:
+    # Add a note for any USA capital responses
+    if "washington" in tool_response.get("result", "").lower():
+        modified_response = copy.deepcopy(tool_response)
+        modified_response["result"] = f"{tool_response['result']} (Note: This is the capital of the USA. 🇺🇸)"
+        return modified_response
+        
+    return None  # Use original response
+```
+
+### Testing Tool Callbacks
+
+To test argument modification:
+- "What is the capital of USA?" (converts to "United States")
+- "What is the capital of Merica?" (converts to "United States")
+
+To test request blocking:
+- "What is the capital of restricted?" (blocks the request)
+
+To test response enhancement:
+- "What is the capital of the United States?" (adds a patriotic note)
+
+To see normal operation:
+- "What is the capital of France?" (no modifications)
+
+## Running the Examples
+
+### Setup
+
+1. Activate the virtual environment from the root directory:
+```bash
+# macOS/Linux:
+source ../.venv/bin/activate
+# Windows CMD:
+..\.venv\Scripts\activate.bat
+# Windows PowerShell:
+..\.venv\Scripts\Activate.ps1
+```
+
+2. Create a `.env` file in each agent directory (`before_after_agent/`, `before_after_model/`, and `before_after_tool/`) based on the provided `.env.example` files:
+```
+GOOGLE_API_KEY=your_api_key_here
+```
+
+### Running the Examples
+
+```bash
+cd 8-callbacks
+adk web
+```
+
+Then select the agent you want to test from the dropdown menu in the web UI:
+- "before_after_agent" to test agent callbacks
+- "before_after_model" to test model callbacks
+- "before_after_tool" to test tool callbacks
+
+## Additional Resources
+
+- [ADK Callbacks Documentation](https://google.github.io/adk-docs/callbacks/)
+- [Types of Callbacks](https://google.github.io/adk-docs/callbacks/types-of-callbacks/)
+- [Design Patterns and Best Practices](https://google.github.io/adk-docs/callbacks/design-patterns-and-best-practices/)
diff --git a/ADK-Crash-Course/9-callbacks/before_after_agent/.env.example b/ADK-Crash-Course/9-callbacks/before_after_agent/.env.example
new file mode 100644
index 000000000..9b0ce4e74
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_agent/.env.example
@@ -0,0 +1,2 @@
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=your_api_key_here 
diff --git a/ADK-Crash-Course/9-callbacks/before_after_agent/__init__.py b/ADK-Crash-Course/9-callbacks/before_after_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/9-callbacks/before_after_agent/agent.py b/ADK-Crash-Course/9-callbacks/before_after_agent/agent.py
new file mode 100644
index 000000000..464b3d8c7
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_agent/agent.py
@@ -0,0 +1,106 @@
+"""
+Before and After Agent Callbacks Example
+
+This example demonstrates how to use both before_agent_callback and after_agent_callback 
+for logging purposes.
+"""
+
+from datetime import datetime
+from typing import Optional
+
+from google.adk.agents import LlmAgent
+from google.adk.agents.callback_context import CallbackContext
+from google.genai import types
+
+
+def before_agent_callback(callback_context: CallbackContext) -> Optional[types.Content]:
+    """
+    Simple callback that logs when the agent starts processing a request.
+
+    Args:
+        callback_context: Contains state and context information
+
+    Returns:
+        None to continue with normal agent processing
+    """
+    # Get the session state
+    state = callback_context.state
+
+    # Record timestamp
+    timestamp = datetime.now()
+
+    # Set agent name if not present
+    if "agent_name" not in state:
+        state["agent_name"] = "SimpleChatBot"
+
+    # Initialize request counter
+    if "request_counter" not in state:
+        state["request_counter"] = 1
+    else:
+        state["request_counter"] += 1
+
+    # Store start time for duration calculation in after_agent_callback
+    state["request_start_time"] = timestamp
+
+    # Log the request
+    print("=== AGENT EXECUTION STARTED ===")
+    print(f"Request #: {state['request_counter']}")
+    print(f"Timestamp: {timestamp.strftime('%Y-%m-%d %H:%M:%S')}")
+
+    # Print to console
+    print(f"\n[BEFORE CALLBACK] Agent processing request #{state['request_counter']}")
+
+    return None
+
+
+def after_agent_callback(callback_context: CallbackContext) -> Optional[types.Content]:
+    """
+    Simple callback that logs when the agent finishes processing a request.
+
+    Args:
+        callback_context: Contains state and context information
+
+    Returns:
+        None to continue with normal agent processing
+    """
+    # Get the session state
+    state = callback_context.state
+
+    # Calculate request duration if start time is available
+    timestamp = datetime.now()
+    duration = None
+    if "request_start_time" in state:
+        duration = (timestamp - state["request_start_time"]).total_seconds()
+
+    # Log the completion
+    print("=== AGENT EXECUTION COMPLETED ===")
+    print(f"Request #: {state.get('request_counter', 'Unknown')}")
+    if duration is not None:
+        print(f"Duration: {duration:.2f} seconds")
+
+    # Print to console
+    print(
+        f"[AFTER CALLBACK] Agent completed request #{state.get('request_counter', 'Unknown')}"
+    )
+    if duration is not None:
+        print(f"[AFTER CALLBACK] Processing took {duration:.2f} seconds")
+
+    return None
+
+
+# Create the Agent
+root_agent = LlmAgent(
+    name="before_after_agent",
+    model="gemini-2.0-flash",
+    description="A basic agent that demonstrates before and after agent callbacks",
+    instruction="""
+    You are a friendly greeting agent. Your name is {agent_name}.
+    
+    Your job is to:
+    - Greet users politely
+    - Respond to basic questions
+    - Keep your responses friendly and concise
+    """,
+    before_agent_callback=before_agent_callback,
+    after_agent_callback=after_agent_callback,
+)
diff --git a/ADK-Crash-Course/9-callbacks/before_after_model/.env.example b/ADK-Crash-Course/9-callbacks/before_after_model/.env.example
new file mode 100644
index 000000000..9b0ce4e74
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_model/.env.example
@@ -0,0 +1,2 @@
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=your_api_key_here 
diff --git a/ADK-Crash-Course/9-callbacks/before_after_model/__init__.py b/ADK-Crash-Course/9-callbacks/before_after_model/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_model/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/9-callbacks/before_after_model/agent.py b/ADK-Crash-Course/9-callbacks/before_after_model/agent.py
new file mode 100644
index 000000000..028d9216d
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_model/agent.py
@@ -0,0 +1,162 @@
+"""
+Before and After Model Callbacks Example
+
+This example demonstrates using model callbacks 
+to filter content and log model interactions.
+"""
+
+import copy
+from datetime import datetime
+from typing import Optional
+
+from google.adk.agents import LlmAgent
+from google.adk.agents.callback_context import CallbackContext
+from google.adk.models import LlmRequest, LlmResponse
+from google.genai import types
+
+
+def before_model_callback(
+    callback_context: CallbackContext, llm_request: LlmRequest
+) -> Optional[LlmResponse]:
+    """
+    This callback runs before the model processes a request.
+    It filters inappropriate content and logs request info.
+
+    Args:
+        callback_context: Contains state and context information
+        llm_request: The LLM request being sent
+
+    Returns:
+        Optional LlmResponse to override model response
+    """
+    # Get the state and agent name
+    state = callback_context.state
+    agent_name = callback_context.agent_name
+
+    # Extract the last user message
+    last_user_message = ""
+    if llm_request.contents and len(llm_request.contents) > 0:
+        for content in reversed(llm_request.contents):
+            if content.role == "user" and content.parts and len(content.parts) > 0:
+                if hasattr(content.parts[0], "text") and content.parts[0].text:
+                    last_user_message = content.parts[0].text
+                    break
+
+    # Log the request
+    print("=== MODEL REQUEST STARTED ===")
+    print(f"Agent: {agent_name}")
+    if last_user_message:
+        print(f"User message: {last_user_message[:100]}...")
+        # Store for later use
+        state["last_user_message"] = last_user_message
+    else:
+        print("User message: <empty>")
+
+    print(f"Timestamp: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+
+    # Check for inappropriate content
+    if last_user_message and "sucks" in last_user_message.lower():
+        print("=== INAPPROPRIATE CONTENT BLOCKED ===")
+        print("Blocked text containing prohibited word: 'sucks'")
+
+        print("[BEFORE MODEL] ⚠️ Request blocked due to inappropriate content")
+
+        # Return a response to skip the model call
+        return LlmResponse(
+            content=types.Content(
+                role="model",
+                parts=[
+                    types.Part(
+                        text="I cannot respond to messages containing inappropriate language. "
+                        "Please rephrase your request without using words like 'sucks'."
+                    )
+                ],
+            )
+        )
+
+    # Record start time for duration calculation
+    state["model_start_time"] = datetime.now()
+    print("[BEFORE MODEL] ✓ Request approved for processing")
+
+    # Return None to proceed with normal model request
+    return None
+
+
+def after_model_callback(
+    callback_context: CallbackContext, llm_response: LlmResponse
+) -> Optional[LlmResponse]:
+    """
+    Simple callback that replaces negative words with more positive alternatives.
+
+    Args:
+        callback_context: Contains state and context information
+        llm_response: The LLM response received
+
+    Returns:
+        Optional LlmResponse to override model response
+    """
+    # Log completion
+    print("[AFTER MODEL] Processing response")
+
+    # Skip processing if response is empty or has no text content
+    if not llm_response or not llm_response.content or not llm_response.content.parts:
+        return None
+
+    # Extract text from the response
+    response_text = ""
+    for part in llm_response.content.parts:
+        if hasattr(part, "text") and part.text:
+            response_text += part.text
+
+    if not response_text:
+        return None
+
+    # Simple word replacements
+    replacements = {
+        "problem": "challenge",
+        "difficult": "complex",
+    }
+
+    # Perform replacements
+    modified_text = response_text
+    modified = False
+
+    for original, replacement in replacements.items():
+        if original in modified_text.lower():
+            modified_text = modified_text.replace(original, replacement)
+            modified_text = modified_text.replace(
+                original.capitalize(), replacement.capitalize()
+            )
+            modified = True
+
+    # Return modified response if changes were made
+    if modified:
+        print("[AFTER MODEL] ↺ Modified response text")
+
+        modified_parts = [copy.deepcopy(part) for part in llm_response.content.parts]
+        for i, part in enumerate(modified_parts):
+            if hasattr(part, "text") and part.text:
+                modified_parts[i].text = modified_text
+
+        return LlmResponse(content=types.Content(role="model", parts=modified_parts))
+
+    # Return None to use the original response
+    return None
+
+
+# Create the Agent
+root_agent = LlmAgent(
+    name="content_filter_agent",
+    model="gemini-2.0-flash",
+    description="An agent that demonstrates model callbacks for content filtering and logging",
+    instruction="""
+    You are a helpful assistant.
+    
+    Your job is to:
+    - Answer user questions concisely
+    - Provide factual information
+    - Be friendly and respectful
+    """,
+    before_model_callback=before_model_callback,
+    after_model_callback=after_model_callback,
+)
diff --git a/ADK-Crash-Course/9-callbacks/before_after_tool/.env.example b/ADK-Crash-Course/9-callbacks/before_after_tool/.env.example
new file mode 100644
index 000000000..9b0ce4e74
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_tool/.env.example
@@ -0,0 +1,2 @@
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=your_api_key_here 
diff --git a/ADK-Crash-Course/9-callbacks/before_after_tool/__init__.py b/ADK-Crash-Course/9-callbacks/before_after_tool/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_tool/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/ADK-Crash-Course/9-callbacks/before_after_tool/agent.py b/ADK-Crash-Course/9-callbacks/before_after_tool/agent.py
new file mode 100644
index 000000000..a830374b4
--- /dev/null
+++ b/ADK-Crash-Course/9-callbacks/before_after_tool/agent.py
@@ -0,0 +1,132 @@
+"""
+Before and After Tool Callbacks Example
+
+This example demonstrates using tool callbacks to modify tool behavior.
+"""
+
+import copy
+from typing import Any, Dict, Optional
+
+from google.adk.agents import LlmAgent
+from google.adk.tools.base_tool import BaseTool
+from google.adk.tools.tool_context import ToolContext
+
+
+# --- Define a Simple Tool Function ---
+def get_capital_city(country: str) -> Dict[str, str]:
+    """
+    Retrieves the capital city of a given country.
+
+    Args:
+        country: Name of the country
+
+    Returns:
+        Dictionary with the capital city result
+    """
+    print(f"[TOOL] Executing get_capital_city tool with country: {country}")
+
+    country_capitals = {
+        "united states": "Washington, D.C.",
+        "usa": "Washington, D.C.",
+        "canada": "Ottawa",
+        "france": "Paris",
+        "germany": "Berlin",
+        "japan": "Tokyo",
+        "brazil": "Brasília",
+        "australia": "Canberra",
+        "india": "New Delhi",
+    }
+
+    # Use lowercase for comparison
+    result = country_capitals.get(country.lower(), f"Capital not found for {country}")
+    print(f"[TOOL] Result: {result}")
+    print(f"[TOOL] Returning: {{'result': '{result}'}}")
+
+    return {"result": result}
+
+
+# --- Define Before Tool Callback ---
+def before_tool_callback(
+    tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext
+) -> Optional[Dict]:
+    """
+    Simple callback that modifies tool arguments or skips the tool call.
+    """
+    tool_name = tool.name
+    print(f"[Callback] Before tool call for '{tool_name}'")
+    print(f"[Callback] Original args: {args}")
+
+    # If someone asks about 'Merica, convert to United States
+    if tool_name == "get_capital_city" and args.get("country", "").lower() == "merica":
+        print("[Callback] Converting 'Merica to 'United States'")
+        args["country"] = "United States"
+        print(f"[Callback] Modified args: {args}")
+        return None
+
+    # Skip the call completely for restricted countries
+    if (
+        tool_name == "get_capital_city"
+        and args.get("country", "").lower() == "restricted"
+    ):
+        print("[Callback] Blocking restricted country")
+        return {"result": "Access to this information has been restricted."}
+
+    print("[Callback] Proceeding with normal tool call")
+    return None
+
+
+# --- Define After Tool Callback ---
+def after_tool_callback(
+    tool: BaseTool, args: Dict[str, Any], tool_context: ToolContext, tool_response: Dict
+) -> Optional[Dict]:
+    """
+    Simple callback that modifies the tool response after execution.
+    """
+    tool_name = tool.name
+    print(f"[Callback] After tool call for '{tool_name}'")
+    print(f"[Callback] Args used: {args}")
+    print(f"[Callback] Original response: {tool_response}")
+
+    original_result = tool_response.get("result", "")
+    print(f"[Callback] Extracted result: '{original_result}'")
+
+    # Add a note for any USA capital responses
+    if tool_name == "get_capital_city" and "washington" in original_result.lower():
+        print("[Callback] DETECTED USA CAPITAL - adding patriotic note!")
+
+        # Create a modified copy of the response
+        modified_response = copy.deepcopy(tool_response)
+        modified_response["result"] = (
+            f"{original_result} (Note: This is the capital of the USA. 🇺🇸)"
+        )
+        modified_response["note_added_by_callback"] = True
+
+        print(f"[Callback] Modified response: {modified_response}")
+        return modified_response
+
+    print("[Callback] No modifications needed, returning original response")
+    return None
+
+
+# Create the Agent
+root_agent = LlmAgent(
+    name="tool_callback_agent",
+    model="gemini-2.0-flash",
+    description="An agent that demonstrates tool callbacks by looking up capital cities",
+    instruction="""
+    You are a helpful geography assistant.
+    
+    Your job is to:
+    - Find capital cities when asked using the get_capital_city tool
+    - Use the exact country name provided by the user
+    - ALWAYS return the EXACT result from the tool, without changing it
+    - When reporting a capital, display it EXACTLY as returned by the tool
+    
+    Examples:
+    - "What is the capital of France?" → Use get_capital_city with country="France"
+    - "Tell me the capital city of Japan" → Use get_capital_city with country="Japan"
+    """,
+    tools=[get_capital_city],
+    before_tool_callback=before_tool_callback,
+    after_tool_callback=after_tool_callback,
+)
diff --git a/ADK-Crash-Course/GEMINI_MODELS.md b/ADK-Crash-Course/GEMINI_MODELS.md
new file mode 100644
index 000000000..9c11a4973
--- /dev/null
+++ b/ADK-Crash-Course/GEMINI_MODELS.md
@@ -0,0 +1,44 @@
+# Gemini Model Overview for ADK
+
+ADK supports several Gemini models with different capabilities and price points. Choosing the right model involves balancing performance, capabilities, and cost for your specific use case.
+
+## Model Capabilities
+
+| Model | Description | Input Types | Best For |
+|-------|-------------|-------------|----------|
+| gemini-2.5-pro | Most powerful thinking model with maximum response accuracy | Audio, images, video, text | Complex coding, reasoning, multimodal understanding |
+| gemini-2.5-flash | Best price-performance balance | Audio, images, video, text | Low latency, high volume tasks that require thinking |
+| gemini-2.0-flash | Newest multimodal model with improved capabilities | Audio, images, video, text | Low latency, enhanced performance, agentic experiences |
+| gemini-2.0-flash-lite | Optimized for efficiency and speed | Audio, images, video, text | Cost efficiency and low latency |
+| gemini-1.5-flash | Versatile performance across diverse tasks | Audio, images, video, text | Fast and versatile performance |
+| gemini-1.5-flash-8b | Smaller, faster model | Audio, images, video, text | High volume and lower intelligence tasks |
+| gemini-1.5-pro | Powerful reasoning capabilities | Audio, images, video, text | Complex reasoning tasks requiring more intelligence |
+
+## Pricing
+
+| Model | Input Price | Output Price |
+|-------|-------------|-------------|
+| gemini-2.5-pro | $10.00 / 1M tokens | $30.00 / 1M tokens |
+| gemini-2.5-flash | $3.50 / 1M tokens | $10.50 / 1M tokens |
+| gemini-2.0-flash | $3.50 / 1M tokens | $10.50 / 1M tokens |
+| gemini-2.0-flash-lite | $0.70 / 1M tokens | $2.10 / 1M tokens |
+| gemini-1.5-flash | $2.50 / 1M tokens | $7.50 / 1M tokens |
+| gemini-1.5-flash-8b | $0.35 / 1M tokens | $1.05 / 1M tokens |
+| gemini-1.5-pro | $7.00 / 1M tokens | $21.00 / 1M tokens |
+
+## Token Information
+
+- A token is approximately 4 characters
+- 100 tokens are roughly 60-80 English words
+- Pricing is calculated based on both input tokens (prompts sent to the model) and output tokens (responses generated by the model)
+
+## Model Selection Guidelines
+
+1. **For budget-conscious applications:** Start with gemini-2.0-flash-lite
+2. **For balanced performance and cost:** Use gemini-2.0-flash or gemini-2.5-flash
+3. **For complex reasoning tasks:** Choose gemini-2.5-pro
+4. **For production applications:** Prefer stable models over experimental/preview versions
+
+## Additional Resources
+
+For the most up-to-date information on Gemini models, visit the [official Gemini API documentation](https://ai.google.dev/gemini-api/docs/models).
diff --git a/ADK-Crash-Course/README.md b/ADK-Crash-Course/README.md
new file mode 100644
index 000000000..dc03edf4c
--- /dev/null
+++ b/ADK-Crash-Course/README.md
@@ -0,0 +1,103 @@
+# Agent Development Kit (ADK) Crash Course
+
+This repository contains examples for learning Google's Agent Development Kit (ADK), a powerful framework for building LLM-powered agents.
+
+## Getting Started
+
+### Setup Environment
+
+You only need to create one virtual environment for all examples in this course. Follow these steps to set it up:
+
+```bash
+# Create virtual environment in the root directory
+python -m venv .venv
+
+# Activate (each new terminal)
+# macOS/Linux:
+source .venv/bin/activate
+# Windows CMD:
+.venv\Scripts\activate.bat
+# Windows PowerShell:
+.venv\Scripts\Activate.ps1
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+Once set up, this single environment will work for all examples in the repository.
+
+### Setting Up API Keys
+
+1. Create an account in Google Cloud https://cloud.google.com/?hl=en
+2. Create a new project
+3. Go to https://aistudio.google.com/apikey
+4. Create an API key
+5. Assign key to the project
+6. Connect to a billing account
+
+Each example folder contains a `.env.example` file. For each project you want to run:
+
+1. Navigate to the example folder
+2. Rename `.env.example` to `.env` 
+3. Open the `.env` file and replace the placeholder with your API key:
+   ```
+   GOOGLE_API_KEY=your_api_key_here
+   ```
+
+You'll need to repeat this for each example project you want to run.
+
+## Examples Overview
+
+Here's what you can learn from each example folder:
+
+### 1. Basic Agent
+Introduction to the simplest form of ADK agents. Learn how to create a basic agent that can respond to user queries.
+
+### 2. Tool Agent
+Learn how to enhance agents with tools that allow them to perform actions beyond just generating text.
+
+### 3. LiteLLM Agent
+Example of using LiteLLM to abstract away LLM provider details and easily switch between different models.
+
+### 4. Structured Outputs
+Learn how to use Pydantic models with `output_schema` to ensure consistent, structured responses from your agents.
+
+### 5. Sessions and State
+Understand how to maintain state and memory across multiple interactions using sessions.
+
+### 6. Persistent Storage
+Learn techniques for storing agent data persistently across sessions and application restarts.
+
+### 7. Multi-Agent
+See how to orchestrate multiple specialized agents working together to solve complex tasks.
+
+### 8. Stateful Multi-Agent
+Build agents that maintain and update state throughout complex multi-turn conversations.
+
+### 9. Callbacks
+Implement event callbacks to monitor and respond to agent behaviors in real-time.
+
+### 10. Sequential Agent
+Create pipeline workflows where agents operate in a defined sequence to process information.
+
+### 11. Parallel Agent
+Leverage concurrent operations with parallel agents for improved efficiency and performance.
+
+### 12. Loop Agent
+Build sophisticated agents that can iteratively refine their outputs through feedback loops.
+
+## Official Documentation
+
+For more detailed information, check out the official ADK documentation:
+- https://google.github.io/adk-docs/get-started/quickstart
+
+## Support
+
+Need help or run into issues? Join our free AI Developer Accelerator community on Skool:
+- [AI Developer Accelerator Community](https://www.skool.com/ai-developer-accelerator/about)
+
+In the community you'll find:
+- Weekly coaching and support calls
+- Early access to code from YouTube projects
+- A network of AI developers of all skill levels ready to help
+- Behind-the-scenes looks at how these apps are built
diff --git a/ADK-Crash-Course/requirements.txt b/ADK-Crash-Course/requirements.txt
new file mode 100644
index 000000000..ba9044959
--- /dev/null
+++ b/ADK-Crash-Course/requirements.txt
@@ -0,0 +1,6 @@
+google-adk[database]==0.3.0
+yfinance==0.2.56
+psutil==5.9.5
+litellm==1.66.3
+google-generativeai==0.8.5
+python-dotenv==1.1.0
diff --git a/adk-docs b/adk-docs
new file mode 160000
index 000000000..9e071d4a6
--- /dev/null
+++ b/adk-docs
@@ -0,0 +1 @@
+Subproject commit 9e071d4a6333eaa231e1d0af285e6ea137756d97
diff --git a/adk-python-custom-streaming-test2-multimodal-agents.zip - Shortcut.lnk b/adk-python-custom-streaming-test2-multimodal-agents.zip - Shortcut.lnk
new file mode 100644
index 000000000..d57684965
Binary files /dev/null and b/adk-python-custom-streaming-test2-multimodal-agents.zip - Shortcut.lnk differ
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/README-not-working-folder.md b/in_progress/adk_streaming_examples/NOT_WORKING/README-not-working-folder.md
new file mode 100644
index 000000000..15a187568
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/README-not-working-folder.md
@@ -0,0 +1 @@
+FILES in this folder are based on published examples, but they cause errors or I can not get them to work properly when tested.
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/README.md b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/README.md
new file mode 100644
index 000000000..16aba4ccb
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/README.md
@@ -0,0 +1,582 @@
+this document is pasted from url:  https://google.github.io/adk-docs/streaming/custom-streaming/
+IMPORTANT NOTE:  I ran: pip install "fastapi[standard]"   
+     app/main.py uses fastapi    
+fastAPI documentation:  https://fastapi.tiangolo.com/  
+
+*EDITS  - I had to remove the google_search_tool calls due to errors:  "RESOURCE_EXHAUSTED: Maximum google search tool usage exceeded."  This error occured when streaming with this tool in other examples too.
+
+-Verified as working with audio conversation and allows for interuptions.   However the responeses seems limited to be only a couple lines at the moment.
+
+Custom Audio Streaming app (SSE)¶
+This article overviews the server and client code for a custom asynchronous web app built with ADK Streaming and FastAPI, enabling real-time, bidirectional audio and text communication with Server-Sent Events (SSE). The key features are:
+
+Server-Side (Python/FastAPI): - FastAPI + ADK integration - Server-Sent Events for real-time streaming - Session management with isolated user contexts - Support for both text and audio communication modes - Google Search tool integration for grounded responses
+
+Client-Side (JavaScript/Web Audio API): - Real-time bidirectional communication via SSE and HTTP POST - Professional audio processing using AudioWorklet processors - Seamless mode switching between text and audio - Automatic reconnection and error handling - Base64 encoding for audio data transmission
+
+1. Install ADK¶
+Create & Activate Virtual Environment (Recommended):
+
+
+# Create
+python -m venv .venv
+# Activate (each new terminal)
+# macOS/Linux: source .venv/bin/activate
+# Windows CMD: .venv\Scripts\activate.bat
+# Windows PowerShell: .venv\Scripts\Activate.ps1
+Install ADK:
+
+
+pip install google-adk==1.0.0
+Set SSL_CERT_FILE variable with the following command.
+Set SSL_CERT_FILE variable with the following command.     this is required everytime you start a new terminal
+
+    in powershell:   $env:SSL_CERT_FILE = (python -m certifi)
+Custom: ALternately you can add this to .venv\Scripts\Activate.ps1 at end of file  
+      # CUSTOM ADDITON: Automatically set the SSL_CERT_FILE path using the certifi package
+      # to prevent potential SSL verification errors with Google Cloud APIs.
+      $env:SSL_CERT_FILE = $(python -m certifi)
+  When you activate the .venv this will run
+
+
+Download the sample code:
+
+
+git clone --no-checkout https://github.com/google/adk-docs.git
+cd adk-docs
+git sparse-checkout init --cone
+git sparse-checkout set examples/python/snippets/streaming/adk-streaming
+git checkout main
+cd examples/python/snippets/streaming/adk-streaming/app
+This sample code has the following files and folders:
+
+
+adk-streaming/
+└── app/ # the web app folder
+    ├── .env # Gemini API key / Google Cloud Project ID
+    ├── main.py # FastAPI web app
+    ├── static/ # Static content folder
+    |   ├── js # JavaScript files folder (includes app.js)
+    |   └── index.html # The web client page
+    └── google_search_agent/ # Agent folder
+        ├── __init__.py # Python package
+        └── agent.py # Agent definition
+2. Set up the platform¶
+To run the sample app, choose a platform from either Google AI Studio or Google Cloud Vertex AI:
+
+
+Gemini - Google AI Studio
+Gemini - Google Cloud Vertex AI
+Get an API key from Google AI Studio.
+Open the .env file located inside (app/) and copy-paste the following code.
+
+.env
+
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
+Replace PASTE_YOUR_ACTUAL_API_KEY_HERE with your actual API KEY.
+
+
+3. Interact with Your Streaming app¶
+1. Navigate to the Correct Directory:
+
+To run your agent effectively, make sure you are in the app folder (adk-streaming/app)
+
+2. Start the Fast API: Run the following command to start CLI interface with
+
+
+uvicorn main:app --reload
+3. Access the app with the text mode: Once the app starts, the terminal will display a local URL (e.g., http://localhost:8000). Click this link to open the UI in your browser.
+
+Now you should see the UI like this:
+
+ADK Streaming app
+
+Try asking a question What time is it now?. The agent will use Google Search to respond to your queries. You would notice that the UI shows the agent's response as streaming text. You can also send messages to the agent at any time, even while the agent is still responding. This demonstrates the bidirectional communication capability of ADK Streaming.
+
+4. Access the app with the audio mode: Now click the Start Audio button. The app reconnects with the server in an audio mode, and the UI will show the following dialog for the first time:
+
+ADK Streaming app
+
+Click Allow while visiting the site, then you will see the microphone icon will be shown at the top of the browser:
+
+ADK Streaming app
+
+Now you can talk to the agent with voice. Ask questions like What time is it now? with voice and you will hear the agent responding in voice too. As Streaming for ADK supports multiple languages, it can also respond to question in the supported languages.
+
+5. Check console logs
+
+If you are using the Chrome browser, use the right click and select Inspect to open the DevTools. On the Console, you can see the incoming and outgoing audio data such as [CLIENT TO AGENT] and [AGENT TO CLIENT], representing the audio data streaming in and out between the browser and the server.
+
+At the same time, in the app server console, you should see something like this:
+
+
+Client #90766266 connected via SSE, audio mode: false
+INFO:     127.0.0.1:52692 - "GET /events/90766266?is_audio=false HTTP/1.1" 200 OK
+[CLIENT TO AGENT]: hi
+INFO:     127.0.0.1:52696 - "POST /send/90766266 HTTP/1.1" 200 OK
+[AGENT TO CLIENT]: text/plain: {'mime_type': 'text/plain', 'data': 'Hi'}
+[AGENT TO CLIENT]: text/plain: {'mime_type': 'text/plain', 'data': ' there! How can I help you today?\n'}
+[AGENT TO CLIENT]: {'turn_complete': True, 'interrupted': None}
+These console logs are important in case you develop your own streaming application. In many cases, the communication failure between the browser and server becomes a major cause for the streaming application bugs.
+
+6. Troubleshooting tips
+
+When gemini-2.0-flash-exp model doesn't work: If you see any errors on the app server console with regard to gemini-2.0-flash-exp model availability, try replacing it with gemini-2.0-flash-live-001 on app/google_search_agent/agent.py at line 6.
+4. Agent definition¶
+The agent definition code agent.py in the google_search_agent folder is where the agent's logic is written:
+
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   name="google_search_agent",
+   model="gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",
+   description="Agent to answer questions using Google Search.",
+   instruction="Answer the question using the Google Search tool.",
+   tools=[google_search],
+)
+Notice how easily you integrated grounding with Google Search capabilities. The Agent class and the google_search tool handle the complex interactions with the LLM and grounding with the search API, allowing you to focus on the agent's purpose and behavior.
+
+intro_components.png
+
+The server and client architecture enables real-time, bidirectional communication between web clients and AI agents with proper session isolation and resource management.
+
+5. Server side code overview¶
+The FastAPI server provides real-time communication between web clients and the AI agent.
+
+Bidirectional communication overview¶
+Client-to-Agent Flow:¶
+Connection Establishment - Client opens SSE connection to /events/{user_id}, triggering session creation and storing request queue in active_sessions
+Message Transmission - Client sends POST to /send/{user_id} with JSON payload containing mime_type and data
+Queue Processing - Server retrieves session's live_request_queue and forwards message to agent via send_content() or send_realtime()
+Agent-to-Client Flow:¶
+Event Generation - Agent processes requests and generates events through live_events async generator
+Stream Processing - agent_to_client_sse() filters events and formats them as SSE-compatible JSON
+Real-time Delivery - Events stream to client via persistent HTTP connection with proper SSE headers
+Session Management:¶
+Per-User Isolation - Each user gets unique session stored in active_sessions dict
+Lifecycle Management - Sessions auto-cleanup on disconnect with proper resource disposal
+Concurrent Support - Multiple users can have simultaneous active sessions
+Error Handling:¶
+Session Validation - POST requests validate session existence before processing
+Stream Resilience - SSE streams handle exceptions and perform cleanup automatically
+Connection Recovery - Clients can reconnect by re-establishing SSE connection
+Agent Session Management¶
+The start_agent_session() function creates isolated AI agent sessions:
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+InMemoryRunner Setup - Creates a runner instance that manages the agent lifecycle in memory, with the app name "ADK Streaming example" and the Google Search agent.
+
+Session Creation - Uses runner.session_service.create_session() to establish a unique session per user ID, enabling multiple concurrent users.
+
+Response Modality Configuration - Sets RunConfig with either "AUDIO" or "TEXT" modality based on the is_audio parameter, determining output format.
+
+LiveRequestQueue - Creates a bidirectional communication channel that queues incoming requests and enables real-time message passing between client and agent.
+
+Live Events Stream - runner.run_live() returns an async generator that yields real-time events from the agent, including partial responses, turn completions, and interruptions.
+
+Server-Sent Events (SSE) Streaming¶
+The agent_to_client_sse() function handles real-time streaming from agent to client:
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+Event Processing Loop - Iterates through live_events async generator, processing each event as it arrives from the agent.
+
+Turn Management - Detects conversation turn completion or interruption events and sends JSON messages with turn_complete and interrupted flags to signal conversation state changes.
+
+Content Part Extraction - Extracts the first Part from event content, which contains either text or audio data.
+
+Audio Streaming - Handles PCM audio data by:
+
+Detecting audio/pcm MIME type in inline_data
+Base64 encoding raw audio bytes for JSON transmission
+Sending with mime_type and data fields
+
+Text Streaming - Processes partial text responses by sending incremental text updates as they're generated, enabling real-time typing effects.
+
+SSE Format - All data is formatted as data: {json}\n\n following SSE specification for browser EventSource API compatibility.
+
+HTTP Endpoints and Routing¶
+Root Endpoint¶
+GET / - Serves static/index.html as the main application interface using FastAPI's FileResponse.
+
+SSE Events Endpoint¶
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+GET /events/{user_id} - Establishes persistent SSE connection:
+
+Parameters - Takes user_id (int) and optional is_audio query parameter (defaults to "false")
+
+Session Initialization - Calls start_agent_session() and stores the live_request_queue in active_sessions dict using user_id as key
+
+StreamingResponse - Returns StreamingResponse with:
+
+event_generator() async function that wraps agent_to_client_sse()
+MIME type: text/event-stream
+CORS headers for cross-origin access
+Cache-control headers to prevent caching
+
+Cleanup Logic - Handles connection termination by closing the request queue and removing from active sessions, with error handling for stream interruptions.
+
+Message Sending Endpoint¶
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
+POST /send/{user_id} - Receives client messages:
+
+Session Lookup - Retrieves live_request_queue from active_sessions or returns error if session doesn't exist
+
+Message Processing - Parses JSON with mime_type and data fields:
+
+Text Messages - Creates Content with Part.from_text() and sends via send_content()
+Audio Messages - Base64 decodes PCM data and sends via send_realtime() with Blob
+
+Error Handling - Returns appropriate error responses for unsupported MIME types or missing sessions.
+
+6. Client side code overview¶
+The client-side consists of a web interface with real-time communication and audio capabilities:
+
+HTML Interface (static/index.html)¶
+
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
+Simple web interface with: - Messages Display - Scrollable div for conversation history - Text Input Form - Input field and send button for text messages - Audio Control - Button to enable audio mode and microphone access
+
+Main Application Logic (static/js/app.js)¶
+Session Management (app.js)¶
+
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let is_audio = false;
+Random Session ID - Generates unique session ID for each browser instance
+URL Construction - Builds SSE and send endpoints with session ID
+Audio Mode Flag - Tracks whether audio mode is enabled
+Server-Sent Events Connection (app.js)¶
+connectSSE() function handles real-time server communication:
+
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    ...
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+EventSource Setup - Creates SSE connection with audio mode parameter
+Connection Handlers:
+onopen - Enables send button and form submission when connected
+onmessage - Processes incoming messages from agent
+onerror - Handles disconnections with auto-reconnect after 5 seconds
+Message Processing (app.js)¶
+Handles different message types from server:
+
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+Turn Management - Detects turn_complete to reset message state
+Audio Playback - Decodes Base64 PCM data and sends to audio worklet
+Text Display - Creates new message elements and appends partial text updates for real-time typing effect
+Message Sending (app.js)¶
+sendMessage() function sends data to server:
+
+
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+HTTP POST - Sends JSON payload to /send/{session_id} endpoint
+Error Handling - Logs failed requests and network errors
+Message Format - Standardized {mime_type, data} structure
+Audio Player (static/js/audio-player.js)¶
+startAudioPlayerWorklet() function:
+
+AudioContext Setup - Creates context with 24kHz sample rate for playback
+Worklet Loading - Loads PCM player processor for audio handling
+Audio Pipeline - Connects worklet node to audio destination (speakers)
+Audio Recorder (static/js/audio-recorder.js)¶
+startAudioRecorderWorklet() function:
+
+AudioContext Setup - Creates context with 16kHz sample rate for recording
+Microphone Access - Requests user media permissions for audio input
+Audio Processing - Connects microphone to recorder worklet
+Data Conversion - Converts Float32 samples to 16-bit PCM format
+Audio Worklet Processors¶
+PCM Player Processor (static/js/pcm-player-processor.js)¶
+PCMPlayerProcessor class handles audio playback:
+
+Ring Buffer - Circular buffer for 180 seconds of 24kHz audio
+Data Ingestion - Converts Int16 to Float32 and stores in buffer
+Playback Loop - Continuously reads from buffer to output channels
+Overflow Handling - Overwrites oldest samples when buffer is full
+PCM Recorder Processor (static/js/pcm-recorder-processor.js)¶
+PCMProcessor class captures microphone input:
+
+Audio Input - Processes incoming audio frames
+Data Transfer - Copies Float32 samples and posts to main thread via message port
+Mode Switching:¶
+Audio Activation - "Start Audio" button enables microphone and reconnects SSE with audio flag
+Seamless Transition - Closes existing connection and establishes new audio-enabled session
+The client architecture enables seamless real-time communication with both text and audio modalities, using modern web APIs for professional-grade audio processing.
+
+Summary¶
+This application demonstrates a complete real-time AI agent system with the following key features:
+
+Architecture Highlights: - Real-time: Streaming responses with partial text updates and continuous audio - Robust: Comprehensive error handling and automatic recovery mechanisms - Modern: Uses latest web standards (AudioWorklet, SSE, ES6 modules)
+
+The system provides a foundation for building sophisticated AI applications that require real-time interaction, web search capabilities, and multimedia communication.
+
+Next steps for production¶
+To deploy this system in a production environment, consider implementing the following improvements:
+
+Security¶
+Authentication: Replace random session IDs with proper user authentication
+API Key Security: Use environment variables or secret management services
+HTTPS: Enforce TLS encryption for all communications
+Rate Limiting: Prevent abuse and control API costs
+Scalability¶
+Persistent Storage: Replace in-memory sessions with a persistent session
+Load Balancing: Support multiple server instances with shared session state
+Audio Optimization: Implement compression to reduce bandwidth usage
+Monitoring¶
+Error Tracking: Monitor and alert on system failures
+API Cost Monitoring: Track Google Search and Gemini usage to prevent budget overruns
+Performance Metrics: Monitor response times and audio latency
+Infrastructure¶
+Containerization: Package with Docker for consistent deployments with Cloud Run or Agent Engine
+Health Checks: Implement endpoint monitoring for uptime tracking
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/__init__.py b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/__init__.py
new file mode 100644
index 000000000..63bd45e6d
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/agent.py b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/agent.py
new file mode 100644
index 000000000..e0c4cf385
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/agent.py
@@ -0,0 +1,12 @@
+from google.adk.agents import Agent
+
+root_agent = Agent(
+    name="google_search_agent",    #greeting_agent",  #google_search_agent  
+    # https://ai.google.dev/gemini-api/docs/models
+    model= "gemini-2.0-flash-live-preview-04-09",  #"gemini-2.0-flash-exp", 
+    description="Greeting agent",
+    instruction="""
+    You are an interesting and engaging conversationalist. you will ask the users name and instegate a conversation with 
+    user.
+    """,
+)
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/agent.py_orig b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/agent.py_orig
new file mode 100644
index 000000000..015977dc3
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/google_search_agent/agent.py_orig
@@ -0,0 +1,30 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   # A unique name for the agent.
+   name="google_search_agent",
+   # The Large Language Model (LLM) that agent will use.
+   model= "gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",  "gemini-2.0-flash-live-preview-04-09",
+   # A short description of the agent's purpose.
+   description="Agent to answer questions using Google Search.",
+   # Instructions to set the agent's behavior.
+   instruction="Answer the question using the Google Search tool.",
+   # Add google_search tool to perform grounding with Google search.
+   tools=[google_search],
+)
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/main.py b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/main.py
new file mode 100644
index 000000000..f7b7d453b
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/main.py
@@ -0,0 +1,222 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import json
+import base64
+import warnings
+
+from pathlib import Path
+from dotenv import load_dotenv
+
+from google.genai.types import (
+    Part,
+    Content,
+    Blob,
+)
+
+from google.adk.runners import InMemoryRunner
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.run_config import RunConfig
+
+from fastapi import FastAPI, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+from google_search_agent.agent import root_agent
+#from greeting_agent.agent import root_agent
+
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+#
+# ADK Streaming
+#
+
+# Load Gemini API Key
+load_dotenv()
+
+APP_NAME = "ADK Streaming example"
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+
+
+#
+# FastAPI web app
+#
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+STATIC_DIR = Path("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+# Store active sessions
+active_sessions = {}
+
+
+@app.get("/")
+async def root():
+    """Serves the index.html"""
+    return FileResponse(os.path.join(STATIC_DIR, "index.html"))
+
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/index.html b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/index.html
new file mode 100644
index 000000000..9b9abd9af
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/index.html
@@ -0,0 +1,23 @@
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/app.js b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/app.js
new file mode 100644
index 000000000..895f230f7
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/app.js
@@ -0,0 +1,267 @@
+/**
+ * app.js: JS code for the adk-streaming sample app.
+ */
+
+/**
+ * SSE (Server-Sent Events) handling
+ */
+
+// Connect the server with SSE
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let eventSource = null;
+let is_audio = false;
+
+// Get DOM elements
+const messageForm = document.getElementById("messageForm");
+const messageInput = document.getElementById("message");
+const messagesDiv = document.getElementById("messages");
+let currentMessageId = null;
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // Check for interrupt message
+    if (
+      message_from_server.interrupted &&
+      message_from_server.interrupted === true
+    ) {
+      // Stop audio playback if it's playing
+      if (audioPlayerNode) {
+        audioPlayerNode.port.postMessage({ command: "endOfAudio" });
+      }
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+connectSSE();
+
+// Add submit handler to the form
+function addSubmitHandler() {
+  messageForm.onsubmit = function (e) {
+    e.preventDefault();
+    const message = messageInput.value;
+    if (message) {
+      const p = document.createElement("p");
+      p.textContent = "> " + message;
+      messagesDiv.appendChild(p);
+      messageInput.value = "";
+      sendMessage({
+        mime_type: "text/plain",
+        data: message,
+      });
+      console.log("[CLIENT TO AGENT] " + message);
+    }
+    return false;
+  };
+}
+
+// Send a message to the server via HTTP POST
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+    
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+
+// Decode Base64 data to Array
+function base64ToArray(base64) {
+  const binaryString = window.atob(base64);
+  const len = binaryString.length;
+  const bytes = new Uint8Array(len);
+  for (let i = 0; i < len; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
+
+/**
+ * Audio handling
+ */
+
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+// Audio buffering for 0.2s intervals
+let audioBuffer = [];
+let bufferTimer = null;
+
+// Import the audio worklets
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet } from "./audio-recorder.js";
+
+// Start audio
+function startAudio() {
+  // Start audio output
+  startAudioPlayerWorklet().then(([node, ctx]) => {
+    audioPlayerNode = node;
+    audioPlayerContext = ctx;
+  });
+  // Start audio input
+  startAudioRecorderWorklet(audioRecorderHandler).then(
+    ([node, ctx, stream]) => {
+      audioRecorderNode = node;
+      audioRecorderContext = ctx;
+      micStream = stream;
+    }
+  );
+}
+
+// Start the audio only when the user clicked the button
+// (due to the gesture requirement for the Web Audio API)
+const startAudioButton = document.getElementById("startAudioButton");
+startAudioButton.addEventListener("click", () => {
+  startAudioButton.disabled = true;
+  startAudio();
+  is_audio = true;
+  eventSource.close(); // close current connection
+  connectSSE(); // reconnect with the audio mode
+});
+
+// Audio recorder handler
+function audioRecorderHandler(pcmData) {
+  // Add audio data to buffer
+  audioBuffer.push(new Uint8Array(pcmData));
+  
+  // Start timer if not already running
+  if (!bufferTimer) {
+    bufferTimer = setInterval(sendBufferedAudio, 200); // 0.2 seconds
+  }
+}
+
+// Send buffered audio data every 0.2 seconds
+function sendBufferedAudio() {
+  if (audioBuffer.length === 0) {
+    return;
+  }
+  
+  // Calculate total length
+  let totalLength = 0;
+  for (const chunk of audioBuffer) {
+    totalLength += chunk.length;
+  }
+  
+  // Combine all chunks into a single buffer
+  const combinedBuffer = new Uint8Array(totalLength);
+  let offset = 0;
+  for (const chunk of audioBuffer) {
+    combinedBuffer.set(chunk, offset);
+    offset += chunk.length;
+  }
+  
+  // Send the combined audio data
+  sendMessage({
+    mime_type: "audio/pcm",
+    data: arrayBufferToBase64(combinedBuffer.buffer),
+  });
+  console.log("[CLIENT TO AGENT] sent %s bytes", combinedBuffer.byteLength);
+  
+  // Clear the buffer
+  audioBuffer = [];
+}
+
+// Stop audio recording and cleanup
+function stopAudioRecording() {
+  if (bufferTimer) {
+    clearInterval(bufferTimer);
+    bufferTimer = null;
+  }
+  
+  // Send any remaining buffered audio
+  if (audioBuffer.length > 0) {
+    sendBufferedAudio();
+  }
+}
+
+// Encode an array buffer with Base64
+function arrayBufferToBase64(buffer) {
+  let binary = "";
+  const bytes = new Uint8Array(buffer);
+  const len = bytes.byteLength;
+  for (let i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/audio-player.js b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/audio-player.js
new file mode 100644
index 000000000..f7f235e01
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+    
+    
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+    
+    // 3. Create an AudioWorkletNode   
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/audio-recorder.js b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..876932a2f
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/pcm-player-processor.js b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..9ef17a556
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/pcm-player-processor.js
@@ -0,0 +1,75 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` ~128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex != this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/pcm-recorder-processor.js b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..585d67491
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/adk-streaming_audio-edited/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/README.md b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/README.md
new file mode 100644
index 000000000..74429b209
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/README.md
@@ -0,0 +1,11 @@
+This is example is added by Joel Dream from here:  https://google.github.io/adk-docs/streaming/streaming-tools/
+
+Streaming tools allows tools(functions) to stream intermediate results back to agents and agents can respond to those intermediate results. For example, we can use streaming tools to monitor the changes of the stock price and have the agent react to it. Another example is we can have the agent monitor the video stream, and when there is changes in video stream, the agent can report the changes.
+
+To define a streaming tool, you must adhere to the following:
+
+Asynchronous Function: The tool must be an async Python function.
+AsyncGenerator Return Type: The function must be typed to return an AsyncGenerator. The first type parameter to AsyncGenerator is the type of the data you yield (e.g., str for text messages, or a custom object for structured data). The second type parameter is typically None if the generator doesn't receive values via send().
+We support two types of streaming tools: - Simple type. This is a one type of streaming tools that only take non video/audio streams(the streams that you feed to adk web or adk runner) as input. - Video streaming tools. This only works in video streaming and the video stream(the streams that you feed to adk web or adk runner) will be passed into this function.
+
+Now let's define an agent that can monitor stock price changes and monitor the video stream changes.
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/main.py b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/main.py
new file mode 100644
index 000000000..f530ca3b3
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/main.py
@@ -0,0 +1,221 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import json
+import base64
+import warnings
+
+from pathlib import Path
+from dotenv import load_dotenv
+
+from google.genai.types import (
+    Part,
+    Content,
+    Blob,
+)
+
+from google.adk.runners import InMemoryRunner
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.run_config import RunConfig
+
+from fastapi import FastAPI, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+from video_streaming_agent.agent import root_agent
+
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+#
+# ADK Streaming
+#
+
+# Load Gemini API Key
+load_dotenv()
+
+APP_NAME = "ADK Streaming example"
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+
+
+#
+# FastAPI web app
+#
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+STATIC_DIR = Path("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+# Store active sessions
+active_sessions = {}
+
+
+@app.get("/")
+async def root():
+    """Serves the index.html"""
+    return FileResponse(os.path.join(STATIC_DIR, "index.html"))
+
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/index.html b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/index.html
new file mode 100644
index 000000000..2286c371e
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/index.html
@@ -0,0 +1,23 @@
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/app.js b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/app.js
new file mode 100644
index 000000000..31f5986fb
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/app.js
@@ -0,0 +1,218 @@
+/**
+ * app.js: JS code for the adk-streaming sample app.
+ */
+
+/**
+ * SSE (Server-Sent Events) handling
+ */
+
+// Connect the server with SSE
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let eventSource = null;
+let is_audio = false;
+
+// Get DOM elements
+const messageForm = document.getElementById("messageForm");
+const messageInput = document.getElementById("message");
+const messagesDiv = document.getElementById("messages");
+let currentMessageId = null;
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // Check for interrupt message
+    if (
+      message_from_server.interrupted &&
+      message_from_server.interrupted === true
+    ) {
+      // Stop audio playback if it's playing
+      if (audioPlayerNode) {
+        audioPlayerNode.port.postMessage({ command: "endOfAudio" });
+      }
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+connectSSE();
+
+// Add submit handler to the form
+function addSubmitHandler() {
+  messageForm.onsubmit = function (e) {
+    e.preventDefault();
+    const message = messageInput.value;
+    if (message) {
+      const p = document.createElement("p");
+      p.textContent = "> " + message;
+      messagesDiv.appendChild(p);
+      messageInput.value = "";
+      sendMessage({
+        mime_type: "text/plain",
+        data: message,
+      });
+      console.log("[CLIENT TO AGENT] " + message);
+    }
+    return false;
+  };
+}
+
+// Send a message to the server via HTTP POST
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+    
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+
+// Decode Base64 data to Array
+function base64ToArray(base64) {
+  const binaryString = window.atob(base64);
+  const len = binaryString.length;
+  const bytes = new Uint8Array(len);
+  for (let i = 0; i < len; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
+
+/**
+ * Audio handling
+ */
+
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+// Import the audio worklets
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet } from "./audio-recorder.js";
+
+// Start audio
+function startAudio() {
+  // Start audio output
+  startAudioPlayerWorklet().then(([node, ctx]) => {
+    audioPlayerNode = node;
+    audioPlayerContext = ctx;
+  });
+  // Start audio input
+  startAudioRecorderWorklet(audioRecorderHandler).then(
+    ([node, ctx, stream]) => {
+      audioRecorderNode = node;
+      audioRecorderContext = ctx;
+      micStream = stream;
+    }
+  );
+}
+
+// Start the audio only when the user clicked the button
+// (due to the gesture requirement for the Web Audio API)
+const startAudioButton = document.getElementById("startAudioButton");
+startAudioButton.addEventListener("click", () => {
+  startAudioButton.disabled = true;
+  startAudio();
+  is_audio = true;
+  eventSource.close(); // close current connection
+  connectSSE(); // reconnect with the audio mode
+});
+
+// Audio recorder handler
+function audioRecorderHandler(pcmData) {
+  // Send the pcm data as base64
+  sendMessage({
+    mime_type: "audio/pcm",
+    data: arrayBufferToBase64(pcmData),
+  });
+  console.log("[CLIENT TO AGENT] sent %s bytes", pcmData.byteLength);
+}
+
+// Encode an array buffer with Base64
+function arrayBufferToBase64(buffer) {
+  let binary = "";
+  const bytes = new Uint8Array(buffer);
+  const len = bytes.byteLength;
+  for (let i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/audio-player.js b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/audio-player.js
new file mode 100644
index 000000000..af890517e
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+    
+    
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+    
+    // 3. Create an AudioWorkletNode   
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/audio-recorder.js b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..82806e6dd
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/pcm-player-processor.js b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..6d9dbf23b
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/pcm-player-processor.js
@@ -0,0 +1,76 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` ~128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex != this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
+
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/pcm-recorder-processor.js b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..b1c54bd31
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/video_streaming_agent/__init__.py b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/video_streaming_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/video_streaming_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/video_streaming_agent/agent.py b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/video_streaming_agent/agent.py
new file mode 100644
index 000000000..58d2f3cfa
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-(not_working)/app/video_streaming_agent/agent.py
@@ -0,0 +1,126 @@
+
+import asyncio
+from typing import AsyncGenerator
+
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.llm_agent import Agent
+from google.adk.tools.function_tool import FunctionTool
+from google.genai import Client
+from google.genai import types as genai_types
+
+
+async def monitor_stock_price(stock_symbol: str) -> AsyncGenerator[str, None]:
+  """This function will monitor the price for the given stock_symbol in a continuous, streaming and asynchronously way."""
+  print(f"Start monitor stock price for {stock_symbol}!")
+
+  # Let's mock stock price change.
+  await asyncio.sleep(4)
+  price_alert1 = f"the price for {stock_symbol} is 300"
+  yield price_alert1
+  print(price_alert1)
+
+  await asyncio.sleep(4)
+  price_alert1 = f"the price for {stock_symbol} is 400"
+  yield price_alert1
+  print(price_alert1)
+
+  await asyncio.sleep(20)
+  price_alert1 = f"the price for {stock_symbol} is 900"
+  yield price_alert1
+  print(price_alert1)
+
+  await asyncio.sleep(20)
+  price_alert1 = f"the price for {stock_symbol} is 500"
+  yield price_alert1
+  print(price_alert1)
+
+
+# for video streaming, `input_stream: LiveRequestQueue` is required and reserved key parameter for ADK to pass the video streams in.
+async def monitor_video_stream(     
+    input_stream: LiveRequestQueue,
+) -> AsyncGenerator[str, None]:
+  """Monitor how many people are in the video streams."""
+  print("start monitor_video_stream!")
+  client = Client() # removed:  Client(vertexai=False)
+  prompt_text = (
+      "Count the number of people in this image. Just respond with a numeric"
+      " number."
+  )
+  last_count = None
+  while True:
+    last_valid_req = None
+    print("Start monitoring loop")
+
+    # use this loop to pull the latest images and discard the old ones
+    while input_stream._queue.qsize() != 0:
+      live_req = await input_stream.get()
+
+      if live_req.blob is not None and live_req.blob.mime_type == "image/jpeg":
+        last_valid_req = live_req
+
+    # If we found a valid image, process it
+    if last_valid_req is not None:
+      print("Processing the most recent frame from the queue")
+
+      # Create an image part using the blob's data and mime type
+      image_part = genai_types.Part.from_bytes(
+          data=last_valid_req.blob.data, mime_type=last_valid_req.blob.mime_type
+      )
+
+      contents = genai_types.Content(
+          role="user",
+          parts=[image_part, genai_types.Part.from_text(prompt_text)],
+      )
+
+      # Call the model to generate content based on the provided image and prompt
+      response = client.models.generate_content(
+          model= "gemini-2.0-flash-live-preview-04-09", #"gemini-2.0-flash-live-001", #"gemini-2.0-flash-exp",
+          contents=contents,
+          config=genai_types.GenerateContentConfig(
+              system_instruction=(
+                  "You are a helpful video analysis assistant. You can count"
+                  " the number of people in this image or video. Just respond"
+                  " with a numeric number."
+              )
+          ),
+      )
+      if not last_count:
+        last_count = response.candidates[0].content.parts[0].text
+      elif last_count != response.candidates[0].content.parts[0].text:
+        last_count = response.candidates[0].content.parts[0].text
+        yield response
+        print("response:", response)
+
+    # Wait before checking for new images
+    await asyncio.sleep(0.5)
+
+
+# Use this exact function to help ADK stop your streaming tools when requested.
+# for example, if we want to stop `monitor_stock_price`, then the agent will
+# invoke this function with stop_streaming(function_name=monitor_stock_price).
+def stop_streaming(function_name: str):
+  """Stop the streaming
+
+  Args:
+    function_name: The name of the streaming function to stop.
+  """
+  pass
+
+
+root_agent = Agent(
+    model= "gemini-2.0-flash-live-preview-04-09", #"gemini-2.0-flash-live-001", #"gemini-2.0-flash-exp",
+    name="video_streaming_agent",
+    instruction="""
+      You are a monitoring agent. You can do video monitoring and
+      using the provided tools/functions.
+      When users want to monitor a video stream,
+      You can use monitor_video_stream function to do that. When monitor_video_stream
+      returns the alert, you should tell the users.
+      Don't ask too many questions. Don't be too talkative.
+    """,
+    tools=[
+        monitor_video_stream,
+       # monitor_stock_price,
+        FunctionTool(stop_streaming),
+    ]
+)
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/README.md b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/README.md
new file mode 100644
index 000000000..74429b209
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/README.md	
@@ -0,0 +1,11 @@
+This is example is added by Joel Dream from here:  https://google.github.io/adk-docs/streaming/streaming-tools/
+
+Streaming tools allows tools(functions) to stream intermediate results back to agents and agents can respond to those intermediate results. For example, we can use streaming tools to monitor the changes of the stock price and have the agent react to it. Another example is we can have the agent monitor the video stream, and when there is changes in video stream, the agent can report the changes.
+
+To define a streaming tool, you must adhere to the following:
+
+Asynchronous Function: The tool must be an async Python function.
+AsyncGenerator Return Type: The function must be typed to return an AsyncGenerator. The first type parameter to AsyncGenerator is the type of the data you yield (e.g., str for text messages, or a custom object for structured data). The second type parameter is typically None if the generator doesn't receive values via send().
+We support two types of streaming tools: - Simple type. This is a one type of streaming tools that only take non video/audio streams(the streams that you feed to adk web or adk runner) as input. - Video streaming tools. This only works in video streaming and the video stream(the streams that you feed to adk web or adk runner) will be passed into this function.
+
+Now let's define an agent that can monitor stock price changes and monitor the video stream changes.
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/app/video_streaming_agent/__init__.py b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/app/video_streaming_agent/__init__.py
new file mode 100644
index 000000000..02c597e11
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/app/video_streaming_agent/__init__.py	
@@ -0,0 +1 @@
+from . import agent
diff --git a/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/app/video_streaming_agent/agent.py b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/app/video_streaming_agent/agent.py
new file mode 100644
index 000000000..3ce23f4f8
--- /dev/null
+++ b/in_progress/adk_streaming_examples/NOT_WORKING/streaming-tools-orig_not working/app/video_streaming_agent/agent.py	
@@ -0,0 +1,126 @@
+import asyncio
+from typing import AsyncGenerator
+
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.llm_agent import Agent
+from google.adk.tools.function_tool import FunctionTool
+from google.genai import Client
+from google.genai import types as genai_types
+
+
+async def monitor_stock_price(stock_symbol: str) -> AsyncGenerator[str, None]:
+  """This function will monitor the price for the given stock_symbol in a continuous, streaming and asynchronously way."""
+  print(f"Start monitor stock price for {stock_symbol}!")
+
+  # Let's mock stock price change.
+  await asyncio.sleep(4)
+  price_alert1 = f"the price for {stock_symbol} is 300"
+  yield price_alert1
+  print(price_alert1)
+
+  await asyncio.sleep(4)
+  price_alert1 = f"the price for {stock_symbol} is 400"
+  yield price_alert1
+  print(price_alert1)
+
+  await asyncio.sleep(20)
+  price_alert1 = f"the price for {stock_symbol} is 900"
+  yield price_alert1
+  print(price_alert1)
+
+  await asyncio.sleep(20)
+  price_alert1 = f"the price for {stock_symbol} is 500"
+  yield price_alert1
+  print(price_alert1)
+
+
+# for video streaming, `input_stream: LiveRequestQueue` is required and reserved key parameter for ADK to pass the video streams in.
+async def monitor_video_stream(
+    input_stream: LiveRequestQueue,
+) -> AsyncGenerator[str, None]:
+  """Monitor how many people are in the video streams."""
+  print("start monitor_video_stream!")
+  client = Client(vertexai=False)
+  prompt_text = (
+      "Count the number of people in this image. Just respond with a numeric"
+      " number."
+  )
+  last_count = None
+  while True:
+    last_valid_req = None
+    print("Start monitoring loop")
+
+    # use this loop to pull the latest images and discard the old ones
+    while input_stream._queue.qsize() != 0:
+      live_req = await input_stream.get()
+
+      if live_req.blob is not None and live_req.blob.mime_type == "image/jpeg":
+        last_valid_req = live_req
+
+    # If we found a valid image, process it
+    if last_valid_req is not None:
+      print("Processing the most recent frame from the queue")
+
+      # Create an image part using the blob's data and mime type
+      image_part = genai_types.Part.from_bytes(
+          data=last_valid_req.blob.data, mime_type=last_valid_req.blob.mime_type
+      )
+
+      contents = genai_types.Content(
+          role="user",
+          parts=[image_part, genai_types.Part.from_text(prompt_text)],
+      )
+
+      # Call the model to generate content based on the provided image and prompt
+      response = client.models.generate_content(
+          model="gemini-2.0-flash-exp",
+          contents=contents,
+          config=genai_types.GenerateContentConfig(
+              system_instruction=(
+                  "You are a helpful video analysis assistant. You can count"
+                  " the number of people in this image or video. Just respond"
+                  " with a numeric number."
+              )
+          ),
+      )
+      if not last_count:
+        last_count = response.candidates[0].content.parts[0].text
+      elif last_count != response.candidates[0].content.parts[0].text:
+        last_count = response.candidates[0].content.parts[0].text
+        yield response
+        print("response:", response)
+
+    # Wait before checking for new images
+    await asyncio.sleep(0.5)
+
+
+# Use this exact function to help ADK stop your streaming tools when requested.
+# for example, if we want to stop `monitor_stock_price`, then the agent will
+# invoke this function with stop_streaming(function_name=monitor_stock_price).
+def stop_streaming(function_name: str):
+  """Stop the streaming
+
+  Args:
+    function_name: The name of the streaming function to stop.
+  """
+  pass
+
+
+root_agent = Agent(
+    model="gemini-2.0-flash-exp",
+    name="video_streaming_agent",
+    instruction="""
+      You are a monitoring agent. You can do video monitoring and stock price monitoring
+      using the provided tools/functions.
+      When users want to monitor a video stream,
+      You can use monitor_video_stream function to do that. When monitor_video_stream
+      returns the alert, you should tell the users.
+      When users want to monitor a stock price, you can use monitor_stock_price.
+      Don't ask too many questions. Don't be too talkative.
+    """,
+    tools=[
+        monitor_video_stream,
+        monitor_stock_price,
+        FunctionTool(stop_streaming),
+    ]
+)
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/README-working.md b/in_progress/adk_streaming_examples/WORKING-VERIFIED/README-working.md
new file mode 100644
index 000000000..4878082a0
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/README-working.md
@@ -0,0 +1 @@
+FILES in this folder are based on published examples and are verified to work properly by Joel when run locally.  
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/README.md b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/README.md
new file mode 100644
index 000000000..72d550ac1
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/README.md
@@ -0,0 +1,143 @@
+This is example is added by Joel Dream from here:  https://google.github.io/adk-docs/get-started/streaming/quickstart-streaming/
+
+*Important NOTE*  for some reason the ability to use websearch causes erorrs,  after brief when this tool is used with streaming audio or video.  error being "RESOURCE_EXHAUSTED: Maximum google search tool usage exceeded."  the web search  still works with text.   I cannot remember if i tested the web_search capablilties with audio or video.   However I did verify that video streaming can tell what it sees in camera, and voice audio input and output streaming bot h work.   
+However, When resorces exhausted error begins, (after daily limit reached possibly) then i can no longer use audio or video.   
+
+-However things have been verified to work with audio and video streaming using "adk web" to open web interface.
+- The audio does not allow interuptions.    
+
+
+With this quickstart, you'll learn to create a simple agent and use ADK Streaming to enable voice and video communication with it that is low-latency and bidirectional. We will install ADK, set up a basic "Google Search" agent, try running the agent with Streaming with adk web tool, and then explain how to build a simple asynchronous web app by yourself using ADK Streaming and FastAPI.
+
+Note: This guide assumes you have experience using a terminal in Windows, Mac, and Linux environments.
+
+Supported models for voice/video streaming¶
+In order to use voice/video streaming in ADK, you will need to use Gemini models that support the Live API. You can find the model ID(s) that supports the Gemini Live API in the documentation:
+
+Google AI Studio: Gemini Live API
+Vertex AI: Gemini Live API
+1. Setup Environment & Install ADK¶
+Create & Activate Virtual Environment (Recommended):
+
+
+# Create
+python -m venv .venv
+# Activate (each new terminal)
+# macOS/Linux: source .venv/bin/activate
+# Windows CMD: .venv\Scripts\activate.bat
+# Windows PowerShell: .venv\Scripts\Activate.ps1
+Install ADK:
+
+
+pip install google-adk
+2. Project Structure¶
+Create the following folder structure with empty files:
+
+
+adk-streaming/  # Project folder
+└── app/ # the web app folder
+    ├── .env # Gemini API key
+    └── google_search_agent/ # Agent folder
+        ├── __init__.py # Python package
+        └── agent.py # Agent definition
+agent.py¶
+Copy-paste the following code block to the agent.py.
+
+For model, please double check the model ID as described earlier in the Models section.
+
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   # A unique name for the agent.
+   name="basic_search_agent",
+   # The Large Language Model (LLM) that agent will use.
+   model="gemini-2.0-flash-exp",
+   # model="gemini-2.0-flash-live-001",  # New streaming model version as of Feb 2025
+   # A short description of the agent's purpose.
+   description="Agent to answer questions using Google Search.",
+   # Instructions to set the agent's behavior.
+   instruction="You are an expert researcher. You always stick to the facts.",
+   # Add google_search tool to perform grounding with Google search.
+   tools=[google_search]
+)
+Note: To enable both text and audio/video input, the model must support the generateContent (for text) and bidiGenerateContent methods. Verify these capabilities by referring to the List Models Documentation. This quickstart utilizes the gemini-2.0-flash-exp model for demonstration purposes.
+
+agent.py is where all your agent(s)' logic will be stored, and you must have a root_agent defined.
+
+Notice how easily you integrated grounding with Google Search capabilities. The Agent class and the google_search tool handle the complex interactions with the LLM and grounding with the search API, allowing you to focus on the agent's purpose and behavior.
+
+intro_components.png
+
+Copy-paste the following code block to __init__.py file.
+
+__init__.py
+
+from . import agent
+3. Set up the platform¶
+To run the agent, choose a platform from either Google AI Studio or Google Cloud Vertex AI:
+
+
+Gemini - Google AI Studio
+Gemini - Google Cloud Vertex AI
+Get an API key from Google AI Studio.
+Open the .env file located inside (app/) and copy-paste the following code.
+
+.env
+
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
+Replace PASTE_YOUR_ACTUAL_API_KEY_HERE with your actual API KEY.
+
+
+4. Try the agent with adk web¶
+Now it's ready to try the agent. Run the following command to launch the dev UI. First, make sure to set the current directory to app:
+
+
+cd app
+Also, set SSL_CERT_FILE variable with the following command. This is required for the voice and video tests later.
+Set SSL_CERT_FILE variable with the following command.     this is required to be set everytime you start a new terminal
+
+    in powershell:   $env:SSL_CERT_FILE = (python -m certifi)
+Custom: ALternately you can add this to .venv\Scripts\Activate.ps1 at end of file  
+      # CUSTOM ADDITON: Automatically set the SSL_CERT_FILE path using the certifi package
+      # to prevent potential SSL verification errors with Google Cloud APIs.
+      $env:SSL_CERT_FILE = $(python -m certifi)
+  When you activate the .venv this will run
+
+
+Then, run the dev UI:
+
+
+adk web
+Note for Windows users
+
+When hitting the _make_subprocess_transport NotImplementedError, consider using adk web --no-reload instead.
+
+Open the URL provided (usually http://localhost:8000 or http://127.0.0.1:8000) directly in your browser. This connection stays entirely on your local machine. Select google_search_agent.
+
+Try with text¶
+Try the following prompts by typing them in the UI.
+
+What is the weather in New York?
+What is the time in New York?
+What is the weather in Paris?
+What is the time in Paris?
+The agent will use the google_search tool to get the latest information to answer those questions.
+
+Try with voice and video¶
+To try with voice, reload the web browser, click the microphone button to enable the voice input, and ask the same question in voice. You will hear the answer in voice in real-time.
+
+To try with video, reload the web browser, click the camera button to enable the video input, and ask questions like "What do you see?". The agent will answer what they see in the video input.
+
+Stop the tool¶
+Stop adk web by pressing Ctrl-C on the console.
+
+Note on ADK Streaming¶
+The following features will be supported in the future versions of the ADK Streaming: Callback, LongRunningTool, ExampleTool, and Shell agent (e.g. SequentialAgent).
+
+Congratulations! You've successfully created and interacted with your first Streaming agent using ADK!
+
+Next steps: build custom streaming app¶
+In Custom Audio Streaming app tutorial, it overviews the server and client code for a custom asynchronous web app built with ADK Streaming and FastAPI, enabling real-time, bidirectional audio and text communication.
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/README.md b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/README.md
new file mode 100644
index 000000000..e8fa81fb7
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/README.md
@@ -0,0 +1,586 @@
+this document is pasted from url: https://google.github.io/adk-docs/streaming/custom-streaming-ws/
+
+This article overviews the server and client code for a custom asynchronous web app built with ADK Streaming and FastAPI, enabling real-time, bidirectional audio and text communication with WebSockets.
+
+Note: This guide assumes you have experience of JavaScript and Python asyncio programming.
+
+Supported models for voice/video streaming¶
+In order to use voice/video streaming in ADK, you will need to use Gemini models that support the Live API. You can find the model ID(s) that supports the Gemini Live API in the documentation:
+
+Google AI Studio: Gemini Live API
+Vertex AI: Gemini Live API
+1. Install ADK¶
+Create & Activate Virtual Environment (Recommended):
+
+
+# Create
+python -m venv .venv
+# Activate (each new terminal)
+# macOS/Linux: source .venv/bin/activate
+# Windows CMD: .venv\Scripts\activate.bat
+# Windows PowerShell: .venv\Scripts\Activate.ps1
+Install ADK:
+
+
+pip install google-adk==1.0.0
+Set SSL_CERT_FILE variable with the following command.
+
+
+export SSL_CERT_FILE=$(python -m certifi)
+Download the sample code:
+
+
+git clone --no-checkout https://github.com/google/adk-docs.git
+cd adk-docs
+git sparse-checkout init --cone
+git sparse-checkout set examples/python/snippets/streaming/adk-streaming-ws
+git checkout main
+cd examples/python/snippets/streaming/adk-streaming-ws/app
+This sample code has the following files and folders:
+
+
+adk-streaming-ws/
+└── app/ # the web app folder
+    ├── .env # Gemini API key / Google Cloud Project ID
+    ├── main.py # FastAPI web app
+    ├── static/ # Static content folder
+    |   ├── js # JavaScript files folder (includes app.js)
+    |   └── index.html # The web client page
+    └── google_search_agent/ # Agent folder
+        ├── __init__.py # Python package
+        └── agent.py # Agent definition
+2. Set up the platform¶
+To run the sample app, choose a platform from either Google AI Studio or Google Cloud Vertex AI:
+
+
+Gemini - Google AI Studio
+Gemini - Google Cloud Vertex AI
+Get an API key from Google AI Studio.
+Open the .env file located inside (app/) and copy-paste the following code.
+
+.env
+
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
+Replace PASTE_YOUR_ACTUAL_API_KEY_HERE with your actual API KEY.
+
+
+agent.py¶
+The agent definition code agent.py in the google_search_agent folder is where the agent's logic is written:
+
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   name="google_search_agent",
+   model="gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",
+   description="Agent to answer questions using Google Search.",
+   instruction="Answer the question using the Google Search tool.",
+   tools=[google_search],
+)
+Note: To enable both text and audio/video input, the model must support the generateContent (for text) and bidiGenerateContent methods. Verify these capabilities by referring to the List Models Documentation. This quickstart utilizes the gemini-2.0-flash-exp model for demonstration purposes.
+
+Notice how easily you integrated grounding with Google Search capabilities. The Agent class and the google_search tool handle the complex interactions with the LLM and grounding with the search API, allowing you to focus on the agent's purpose and behavior.
+
+intro_components.png
+
+3. Interact with Your Streaming app¶
+1. Navigate to the Correct Directory:
+
+To run your agent effectively, make sure you are in the app folder (adk-streaming-ws/app)
+
+2. Start the Fast API: Run the following command to start CLI interface with
+
+
+uvicorn main:app --reload
+3. Access the app with the text mode: Once the app starts, the terminal will display a local URL (e.g., http://localhost:8000). Click this link to open the UI in your browser.
+
+Now you should see the UI like this:
+
+ADK Streaming app
+
+Try asking a question What time is it now?. The agent will use Google Search to respond to your queries. You would notice that the UI shows the agent's response as streaming text. You can also send messages to the agent at any time, even while the agent is still responding. This demonstrates the bidirectional communication capability of ADK Streaming.
+
+4. Access the app with the audio mode: Now click the Start Audio button. The app reconnects with the server in an audio mode, and the UI will show the following dialog for the first time:
+
+ADK Streaming app
+
+Click Allow while visiting the site, then you will see the microphone icon will be shown at the top of the browser:
+
+ADK Streaming app
+
+Now you can talk to the agent with voice. Ask questions like What time is it now? with voice and you will hear the agent responding in voice too. As Streaming for ADK supports multiple languages, it can also respond to question in the supported languages.
+
+5. Check console logs
+
+If you are using the Chrome browser, use the right click and select Inspect to open the DevTools. On the Console, you can see the incoming and outgoing audio data such as [CLIENT TO AGENT] and [AGENT TO CLIENT], representing the audio data streaming in and out between the browser and the server.
+
+At the same time, in the app server console, you should see something like this:
+
+
+INFO:     ('127.0.0.1', 50068) - "WebSocket /ws/70070018?is_audio=true" [accepted]
+Client #70070018 connected, audio mode: true
+INFO:     connection open
+INFO:     127.0.0.1:50061 - "GET /static/js/pcm-player-processor.js HTTP/1.1" 200 OK
+INFO:     127.0.0.1:50060 - "GET /static/js/pcm-recorder-processor.js HTTP/1.1" 200 OK
+[AGENT TO CLIENT]: audio/pcm: 9600 bytes.
+INFO:     127.0.0.1:50082 - "GET /favicon.ico HTTP/1.1" 404 Not Found
+[AGENT TO CLIENT]: audio/pcm: 11520 bytes.
+[AGENT TO CLIENT]: audio/pcm: 11520 bytes.
+These console logs are important in case you develop your own streaming application. In many cases, the communication failure between the browser and server becomes a major cause for the streaming application bugs.
+
+6. Troubleshooting tips
+
+When ws:// doesn't work: If you see any errors on the Chrome DevTools with regard to ws:// connection, try replacing ws:// with wss:// on app/static/js/app.js at line 28. This may happen when you are running the sample on a cloud environment and using a proxy connection to connect from your browser.
+When gemini-2.0-flash-exp model doesn't work: If you see any errors on the app server console with regard to gemini-2.0-flash-exp model availability, try replacing it with gemini-2.0-flash-live-001 on app/google_search_agent/agent.py at line 6.
+4. Server code overview¶
+This server app enables real-time, streaming interaction with ADK agent via WebSockets. Clients send text/audio to the ADK agent and receive streamed text/audio responses.
+
+Core functions: 1. Initialize/manage ADK agent sessions. 2. Handle client WebSocket connections. 3. Relay client messages to the ADK agent. 4. Stream ADK agent responses (text/audio) to clients.
+
+ADK Streaming Setup¶
+
+import os
+import json
+import asyncio
+import base64
+
+from pathlib import Path
+from dotenv import load_dotenv
+
+from google.genai.types import (
+    Part,
+    Content,
+    Blob,
+)
+
+from google.adk.runners import Runner
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.run_config import RunConfig
+from google.adk.sessions.in_memory_session_service import InMemorySessionService
+
+from fastapi import FastAPI, WebSocket
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+
+from google_search_agent.agent import root_agent
+Imports: Includes standard Python libraries, dotenv for environment variables, Google ADK, and FastAPI.
+load_dotenv(): Loads environment variables.
+APP_NAME: Application identifier for ADK.
+session_service = InMemorySessionService(): Initializes an in-memory ADK session service, suitable for single-instance or development use. Production might use a persistent store.
+start_agent_session(session_id, is_audio=False)¶
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+This function initializes an ADK agent live session.
+
+Parameter	Type	Description
+user_id	str	Unique client identifier.
+is_audio	bool	True for audio responses, False for text (default).
+Key Steps: 1. Create Runner: Instantiates the ADK runner for the root_agent. 2. Create Session: Establishes an ADK session. 3. Set Response Modality: Configures agent response as "AUDIO" or "TEXT". 4. Create LiveRequestQueue: Creates a queue for client inputs to the agent. 5. Start Agent Session: runner.run_live(...) starts the agent, returning: * live_events: Asynchronous iterable for agent events (text, audio, completion). * live_request_queue: Queue to send data to the agent.
+
+Returns: (live_events, live_request_queue).
+
+agent_to_client_messaging(websocket, live_events)¶
+
+async def agent_to_client_messaging(websocket, live_events):
+    """Agent to client communication"""
+    while True:
+        async for event in live_events:
+
+            # If the turn complete or interrupted, send it
+            if event.turn_complete or event.interrupted:
+                message = {
+                    "turn_complete": event.turn_complete,
+                    "interrupted": event.interrupted,
+                }
+                await websocket.send_text(json.dumps(message))
+                print(f"[AGENT TO CLIENT]: {message}")
+                continue
+
+            # Read the Content and its first Part
+            part: Part = (
+                event.content and event.content.parts and event.content.parts[0]
+            )
+            if not part:
+                continue
+
+            # If it's audio, send Base64 encoded audio data
+            is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+            if is_audio:
+                audio_data = part.inline_data and part.inline_data.data
+                if audio_data:
+                    message = {
+                        "mime_type": "audio/pcm",
+                        "data": base64.b64encode(audio_data).decode("ascii")
+                    }
+                    await websocket.send_text(json.dumps(message))
+                    print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                    continue
+
+            # If it's text and a parial text, send it
+            if part.text and event.partial:
+                message = {
+                    "mime_type": "text/plain",
+                    "data": part.text
+                }
+                await websocket.send_text(json.dumps(message))
+                print(f"[AGENT TO CLIENT]: text/plain: {message}")
+This asynchronous function streams ADK agent events to the WebSocket client.
+
+Logic: 1. Iterates through live_events from the agent. 2. Turn Completion/Interruption: Sends status flags to the client. 3. Content Processing: * Extracts the first Part from event content. * Audio Data: If audio (PCM), Base64 encodes and sends it as JSON: { "mime_type": "audio/pcm", "data": "<base64_audio>" }. * Text Data: If partial text, sends it as JSON: { "mime_type": "text/plain", "data": "<partial_text>" }. 4. Logs messages.
+
+client_to_agent_messaging(websocket, live_request_queue)¶
+
+async def client_to_agent_messaging(websocket, live_request_queue):
+    """Client to agent communication"""
+    while True:
+        # Decode JSON message
+        message_json = await websocket.receive_text()
+        message = json.loads(message_json)
+        mime_type = message["mime_type"]
+        data = message["data"]
+
+        # Send the message to the agent
+        if mime_type == "text/plain":
+            # Send a text message
+            content = Content(role="user", parts=[Part.from_text(text=data)])
+            live_request_queue.send_content(content=content)
+            print(f"[CLIENT TO AGENT]: {data}")
+        elif mime_type == "audio/pcm":
+            # Send an audio data
+            decoded_data = base64.b64decode(data)
+            live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        else:
+            raise ValueError(f"Mime type not supported: {mime_type}")
+This asynchronous function relays messages from the WebSocket client to the ADK agent.
+
+Logic: 1. Receives and parses JSON messages from the WebSocket, expecting: { "mime_type": "text/plain" | "audio/pcm", "data": "<data>" }. 2. Text Input: For "text/plain", sends Content to agent via live_request_queue.send_content(). 3. Audio Input: For "audio/pcm", decodes Base64 data, wraps in Blob, and sends via live_request_queue.send_realtime(). 4. Raises ValueError for unsupported MIME types. 5. Logs messages.
+
+FastAPI Web Application¶
+
+app = FastAPI()
+
+STATIC_DIR = Path("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+
+@app.get("/")
+async def root():
+    """Serves the index.html"""
+    return FileResponse(os.path.join(STATIC_DIR, "index.html"))
+
+
+@app.websocket("/ws/{user_id}")
+async def websocket_endpoint(websocket: WebSocket, user_id: int, is_audio: str):
+    """Client websocket endpoint"""
+
+    # Wait for client connection
+    await websocket.accept()
+    print(f"Client #{user_id} connected, audio mode: {is_audio}")
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Start tasks
+    agent_to_client_task = asyncio.create_task(
+        agent_to_client_messaging(websocket, live_events)
+    )
+    client_to_agent_task = asyncio.create_task(
+        client_to_agent_messaging(websocket, live_request_queue)
+    )
+
+    # Wait until the websocket is disconnected or an error occurs
+    tasks = [agent_to_client_task, client_to_agent_task]
+    await asyncio.wait(tasks, return_when=asyncio.FIRST_EXCEPTION)
+
+    # Close LiveRequestQueue
+    live_request_queue.close()
+
+    # Disconnected
+    print(f"Client #{user_id} disconnected")
+app = FastAPI(): Initializes the application.
+Static Files: Serves files from the static directory under /static.
+@app.get("/") (Root Endpoint): Serves index.html.
+@app.websocket("/ws/{user_id}") (WebSocket Endpoint):
+Path Parameters: user_id (int) and is_audio (str: "true"/"false").
+Connection Handling:
+Accepts WebSocket connection.
+Calls start_agent_session() using user_id and is_audio.
+Concurrent Messaging Tasks: Creates and runs agent_to_client_messaging and client_to_agent_messaging concurrently using asyncio.gather. These tasks handle bidirectional message flow.
+Logs client connection and disconnection.
+How It Works (Overall Flow)¶
+Client connects to ws://<server>/ws/<user_id>?is_audio=<true_or_false>.
+Server's websocket_endpoint accepts, starts ADK session (start_agent_session).
+Two asyncio tasks manage communication:
+client_to_agent_messaging: Client WebSocket messages -> ADK live_request_queue.
+agent_to_client_messaging: ADK live_events -> Client WebSocket.
+Bidirectional streaming continues until disconnection or error.
+5. Client code overview¶
+The JavaScript app.js (in app/static/js) manages client-side interaction with the ADK Streaming WebSocket backend. It handles sending text/audio and receiving/displaying streamed responses.
+
+Key functionalities: 1. Manage WebSocket connection. 2. Handle text input. 3. Capture microphone audio (Web Audio API, AudioWorklets). 4. Send text/audio to backend. 5. Receive and render text/audio agent responses. 6. Manage UI.
+
+Prerequisites¶
+HTML Structure: Requires specific element IDs (e.g., messageForm, message, messages, sendButton, startAudioButton).
+Backend Server: The Python FastAPI server must be running.
+Audio Worklet Files: audio-player.js and audio-recorder.js for audio processing.
+WebSocket Handling¶
+
+// Connect the server with a WebSocket connection
+const sessionId = Math.random().toString().substring(10);
+const ws_url =
+  "ws://" + window.location.host + "/ws/" + sessionId;
+let websocket = null;
+let is_audio = false;
+
+// Get DOM elements
+const messageForm = document.getElementById("messageForm");
+const messageInput = document.getElementById("message");
+const messagesDiv = document.getElementById("messages");
+let currentMessageId = null;
+
+// WebSocket handlers
+function connectWebsocket() {
+  // Connect websocket
+  websocket = new WebSocket(ws_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  websocket.onopen = function () {
+    // Connection opened messages
+    console.log("WebSocket connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  websocket.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+  };
+
+  // Handle connection close
+  websocket.onclose = function () {
+    console.log("WebSocket connection closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectWebsocket();
+    }, 5000);
+  };
+
+  websocket.onerror = function (e) {
+    console.log("WebSocket error: ", e);
+  };
+}
+connectWebsocket();
+
+// Add submit handler to the form
+function addSubmitHandler() {
+  messageForm.onsubmit = function (e) {
+    e.preventDefault();
+    const message = messageInput.value;
+    if (message) {
+      const p = document.createElement("p");
+      p.textContent = "> " + message;
+      messagesDiv.appendChild(p);
+      messageInput.value = "";
+      sendMessage({
+        mime_type: "text/plain",
+        data: message,
+      });
+      console.log("[CLIENT TO AGENT] " + message);
+    }
+    return false;
+  };
+}
+
+// Send a message to the server as a JSON string
+function sendMessage(message) {
+  if (websocket && websocket.readyState == WebSocket.OPEN) {
+    const messageJson = JSON.stringify(message);
+    websocket.send(messageJson);
+  }
+}
+
+// Decode Base64 data to Array
+function base64ToArray(base64) {
+  const binaryString = window.atob(base64);
+  const len = binaryString.length;
+  const bytes = new Uint8Array(len);
+  for (let i = 0; i < len; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
+Connection Setup: Generates sessionId, constructs ws_url. is_audio flag (initially false) appends ?is_audio=true to URL when active. connectWebsocket() initializes the connection.
+websocket.onopen: Enables send button, updates UI, calls addSubmitHandler().
+websocket.onmessage: Parses incoming JSON from server.
+Turn Completion: Resets currentMessageId if agent turn is complete.
+Audio Data (audio/pcm): Decodes Base64 audio (base64ToArray()) and sends to audioPlayerNode for playback.
+Text Data (text/plain): If new turn (currentMessageId is null), creates new <p>. Appends received text to the current message paragraph for streaming effect. Scrolls messagesDiv.
+websocket.onclose: Disables send button, updates UI, attempts auto-reconnection after 5s.
+websocket.onerror: Logs errors.
+Initial Connection: connectWebsocket() is called on script load.
+DOM Interaction & Message Submission¶
+Element Retrieval: Fetches required DOM elements.
+addSubmitHandler(): Attached to messageForm's submit. Prevents default submission, gets text from messageInput, displays user message, clears input, and calls sendMessage() with { mime_type: "text/plain", data: messageText }.
+sendMessage(messagePayload): Sends JSON stringified messagePayload if WebSocket is open.
+Audio Handling¶
+
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+// Import the audio worklets
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet } from "./audio-recorder.js";
+
+// Start audio
+function startAudio() {
+  // Start audio output
+  startAudioPlayerWorklet().then(([node, ctx]) => {
+    audioPlayerNode = node;
+    audioPlayerContext = ctx;
+  });
+  // Start audio input
+  startAudioRecorderWorklet(audioRecorderHandler).then(
+    ([node, ctx, stream]) => {
+      audioRecorderNode = node;
+      audioRecorderContext = ctx;
+      micStream = stream;
+    }
+  );
+}
+
+// Start the audio only when the user clicked the button
+// (due to the gesture requirement for the Web Audio API)
+const startAudioButton = document.getElementById("startAudioButton");
+startAudioButton.addEventListener("click", () => {
+  startAudioButton.disabled = true;
+  startAudio();
+  is_audio = true;
+  connectWebsocket(); // reconnect with the audio mode
+});
+
+// Audio recorder handler
+function audioRecorderHandler(pcmData) {
+  // Send the pcm data as base64
+  sendMessage({
+    mime_type: "audio/pcm",
+    data: arrayBufferToBase64(pcmData),
+  });
+  console.log("[CLIENT TO AGENT] sent %s bytes", pcmData.byteLength);
+}
+
+// Encode an array buffer with Base64
+function arrayBufferToBase64(buffer) {
+  let binary = "";
+  const bytes = new Uint8Array(buffer);
+  const len = bytes.byteLength;
+  for (let i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
+Audio Worklets: Uses AudioWorkletNode via audio-player.js (for playback) and audio-recorder.js (for capture).
+State Variables: Store AudioContexts and WorkletNodes (e.g., audioPlayerNode).
+startAudio(): Initializes player and recorder worklets. Passes audioRecorderHandler as callback to recorder.
+"Start Audio" Button (startAudioButton):
+Requires user gesture for Web Audio API.
+On click: disables button, calls startAudio(), sets is_audio = true, then calls connectWebsocket() to reconnect in audio mode (URL includes ?is_audio=true).
+audioRecorderHandler(pcmData): Callback from recorder worklet with PCM audio chunks. Encodes pcmData to Base64 (arrayBufferToBase64()) and sends to server via sendMessage() with mime_type: "audio/pcm".
+Helper Functions: base64ToArray() (server audio -> client player) and arrayBufferToBase64() (client mic audio -> server).
+How It Works (Client-Side Flow)¶
+Page Load: Establishes WebSocket in text mode.
+Text Interaction: User types/submits text; sent to server. Server text responses displayed, streamed.
+Switching to Audio Mode: "Start Audio" button click initializes audio worklets, sets is_audio=true, and reconnects WebSocket in audio mode.
+Audio Interaction: Recorder sends mic audio (Base64 PCM) to server. Server audio/text responses handled by websocket.onmessage for playback/display.
+Connection Management: Auto-reconnect on WebSocket close.
+Summary¶
+This article overviews the server and client code for a custom asynchronous web app built with ADK Streaming and FastAPI, enabling real-time, bidirectional voice and text communication.
+
+The Python FastAPI server code initializes ADK agent sessions, configured for text or audio responses. It uses a WebSocket endpoint to handle client connections. Asynchronous tasks manage bidirectional messaging: forwarding client text or Base64-encoded PCM audio to the ADK agent, and streaming text or Base64-encoded PCM audio responses from the agent back to the client.
+
+The client-side JavaScript code manages a WebSocket connection, which can be re-established to switch between text and audio modes. It sends user input (text or microphone audio captured via Web Audio API and AudioWorklets) to the server. Incoming messages from the server are processed: text is displayed (streamed), and Base64-encoded PCM audio is decoded and played using an AudioWorklet.
+
+Next steps for production¶
+When you will use the Streaming for ADK in production apps, you may want to consinder the following points:
+
+Deploy Multiple Instances: Run several instances of your FastAPI application instead of a single one.
+Implement Load Balancing: Place a load balancer in front of your application instances to distribute incoming WebSocket connections.
+Configure for WebSockets: Ensure the load balancer supports long-lived WebSocket connections and consider "sticky sessions" (session affinity) to route a client to the same backend instance, or design for stateless instances (see next point).
+Externalize Session State: Replace the InMemorySessionService for ADK with a distributed, persistent session store. This allows any server instance to handle any user's session, enabling true statelessness at the application server level and improving fault tolerance.
+Implement Health Checks: Set up robust health checks for your WebSocket server instances so the load balancer can automatically remove unhealthy instances from rotation.
+Utilize Orchestration: Consider using an orchestration platform like Kubernetes for automated deployment, scaling, self-healing, and management of your WebSocket server instances.
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/__init__.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/__init__.py
new file mode 100644
index 000000000..63bd45e6d
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/agent.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/agent.py
new file mode 100644
index 000000000..3c08c8c0c
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/agent.py
@@ -0,0 +1,11 @@
+from google.adk.agents import Agent
+
+root_agent = Agent(
+    name="greeting_agent",  #google_search_agent  
+    # https://ai.google.dev/gemini-api/docs/models
+    model= "gemini-2.0-flash-live-preview-04-09",  #"gemini-2.0-flash-exp", 
+    description="Greeting agent to talk to user in an engaging helpful and entertaining way",
+    instruction="""You are an interesting and engaging conversationalist and tallented story teller. 
+    You will ask the users name and instegate a conversation with 
+    user.""",
+)
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/agent.py_orig b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/agent.py_orig
new file mode 100644
index 000000000..b328c99bd
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/google_search_agent/agent.py_orig
@@ -0,0 +1,28 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   name="google_search_agent",
+   model="gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",
+   description="Agent to answer questions using Google Search.",
+   instruction="Answer the question using the Google Search tool.",
+   tools=[google_search],
+)
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/main.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/main.py
new file mode 100644
index 000000000..2849d805b
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/main.py
@@ -0,0 +1,195 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import json
+import asyncio
+import base64
+import warnings
+
+from pathlib import Path
+from dotenv import load_dotenv
+
+from google.genai.types import (
+    Part,
+    Content,
+    Blob,
+)
+
+from google.adk.runners import InMemoryRunner
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.run_config import RunConfig
+
+from fastapi import FastAPI, WebSocket
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+
+from google_search_agent.agent import root_agent
+
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+#
+# ADK Streaming
+#
+
+# Load Gemini API Key
+load_dotenv()
+
+APP_NAME = "ADK Streaming example"
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+
+
+async def agent_to_client_messaging(websocket, live_events):
+    """Agent to client communication"""
+    while True:
+        async for event in live_events:
+
+            # If the turn complete or interrupted, send it
+            if event.turn_complete or event.interrupted:
+                message = {
+                    "turn_complete": event.turn_complete,
+                    "interrupted": event.interrupted,
+                }
+                await websocket.send_text(json.dumps(message))
+                print(f"[AGENT TO CLIENT]: {message}")
+                continue
+
+            # Read the Content and its first Part
+            part: Part = (
+                event.content and event.content.parts and event.content.parts[0]
+            )
+            if not part:
+                continue
+
+            # If it's audio, send Base64 encoded audio data
+            is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+            if is_audio:
+                audio_data = part.inline_data and part.inline_data.data
+                if audio_data:
+                    message = {
+                        "mime_type": "audio/pcm",
+                        "data": base64.b64encode(audio_data).decode("ascii")
+                    }
+                    await websocket.send_text(json.dumps(message))
+                    print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                    continue
+
+            # If it's text and a parial text, send it
+            if part.text and event.partial:
+                message = {
+                    "mime_type": "text/plain",
+                    "data": part.text
+                }
+                await websocket.send_text(json.dumps(message))
+                print(f"[AGENT TO CLIENT]: text/plain: {message}")
+
+
+async def client_to_agent_messaging(websocket, live_request_queue):
+    """Client to agent communication"""
+    while True:
+        # Decode JSON message
+        message_json = await websocket.receive_text()
+        message = json.loads(message_json)
+        mime_type = message["mime_type"]
+        data = message["data"]
+
+        # Send the message to the agent
+        if mime_type == "text/plain":
+            # Send a text message
+            content = Content(role="user", parts=[Part.from_text(text=data)])
+            live_request_queue.send_content(content=content)
+            print(f"[CLIENT TO AGENT]: {data}")
+        elif mime_type == "audio/pcm":
+            # Send an audio data
+            decoded_data = base64.b64decode(data)
+            live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        else:
+            raise ValueError(f"Mime type not supported: {mime_type}")
+
+
+#
+# FastAPI web app
+#
+
+app = FastAPI()
+
+STATIC_DIR = Path("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+
+@app.get("/")
+async def root():
+    """Serves the index.html"""
+    return FileResponse(os.path.join(STATIC_DIR, "index.html"))
+
+
+@app.websocket("/ws/{user_id}")
+async def websocket_endpoint(websocket: WebSocket, user_id: int, is_audio: str):
+    """Client websocket endpoint"""
+
+    # Wait for client connection
+    await websocket.accept()
+    print(f"Client #{user_id} connected, audio mode: {is_audio}")
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Start tasks
+    agent_to_client_task = asyncio.create_task(
+        agent_to_client_messaging(websocket, live_events)
+    )
+    client_to_agent_task = asyncio.create_task(
+        client_to_agent_messaging(websocket, live_request_queue)
+    )
+
+    # Wait until the websocket is disconnected or an error occurs
+    tasks = [agent_to_client_task, client_to_agent_task]
+    await asyncio.wait(tasks, return_when=asyncio.FIRST_EXCEPTION)
+
+    # Close LiveRequestQueue
+    live_request_queue.close()
+
+    # Disconnected
+    print(f"Client #{user_id} disconnected")
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/index.html b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/index.html
new file mode 100644
index 000000000..2286c371e
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/index.html
@@ -0,0 +1,23 @@
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/app.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/app.js
new file mode 100644
index 000000000..8cc52e9e8
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/app.js
@@ -0,0 +1,272 @@
+/**
+* Copyright 2025 Google LLC
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+/**
+ * app.js: JS code for the adk-streaming sample app.
+ */
+
+/**
+ * WebSocket handling
+ */
+
+// Connect the server with a WebSocket connection
+const sessionId = Math.random().toString().substring(10);
+const ws_url =
+  "ws://" + window.location.host + "/ws/" + sessionId;
+let websocket = null;
+let is_audio = false;
+
+// Get DOM elements
+const messageForm = document.getElementById("messageForm");
+const messageInput = document.getElementById("message");
+const messagesDiv = document.getElementById("messages");
+let currentMessageId = null;
+
+// WebSocket handlers
+function connectWebsocket() {
+  // Connect websocket
+  websocket = new WebSocket(ws_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  websocket.onopen = function () {
+    // Connection opened messages
+    console.log("WebSocket connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  websocket.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // Check for interrupt message
+    if (
+      message_from_server.interrupted &&
+      message_from_server.interrupted === true
+    ) {
+      // Stop audio playback if it's playing
+      if (audioPlayerNode) {
+        audioPlayerNode.port.postMessage({ command: "endOfAudio" });
+      }
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+  };
+
+  // Handle connection close
+  websocket.onclose = function () {
+    console.log("WebSocket connection closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectWebsocket();
+    }, 5000);
+  };
+
+  websocket.onerror = function (e) {
+    console.log("WebSocket error: ", e);
+  };
+}
+connectWebsocket();
+
+// Add submit handler to the form
+function addSubmitHandler() {
+  messageForm.onsubmit = function (e) {
+    e.preventDefault();
+    const message = messageInput.value;
+    if (message) {
+      const p = document.createElement("p");
+      p.textContent = "> " + message;
+      messagesDiv.appendChild(p);
+      messageInput.value = "";
+      sendMessage({
+        mime_type: "text/plain",
+        data: message,
+      });
+      console.log("[CLIENT TO AGENT] " + message);
+    }
+    return false;
+  };
+}
+
+// Send a message to the server as a JSON string
+function sendMessage(message) {
+  if (websocket && websocket.readyState == WebSocket.OPEN) {
+    const messageJson = JSON.stringify(message);
+    websocket.send(messageJson);
+  }
+}
+
+// Decode Base64 data to Array
+function base64ToArray(base64) {
+  const binaryString = window.atob(base64);
+  const len = binaryString.length;
+  const bytes = new Uint8Array(len);
+  for (let i = 0; i < len; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
+
+/**
+ * Audio handling
+ */
+
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+// Audio buffering for 0.2s intervals
+let audioBuffer = [];
+let bufferTimer = null;
+
+// Import the audio worklets
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet } from "./audio-recorder.js";
+
+// Start audio
+function startAudio() {
+  // Start audio output
+  startAudioPlayerWorklet().then(([node, ctx]) => {
+    audioPlayerNode = node;
+    audioPlayerContext = ctx;
+  });
+  // Start audio input
+  startAudioRecorderWorklet(audioRecorderHandler).then(
+    ([node, ctx, stream]) => {
+      audioRecorderNode = node;
+      audioRecorderContext = ctx;
+      micStream = stream;
+    }
+  );
+}
+
+// Start the audio only when the user clicked the button
+// (due to the gesture requirement for the Web Audio API)
+const startAudioButton = document.getElementById("startAudioButton");
+startAudioButton.addEventListener("click", () => {
+  startAudioButton.disabled = true;
+  startAudio();
+  is_audio = true;
+  connectWebsocket(); // reconnect with the audio mode
+});
+
+// Audio recorder handler
+function audioRecorderHandler(pcmData) {
+  // Add audio data to buffer
+  audioBuffer.push(new Uint8Array(pcmData));
+  
+  // Start timer if not already running
+  if (!bufferTimer) {
+    bufferTimer = setInterval(sendBufferedAudio, 200); // 0.2 seconds
+  }
+}
+
+// Send buffered audio data every 0.2 seconds
+function sendBufferedAudio() {
+  if (audioBuffer.length === 0) {
+    return;
+  }
+  
+  // Calculate total length
+  let totalLength = 0;
+  for (const chunk of audioBuffer) {
+    totalLength += chunk.length;
+  }
+  
+  // Combine all chunks into a single buffer
+  const combinedBuffer = new Uint8Array(totalLength);
+  let offset = 0;
+  for (const chunk of audioBuffer) {
+    combinedBuffer.set(chunk, offset);
+    offset += chunk.length;
+  }
+  
+  // Send the combined audio data
+  sendMessage({
+    mime_type: "audio/pcm",
+    data: arrayBufferToBase64(combinedBuffer.buffer),
+  });
+  console.log("[CLIENT TO AGENT] sent %s bytes", combinedBuffer.byteLength);
+  
+  // Clear the buffer
+  audioBuffer = [];
+}
+
+// Stop audio recording and cleanup
+function stopAudioRecording() {
+  if (bufferTimer) {
+    clearInterval(bufferTimer);
+    bufferTimer = null;
+  }
+  
+  // Send any remaining buffered audio
+  if (audioBuffer.length > 0) {
+    sendBufferedAudio();
+  }
+}
+
+// Encode an array buffer with Base64
+function arrayBufferToBase64(buffer) {
+  let binary = "";
+  const bytes = new Uint8Array(buffer);
+  const len = bytes.byteLength;
+  for (let i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/audio-player.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/audio-player.js
new file mode 100644
index 000000000..f7f235e01
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+    
+    
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+    
+    // 3. Create an AudioWorkletNode   
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/audio-recorder.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..876932a2f
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-player-processor copy.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-player-processor copy.js
new file mode 100644
index 000000000..9ef17a556
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-player-processor copy.js	
@@ -0,0 +1,75 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` ~128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex != this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-player-processor.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..9ef17a556
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-player-processor.js
@@ -0,0 +1,75 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` ~128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex != this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-recorder-processor.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..585d67491
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/adk-streaming_websockets-works/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/app/google_search_agent/__init__.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/app/google_search_agent/__init__.py
new file mode 100644
index 000000000..63bd45e6d
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/app/google_search_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/app/google_search_agent/agent.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/app/google_search_agent/agent.py
new file mode 100644
index 000000000..5807eb13c
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming-orig-works/app/google_search_agent/agent.py
@@ -0,0 +1,16 @@
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   # A unique name for the agent.
+   name="basic_search_agent",
+   # The Large Language Model (LLM) that agent will use.
+   model= "gemini-2.0-flash-exp", #"gemini-2.0-flash-live-preview-04-09",
+   # model="gemini-2.0-flash-live-001",  # New streaming model version as of Feb 2025
+   # A short description of the agent's purpose.
+   description="Agent to answer questions using Google Search.",
+   # Instructions to set the agent's behavior.
+   instruction="You are an expert researcher. You always stick to the facts.",
+   # Add google_search tool to perform grounding with Google search.
+   tools=[google_search]
+)
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/README.md b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/README.md
new file mode 100644
index 000000000..f0cd25f9e
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/README.md
@@ -0,0 +1,582 @@
+this document is pasted from url:  https://google.github.io/adk-docs/streaming/custom-streaming/
+IMPORTANT NOTE:  I ran: pip install "fastapi[standard]"   
+     app/main.py uses fastapi    
+fastAPI documentation:  https://fastapi.tiangolo.com/  
+
+*EDITS  - I had to remove the google_search_tool calls due to errors:  "RESOURCE_EXHAUSTED: Maximum google search tool usage exceeded."  This error occured when streaming with this tool in other examples too.
+
+-Verified as working with audio conversation and allows for interuptions.   However the responeses seems limited to be only a couple lines at the moment.
+
+Custom Audio Streaming app (SSE)¶
+This article overviews the server and client code for a custom asynchronous web app built with ADK Streaming and FastAPI, enabling real-time, bidirectional audio and text communication with Server-Sent Events (SSE). The key features are:
+
+Server-Side (Python/FastAPI): - FastAPI + ADK integration - Server-Sent Events for real-time streaming - Session management with isolated user contexts - Support for both text and audio communication modes - Google Search tool integration for grounded responses
+
+Client-Side (JavaScript/Web Audio API): - Real-time bidirectional communication via SSE and HTTP POST - Professional audio processing using AudioWorklet processors - Seamless mode switching between text and audio - Automatic reconnection and error handling - Base64 encoding for audio data transmission
+
+1. Install ADK¶
+Create & Activate Virtual Environment (Recommended):
+
+
+# Create
+python -m venv .venv
+# Activate (each new terminal)
+# macOS/Linux: source .venv/bin/activate
+# Windows CMD: .venv\Scripts\activate.bat
+# Windows PowerShell: .venv\Scripts\Activate.ps1
+Install ADK:
+
+
+pip install google-adk==1.0.0
+Set SSL_CERT_FILE variable with the following command.     this is required everytime you start a new terminal
+
+    in powershell:   $env:SSL_CERT_FILE = (python -m certifi)
+Custom: ALternately you can add this to .venv\Scripts\Activate.ps1 at end of file  
+      # CUSTOM ADDITON: Automatically set the SSL_CERT_FILE path using the certifi package
+      # to prevent potential SSL verification errors with Google Cloud APIs.
+      $env:SSL_CERT_FILE = $(python -m certifi)
+  When you activate the .venv this will run
+
+
+
+Download the sample code:
+
+
+git clone --no-checkout https://github.com/google/adk-docs.git
+cd adk-docs
+git sparse-checkout init --cone
+git sparse-checkout set examples/python/snippets/streaming/adk-streaming
+git checkout main
+cd examples/python/snippets/streaming/adk-streaming/app
+This sample code has the following files and folders:
+
+
+adk-streaming/
+└── app/ # the web app folder
+    ├── .env # Gemini API key / Google Cloud Project ID
+    ├── main.py # FastAPI web app
+    ├── static/ # Static content folder
+    |   ├── js # JavaScript files folder (includes app.js)
+    |   └── index.html # The web client page
+    └── google_search_agent/ # Agent folder
+        ├── __init__.py # Python package
+        └── agent.py # Agent definition
+2. Set up the platform¶
+To run the sample app, choose a platform from either Google AI Studio or Google Cloud Vertex AI:
+
+
+Gemini - Google AI Studio
+Gemini - Google Cloud Vertex AI
+Get an API key from Google AI Studio.
+Open the .env file located inside (app/) and copy-paste the following code.
+
+.env
+
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
+Replace PASTE_YOUR_ACTUAL_API_KEY_HERE with your actual API KEY.
+
+
+3. Interact with Your Streaming app¶
+1. Navigate to the Correct Directory:
+
+To run your agent effectively, make sure you are in the app folder (adk-streaming/app)
+
+2. Start the Fast API: Run the following command to start CLI interface with
+
+
+uvicorn main:app --reload
+3. Access the app with the text mode: Once the app starts, the terminal will display a local URL (e.g., http://localhost:8000). Click this link to open the UI in your browser.
+
+Now you should see the UI like this:
+
+ADK Streaming app
+
+Try asking a question What time is it now?. The agent will use Google Search to respond to your queries. You would notice that the UI shows the agent's response as streaming text. You can also send messages to the agent at any time, even while the agent is still responding. This demonstrates the bidirectional communication capability of ADK Streaming.
+
+4. Access the app with the audio mode: Now click the Start Audio button. The app reconnects with the server in an audio mode, and the UI will show the following dialog for the first time:
+
+ADK Streaming app
+
+Click Allow while visiting the site, then you will see the microphone icon will be shown at the top of the browser:
+
+ADK Streaming app
+
+Now you can talk to the agent with voice. Ask questions like What time is it now? with voice and you will hear the agent responding in voice too. As Streaming for ADK supports multiple languages, it can also respond to question in the supported languages.
+
+5. Check console logs
+
+If you are using the Chrome browser, use the right click and select Inspect to open the DevTools. On the Console, you can see the incoming and outgoing audio data such as [CLIENT TO AGENT] and [AGENT TO CLIENT], representing the audio data streaming in and out between the browser and the server.
+
+At the same time, in the app server console, you should see something like this:
+
+
+Client #90766266 connected via SSE, audio mode: false
+INFO:     127.0.0.1:52692 - "GET /events/90766266?is_audio=false HTTP/1.1" 200 OK
+[CLIENT TO AGENT]: hi
+INFO:     127.0.0.1:52696 - "POST /send/90766266 HTTP/1.1" 200 OK
+[AGENT TO CLIENT]: text/plain: {'mime_type': 'text/plain', 'data': 'Hi'}
+[AGENT TO CLIENT]: text/plain: {'mime_type': 'text/plain', 'data': ' there! How can I help you today?\n'}
+[AGENT TO CLIENT]: {'turn_complete': True, 'interrupted': None}
+These console logs are important in case you develop your own streaming application. In many cases, the communication failure between the browser and server becomes a major cause for the streaming application bugs.
+
+6. Troubleshooting tips
+
+When gemini-2.0-flash-exp model doesn't work: If you see any errors on the app server console with regard to gemini-2.0-flash-exp model availability, try replacing it with gemini-2.0-flash-live-001 on app/google_search_agent/agent.py at line 6.
+4. Agent definition¶
+The agent definition code agent.py in the google_search_agent folder is where the agent's logic is written:
+
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   name="google_search_agent",
+   model="gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",
+   description="Agent to answer questions using Google Search.",
+   instruction="Answer the question using the Google Search tool.",
+   tools=[google_search],
+)
+Notice how easily you integrated grounding with Google Search capabilities. The Agent class and the google_search tool handle the complex interactions with the LLM and grounding with the search API, allowing you to focus on the agent's purpose and behavior.
+
+intro_components.png
+
+The server and client architecture enables real-time, bidirectional communication between web clients and AI agents with proper session isolation and resource management.
+
+5. Server side code overview¶
+The FastAPI server provides real-time communication between web clients and the AI agent.
+
+Bidirectional communication overview¶
+Client-to-Agent Flow:¶
+Connection Establishment - Client opens SSE connection to /events/{user_id}, triggering session creation and storing request queue in active_sessions
+Message Transmission - Client sends POST to /send/{user_id} with JSON payload containing mime_type and data
+Queue Processing - Server retrieves session's live_request_queue and forwards message to agent via send_content() or send_realtime()
+Agent-to-Client Flow:¶
+Event Generation - Agent processes requests and generates events through live_events async generator
+Stream Processing - agent_to_client_sse() filters events and formats them as SSE-compatible JSON
+Real-time Delivery - Events stream to client via persistent HTTP connection with proper SSE headers
+Session Management:¶
+Per-User Isolation - Each user gets unique session stored in active_sessions dict
+Lifecycle Management - Sessions auto-cleanup on disconnect with proper resource disposal
+Concurrent Support - Multiple users can have simultaneous active sessions
+Error Handling:¶
+Session Validation - POST requests validate session existence before processing
+Stream Resilience - SSE streams handle exceptions and perform cleanup automatically
+Connection Recovery - Clients can reconnect by re-establishing SSE connection
+Agent Session Management¶
+The start_agent_session() function creates isolated AI agent sessions:
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+InMemoryRunner Setup - Creates a runner instance that manages the agent lifecycle in memory, with the app name "ADK Streaming example" and the Google Search agent.
+
+Session Creation - Uses runner.session_service.create_session() to establish a unique session per user ID, enabling multiple concurrent users.
+
+Response Modality Configuration - Sets RunConfig with either "AUDIO" or "TEXT" modality based on the is_audio parameter, determining output format.
+
+LiveRequestQueue - Creates a bidirectional communication channel that queues incoming requests and enables real-time message passing between client and agent.
+
+Live Events Stream - runner.run_live() returns an async generator that yields real-time events from the agent, including partial responses, turn completions, and interruptions.
+
+Server-Sent Events (SSE) Streaming¶
+The agent_to_client_sse() function handles real-time streaming from agent to client:
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+Event Processing Loop - Iterates through live_events async generator, processing each event as it arrives from the agent.
+
+Turn Management - Detects conversation turn completion or interruption events and sends JSON messages with turn_complete and interrupted flags to signal conversation state changes.
+
+Content Part Extraction - Extracts the first Part from event content, which contains either text or audio data.
+
+Audio Streaming - Handles PCM audio data by:
+
+Detecting audio/pcm MIME type in inline_data
+Base64 encoding raw audio bytes for JSON transmission
+Sending with mime_type and data fields
+
+Text Streaming - Processes partial text responses by sending incremental text updates as they're generated, enabling real-time typing effects.
+
+SSE Format - All data is formatted as data: {json}\n\n following SSE specification for browser EventSource API compatibility.
+
+HTTP Endpoints and Routing¶
+Root Endpoint¶
+GET / - Serves static/index.html as the main application interface using FastAPI's FileResponse.
+
+SSE Events Endpoint¶
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+GET /events/{user_id} - Establishes persistent SSE connection:
+
+Parameters - Takes user_id (int) and optional is_audio query parameter (defaults to "false")
+
+Session Initialization - Calls start_agent_session() and stores the live_request_queue in active_sessions dict using user_id as key
+
+StreamingResponse - Returns StreamingResponse with:
+
+event_generator() async function that wraps agent_to_client_sse()
+MIME type: text/event-stream
+CORS headers for cross-origin access
+Cache-control headers to prevent caching
+
+Cleanup Logic - Handles connection termination by closing the request queue and removing from active sessions, with error handling for stream interruptions.
+
+Message Sending Endpoint¶
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
+POST /send/{user_id} - Receives client messages:
+
+Session Lookup - Retrieves live_request_queue from active_sessions or returns error if session doesn't exist
+
+Message Processing - Parses JSON with mime_type and data fields:
+
+Text Messages - Creates Content with Part.from_text() and sends via send_content()
+Audio Messages - Base64 decodes PCM data and sends via send_realtime() with Blob
+
+Error Handling - Returns appropriate error responses for unsupported MIME types or missing sessions.
+
+6. Client side code overview¶
+The client-side consists of a web interface with real-time communication and audio capabilities:
+
+HTML Interface (static/index.html)¶
+
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
+Simple web interface with: - Messages Display - Scrollable div for conversation history - Text Input Form - Input field and send button for text messages - Audio Control - Button to enable audio mode and microphone access
+
+Main Application Logic (static/js/app.js)¶
+Session Management (app.js)¶
+
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let is_audio = false;
+Random Session ID - Generates unique session ID for each browser instance
+URL Construction - Builds SSE and send endpoints with session ID
+Audio Mode Flag - Tracks whether audio mode is enabled
+Server-Sent Events Connection (app.js)¶
+connectSSE() function handles real-time server communication:
+
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    ...
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+EventSource Setup - Creates SSE connection with audio mode parameter
+Connection Handlers:
+onopen - Enables send button and form submission when connected
+onmessage - Processes incoming messages from agent
+onerror - Handles disconnections with auto-reconnect after 5 seconds
+Message Processing (app.js)¶
+Handles different message types from server:
+
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+Turn Management - Detects turn_complete to reset message state
+Audio Playback - Decodes Base64 PCM data and sends to audio worklet
+Text Display - Creates new message elements and appends partial text updates for real-time typing effect
+Message Sending (app.js)¶
+sendMessage() function sends data to server:
+
+
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+HTTP POST - Sends JSON payload to /send/{session_id} endpoint
+Error Handling - Logs failed requests and network errors
+Message Format - Standardized {mime_type, data} structure
+Audio Player (static/js/audio-player.js)¶
+startAudioPlayerWorklet() function:
+
+AudioContext Setup - Creates context with 24kHz sample rate for playback
+Worklet Loading - Loads PCM player processor for audio handling
+Audio Pipeline - Connects worklet node to audio destination (speakers)
+Audio Recorder (static/js/audio-recorder.js)¶
+startAudioRecorderWorklet() function:
+
+AudioContext Setup - Creates context with 16kHz sample rate for recording
+Microphone Access - Requests user media permissions for audio input
+Audio Processing - Connects microphone to recorder worklet
+Data Conversion - Converts Float32 samples to 16-bit PCM format
+Audio Worklet Processors¶
+PCM Player Processor (static/js/pcm-player-processor.js)¶
+PCMPlayerProcessor class handles audio playback:
+
+Ring Buffer - Circular buffer for 180 seconds of 24kHz audio
+Data Ingestion - Converts Int16 to Float32 and stores in buffer
+Playback Loop - Continuously reads from buffer to output channels
+Overflow Handling - Overwrites oldest samples when buffer is full
+PCM Recorder Processor (static/js/pcm-recorder-processor.js)¶
+PCMProcessor class captures microphone input:
+
+Audio Input - Processes incoming audio frames
+Data Transfer - Copies Float32 samples and posts to main thread via message port
+Mode Switching:¶
+Audio Activation - "Start Audio" button enables microphone and reconnects SSE with audio flag
+Seamless Transition - Closes existing connection and establishes new audio-enabled session
+The client architecture enables seamless real-time communication with both text and audio modalities, using modern web APIs for professional-grade audio processing.
+
+Summary¶
+This application demonstrates a complete real-time AI agent system with the following key features:
+
+Architecture Highlights: - Real-time: Streaming responses with partial text updates and continuous audio - Robust: Comprehensive error handling and automatic recovery mechanisms - Modern: Uses latest web standards (AudioWorklet, SSE, ES6 modules)
+
+The system provides a foundation for building sophisticated AI applications that require real-time interaction, web search capabilities, and multimedia communication.
+
+Next steps for production¶
+To deploy this system in a production environment, consider implementing the following improvements:
+
+Security¶
+Authentication: Replace random session IDs with proper user authentication
+API Key Security: Use environment variables or secret management services
+HTTPS: Enforce TLS encryption for all communications
+Rate Limiting: Prevent abuse and control API costs
+Scalability¶
+Persistent Storage: Replace in-memory sessions with a persistent session
+Load Balancing: Support multiple server instances with shared session state
+Audio Optimization: Implement compression to reduce bandwidth usage
+Monitoring¶
+Error Tracking: Monitor and alert on system failures
+API Cost Monitoring: Track Google Search and Gemini usage to prevent budget overruns
+Performance Metrics: Monitor response times and audio latency
+Infrastructure¶
+Containerization: Package with Docker for consistent deployments with Cloud Run or Agent Engine
+Health Checks: Implement endpoint monitoring for uptime tracking
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/__init__.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/__init__.py
new file mode 100644
index 000000000..63bd45e6d
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
\ No newline at end of file
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/agent.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/agent.py
new file mode 100644
index 000000000..da226ac97
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/agent.py
@@ -0,0 +1,12 @@
+from google.adk.agents import Agent
+
+root_agent = Agent(
+    name="google_search_agent",  #google_search_agent  
+    # https://ai.google.dev/gemini-api/docs/models
+    model= "gemini-2.0-flash-live-preview-04-09",  #"gemini-2.0-flash-exp", 
+    description="Greeting agent",
+    instruction="""
+    You are an interesting and engaging conversationalist. you will ask the users name and instegate a conversation with 
+    user.
+    """,
+)
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/agent.py_orig b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/agent.py_orig
new file mode 100644
index 000000000..015977dc3
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/google_search_agent/agent.py_orig
@@ -0,0 +1,30 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   # A unique name for the agent.
+   name="google_search_agent",
+   # The Large Language Model (LLM) that agent will use.
+   model= "gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",  "gemini-2.0-flash-live-preview-04-09",
+   # A short description of the agent's purpose.
+   description="Agent to answer questions using Google Search.",
+   # Instructions to set the agent's behavior.
+   instruction="Answer the question using the Google Search tool.",
+   # Add google_search tool to perform grounding with Google search.
+   tools=[google_search],
+)
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/main.py b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/main.py
new file mode 100644
index 000000000..72b24a99c
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/main.py
@@ -0,0 +1,222 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import json
+import base64
+import warnings
+
+from pathlib import Path
+from dotenv import load_dotenv
+
+from google.genai.types import (
+    Part,
+    Content,
+    Blob,
+)
+
+from google.adk.runners import InMemoryRunner
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.run_config import RunConfig
+
+from fastapi import FastAPI, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+from google_search_agent.agent import root_agent
+
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+#
+# ADK Streaming
+#
+
+# Load Gemini API Key
+load_dotenv()
+
+APP_NAME = "ADK Streaming example"
+USER_ID = "user1"  # Replace with actual user ID
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=USER_ID,  # user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+
+
+#
+# FastAPI web app
+#
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+STATIC_DIR = Path("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+# Store active sessions
+active_sessions = {}
+
+
+@app.get("/")
+async def root():
+    """Serves the index.html"""
+    return FileResponse(os.path.join(STATIC_DIR, "index.html"))
+
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/index.html b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/index.html
new file mode 100644
index 000000000..2286c371e
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/index.html
@@ -0,0 +1,23 @@
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/app.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/app.js
new file mode 100644
index 000000000..31f5986fb
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/app.js
@@ -0,0 +1,218 @@
+/**
+ * app.js: JS code for the adk-streaming sample app.
+ */
+
+/**
+ * SSE (Server-Sent Events) handling
+ */
+
+// Connect the server with SSE
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let eventSource = null;
+let is_audio = false;
+
+// Get DOM elements
+const messageForm = document.getElementById("messageForm");
+const messageInput = document.getElementById("message");
+const messagesDiv = document.getElementById("messages");
+let currentMessageId = null;
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // Check for interrupt message
+    if (
+      message_from_server.interrupted &&
+      message_from_server.interrupted === true
+    ) {
+      // Stop audio playback if it's playing
+      if (audioPlayerNode) {
+        audioPlayerNode.port.postMessage({ command: "endOfAudio" });
+      }
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+connectSSE();
+
+// Add submit handler to the form
+function addSubmitHandler() {
+  messageForm.onsubmit = function (e) {
+    e.preventDefault();
+    const message = messageInput.value;
+    if (message) {
+      const p = document.createElement("p");
+      p.textContent = "> " + message;
+      messagesDiv.appendChild(p);
+      messageInput.value = "";
+      sendMessage({
+        mime_type: "text/plain",
+        data: message,
+      });
+      console.log("[CLIENT TO AGENT] " + message);
+    }
+    return false;
+  };
+}
+
+// Send a message to the server via HTTP POST
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+    
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+
+// Decode Base64 data to Array
+function base64ToArray(base64) {
+  const binaryString = window.atob(base64);
+  const len = binaryString.length;
+  const bytes = new Uint8Array(len);
+  for (let i = 0; i < len; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
+
+/**
+ * Audio handling
+ */
+
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+// Import the audio worklets
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet } from "./audio-recorder.js";
+
+// Start audio
+function startAudio() {
+  // Start audio output
+  startAudioPlayerWorklet().then(([node, ctx]) => {
+    audioPlayerNode = node;
+    audioPlayerContext = ctx;
+  });
+  // Start audio input
+  startAudioRecorderWorklet(audioRecorderHandler).then(
+    ([node, ctx, stream]) => {
+      audioRecorderNode = node;
+      audioRecorderContext = ctx;
+      micStream = stream;
+    }
+  );
+}
+
+// Start the audio only when the user clicked the button
+// (due to the gesture requirement for the Web Audio API)
+const startAudioButton = document.getElementById("startAudioButton");
+startAudioButton.addEventListener("click", () => {
+  startAudioButton.disabled = true;
+  startAudio();
+  is_audio = true;
+  eventSource.close(); // close current connection
+  connectSSE(); // reconnect with the audio mode
+});
+
+// Audio recorder handler
+function audioRecorderHandler(pcmData) {
+  // Send the pcm data as base64
+  sendMessage({
+    mime_type: "audio/pcm",
+    data: arrayBufferToBase64(pcmData),
+  });
+  console.log("[CLIENT TO AGENT] sent %s bytes", pcmData.byteLength);
+}
+
+// Encode an array buffer with Base64
+function arrayBufferToBase64(buffer) {
+  let binary = "";
+  const bytes = new Uint8Array(buffer);
+  const len = bytes.byteLength;
+  for (let i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/audio-player.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/audio-player.js
new file mode 100644
index 000000000..af890517e
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+    
+    
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+    
+    // 3. Create an AudioWorkletNode   
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/audio-recorder.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..82806e6dd
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/pcm-player-processor.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..6d9dbf23b
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/pcm-player-processor.js
@@ -0,0 +1,76 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` ~128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex != this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
+
diff --git a/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/pcm-recorder-processor.js b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..b1c54bd31
--- /dev/null
+++ b/in_progress/adk_streaming_examples/WORKING-VERIFIED/adk-streaming_audio-works/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
diff --git a/in_progress/streaming-test/.gitignore b/in_progress/streaming-test/.gitignore
new file mode 100644
index 000000000..f20068ff7
--- /dev/null
+++ b/in_progress/streaming-test/.gitignore
@@ -0,0 +1,6 @@
+.venv/
+__pycache__/
+*.pyc
+.env
+instance/
+node_modules/
diff --git a/in_progress/streaming-test/README.md b/in_progress/streaming-test/README.md
new file mode 100644
index 000000000..eadc82ee2
--- /dev/null
+++ b/in_progress/streaming-test/README.md
@@ -0,0 +1,149 @@
+# Live Multimodal Streaming Agent with Gemini
+
+This project implements a Python-based multimodal agent that interacts using live audio and video streams, leveraging Google Cloud's Vertex AI (with a Gemini model), Speech-to-Text, and Text-to-Speech services. It features a simple web interface for real-time video monitoring and chat interaction.
+
+## Features
+
+*   **Live Audio Input/Output**: Captures audio from a microphone, transcribes it in real-time, and can synthesize text responses back to audio.
+*   **Live Video Input & Monitoring**: Captures video from a camera and monitors the feed for significant changes (e.g., motion).
+*   **AI Model Interaction**: Uses a specified Gemini model via Vertex AI to understand and comment on text, audio transcriptions, and video frame changes.
+*   **Web Interface**: Provides a browser-based UI to:
+    *   View the live camera feed.
+    *   Engage in text-based chat with the agent.
+    *   Receive real-time updates on detected video changes and the agent's comments.
+    *   Start and stop the agent's interaction.
+*   **Environment Configuration**: Uses a `.env` file for easy setup of Google Cloud project details.
+*   **Unit Tests**: Includes a suite of unit tests for core components.
+
+## Prerequisites
+
+1.  **Python**: Python 3.8 or newer.
+2.  **Google Cloud Project**:
+    *   A Google Cloud Platform project.
+    *   Vertex AI API enabled.
+    *   Cloud Speech-to-Text API enabled.
+    *   Cloud Text-to-Speech API enabled.
+    *   Billing enabled for the project.
+3.  **Authentication**:
+    *   `gcloud` CLI installed and configured.
+    *   Application Default Credentials (ADC) set up. The easiest way is to run:
+        ```bash
+        gcloud auth application-default login
+        ```
+    *   Alternatively, you can use a service account key by setting the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of your JSON key file. This is often used in production or automated environments.
+4.  **Hardware**:
+    *   A working microphone connected to the system where the application will run.
+    *   A working camera (e.g., webcam) connected to the system.
+5.  **System Dependencies**:
+    *   **For `sounddevice` (audio)**: PortAudio library.
+        *   On Debian/Ubuntu: `sudo apt-get update && sudo apt-get install libportaudio2`
+        *   On Fedora: `sudo dnf install portaudio-devel`
+        *   On macOS (using Homebrew): `brew install portaudio`
+        *   On Windows: PortAudio is often bundled with Python audio libraries, but ensure your microphone drivers are up to date.
+    *   **For `opencv-python` (video)**: While `opencv-python-headless` bundles many dependencies, ensure your system allows access to camera hardware.
+
+## Setup Instructions
+
+1.  **Clone the Repository** (if applicable):
+    ```bash
+    # git clone <repository-url>
+    # cd <repository-directory>/streaming-test
+    # For this project, you are already in the streaming-test directory.
+    ```
+
+2.  **Create and Activate Virtual Environment**:
+    It is highly recommended to use a virtual environment.
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+    ```
+
+3.  **Install Dependencies**:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4.  **Configure Environment Variables**:
+    Create a `.env` file in the `streaming-test` directory (root of this project). Add the following lines, replacing placeholders with your actual Google Cloud project details:
+    ```env
+    GOOGLE_GENAI_USE_VERTEXAI=True
+    GOOGLE_CLOUD_PROJECT="your-gcp-project-id"
+    GOOGLE_CLOUD_LOCATION="your-gcp-project-location" # e.g., us-central1
+    ```
+    The application is currently hardcoded to use `analog-delight-459210-v0` and `us-central1` as per the initial issue, so ensure these are correct for your setup or update the `.env` file accordingly.
+
+5.  **Verify Google Cloud Authentication**:
+    Ensure your environment is authenticated to Google Cloud as mentioned in the Prerequisites (e.g., by running `gcloud auth application-default login`).
+
+## Running the Application
+
+1.  **Start the Application**:
+    From the `streaming-test` directory, run:
+    ```bash
+    python run.py
+    ```
+    You should see log messages indicating the agent and web server are starting.
+
+2.  **Access the Web Interface**:
+    Open your web browser and navigate to:
+    [http://localhost:8080](http://localhost:8080) (or the host/port specified in `app/config.py` if changed).
+
+    *   You should see the live video feed.
+    *   You can type messages in the chat input to interact with the agent.
+    *   Use the "Start Agent" and "Stop Agent" buttons to control the main interaction logic (though some features like video feed might start automatically).
+    *   Watch for messages about detected video changes and the agent's comments.
+
+## Running Tests
+
+To run the unit tests:
+```bash
+python run_tests.py
+```
+This command will discover and execute all tests in the `tests` directory. You should see output indicating the number of tests run and their status.
+
+## Project Structure
+
+```
+streaming-test/
+├── .venv/                  # Python virtual environment
+├── .env                    # Environment variables (GITIGNORED)
+├── .gitignore              # Specifies intentionally untracked files
+├── app/                    # Main application package
+│   ├── __init__.py
+│   ├── agent.py            # Core MultimodalAgent class
+│   ├── audio_utils.py      # Audio recording, transcription, synthesis
+│   ├── config.py           # Application configuration
+│   ├── main.py             # Main application logic (used by run.py)
+│   ├── video_utils.py      # Video capture and change detection
+│   ├── web_interface.py    # Flask web server and UI logic
+│   └── templates/
+│       └── index.html      # HTML template for the web UI
+├── requirements.txt        # Python dependencies
+├── run.py                  # Main script to start the application (web server & agent tasks)
+├── run_tests.py            # Script to execute unit tests
+└── tests/                  # Unit tests
+    ├── __init__.py
+    ├── test_agent.py
+    ├── test_audio_utils.py
+    └── test_video_utils.py
+```
+
+## Troubleshooting
+
+*   **Camera/Microphone Not Working**:
+    *   Ensure they are properly connected and enabled in your system settings.
+    *   Check if other applications can access them.
+    *   Permissions: Your OS might require explicit permission for Python or your terminal to access the camera/microphone.
+    *   OpenCV/Sounddevice: If `VideoMonitor` or `record_audio` report errors, ensure system dependencies (like PortAudio) are installed correctly. Try a different camera index in `app/agent.py` if you have multiple cameras (e.g., `camera_index=1`).
+*   **Google Cloud API Errors**:
+    *   `PermissionDenied`: Check your ADC (`gcloud auth list`) or service account key permissions. Ensure the necessary APIs (Vertex AI, Speech, Text-to-Speech) are enabled in your GCP project.
+    *   `Billing not enabled`: Ensure billing is active for your GCP project.
+    *   `QuotaExceeded`: You might have hit API usage quotas. Check the GCP console.
+*   **Python Errors**:
+    *   `ModuleNotFoundError`: Ensure your virtual environment is activated and all dependencies in `requirements.txt` are installed.
+    *   Errors from `sounddevice` or `cv2`: Often related to missing system libraries or hardware access issues.
+*   **Web Interface Issues**:
+    *   Video feed not showing: Check browser console for errors. Ensure the Flask server is running and there are no errors in the terminal output related to camera capture.
+    *   SSE events not updating: Check browser console and Flask server logs. The `/agent_events` endpoint might have issues, or the background thread in `run.py` might not be running correctly.
+
+This README provides a starting point. Feel free to expand it as the project evolves.
diff --git a/in_progress/streaming-test/app/__init__.py b/in_progress/streaming-test/app/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test/app/agent.py b/in_progress/streaming-test/app/agent.py
new file mode 100644
index 000000000..24fb395b7
--- /dev/null
+++ b/in_progress/streaming-test/app/agent.py
@@ -0,0 +1,259 @@
+import os
+import time # Required for sleep in __main__
+import vertexai
+from vertexai.generative_models import GenerativeModel, Part
+
+from .audio_utils import transcribe_audio_bytes, synthesize_text_to_audio_bytes, play_audio_bytes, record_audio
+from .video_utils import VideoMonitor
+from dotenv import load_dotenv # Added for direct run in __main__
+
+class MultimodalAgent:
+    def __init__(self, project_id: str, location: str, model_name: str, camera_index: int = 0):
+        if not project_id:
+            raise ValueError("Google Cloud Project ID is required.")
+        if not location:
+            raise ValueError("Google Cloud Location is required.")
+        if not model_name:
+            raise ValueError("Model name is required.")
+
+        self.project_id = project_id
+        self.location = location
+        self.model_name = model_name
+
+        vertexai.init(project=self.project_id, location=self.location)
+        self.model = GenerativeModel(self.model_name)
+        self.chat = None
+
+        self.video_monitor = VideoMonitor(camera_index=camera_index, fps_limit=1)
+
+        print(f"AGENT: MultimodalAgent initialized with project: {self.project_id}, location: {self.location}, model: {self.model_name}")
+        print(f"AGENT: Video monitor configured for camera index {camera_index}.")
+
+    def start_chat(self):
+        print("AGENT: Attempting to start chat session...")
+        self.chat = self.model.start_chat(response_validation=False)
+        print("AGENT: New chat session started with Vertex AI.")
+        if not self.video_monitor.cap:
+             if self.video_monitor.start_capture():
+                 print("AGENT: Video capture started successfully.")
+             else:
+                 print("AGENT: Failed to start video capture.")
+        return self.chat
+
+    def stop_chat(self):
+        print("AGENT: Attempting to stop chat session...")
+        if self.video_monitor.cap:
+            self.video_monitor.stop_capture()
+            # print("AGENT: Video capture stopped.") # Covered by video_monitor's own print
+        self.chat = None
+        print("AGENT: Chat session ended.")
+
+    def send_text_message(self, text_prompt: str) -> str:
+        if not self.chat:
+            print("AGENT: Chat not started in send_text_message. Starting chat...")
+            self.start_chat()
+
+        print(f"AGENT: Sending text to model: '{text_prompt}'")
+        try:
+            response = self.chat.send_message(text_prompt)
+            # Ensure response.text is accessed safely
+            response_text = ""
+            if response and response.candidates and response.candidates[0].content and response.candidates[0].content.parts:
+                 response_text = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts[0].text else ""
+
+            print(f"AGENT: Received text response from model: '{response_text[:100]}...'")
+            return response_text
+        except Exception as e:
+            print(f"AGENT: Error sending text message to model: {e}")
+            raise
+
+    def handle_voice_interaction(self, record_duration_seconds: int = 5, event_queue=None):
+        print(f"AGENT: --- Starting Voice Interaction Cycle (Duration: {record_duration_seconds}s) ---")
+        if not self.chat:
+            print("AGENT: Chat not started in handle_voice_interaction. Starting chat...")
+            self.start_chat()
+
+        # 1. Record audio
+        print("AGENT: Calling record_audio...")
+        user_audio_bytes = record_audio(duration_seconds=record_duration_seconds)
+        if not user_audio_bytes:
+            print("AGENT: Audio recording failed or returned no data.")
+            if event_queue: event_queue.put({"type": "status", "message": "Audio recording failed."})
+            return
+        print(f"AGENT: record_audio successful, {len(user_audio_bytes)} bytes recorded.")
+
+        # 2. Transcribe audio
+        print("AGENT: Calling transcribe_audio_bytes...")
+        user_text_prompt = transcribe_audio_bytes(user_audio_bytes)
+        if user_text_prompt is None:
+            print("AGENT: Audio transcription failed (returned None).")
+            if event_queue: event_queue.put({"type": "status", "message": "Transcription failed."})
+            return
+        if not user_text_prompt: # Empty string if no speech detected
+            print("AGENT: No speech detected in the recording (transcription returned empty string).")
+            if event_queue: event_queue.put({"type": "status", "message": "No speech detected."})
+            return
+        print(f"AGENT: User (transcribed): '{user_text_prompt}'")
+        if event_queue:
+            print(f"AGENT: Putting transcription to event_queue: '{user_text_prompt}'")
+            event_queue.put({"type": "transcription", "text": user_text_prompt})
+
+        # 3. Send transcribed text to model
+        print(f"AGENT: Calling send_text_message with transcribed text: '{user_text_prompt}'")
+        model_response_text = self.send_text_message(user_text_prompt)
+        if model_response_text is None : # Explicitly check for None, empty string is a valid response
+            print("AGENT: Model did not return a text response (send_text_message returned None).")
+            if event_queue: event_queue.put({"type": "status", "message": "Model did not respond."})
+            return
+
+        print(f"AGENT: Model (text response): '{model_response_text}'")
+        if event_queue:
+            print(f"AGENT: Putting model text response to event_queue: '{model_response_text}'")
+            event_queue.put({"type": "model_response_audio_text", "text": model_response_text})
+
+        # 4. Synthesize model's text response to audio
+        if not model_response_text.strip(): # Don't synthesize if empty or only whitespace
+            print("AGENT: Model response was empty, skipping audio synthesis.")
+        else:
+            print("AGENT: Calling synthesize_text_to_audio_bytes...")
+            model_audio_bytes = synthesize_text_to_audio_bytes(model_response_text)
+            if not model_audio_bytes:
+                print("AGENT: Model audio synthesis failed.")
+            else:
+                print(f"AGENT: synthesize_text_to_audio_bytes successful, {len(model_audio_bytes)} bytes synthesized.")
+                # 5. Play back synthesized audio (plays on server where Python runs)
+                print("AGENT: Calling play_audio_bytes (server-side playback)...")
+                play_audio_bytes(model_audio_bytes, output_format="mp3")
+
+        print("AGENT: --- Voice Interaction Cycle Ended ---")
+
+
+    def check_for_video_changes_and_comment(self, event_queue=None):
+        if not self.video_monitor.cap or not self.video_monitor.cap.isOpened():
+            if self.chat:
+                print("AGENT: Video monitor not active, attempting to restart for change detection...")
+                self.video_monitor.start_capture()
+                if not self.video_monitor.cap or not self.video_monitor.cap.isOpened():
+                    # print("AGENT: Video capture is not active. Cannot check for changes.") # Can be noisy
+                    return
+            else:
+                return
+
+        changed, description, frame_bytes = self.video_monitor.process_frame_for_changes()
+
+        if changed and frame_bytes:
+            print(f"AGENT: Video change detected: {description}. Preparing to send to model.")
+
+            if not self.chat:
+                print("AGENT: Chat not started in check_for_video_changes. Starting chat...")
+                self.start_chat()
+
+            image_part = Part.from_data(frame_bytes, mime_type="image/jpeg")
+            prompt_text = f"A change was detected in the video feed: {description}. Concisely describe what you observe in this image related to the change."
+
+            print(f"AGENT: Sending video change event to model: '{prompt_text}'")
+            model_response = self.send_message(text_prompt=prompt_text, image_parts=[image_part])
+
+            if model_response: # Includes empty string
+                print(f"AGENT: Model commented on video change: '{model_response}'")
+                if event_queue:
+                    print(f"AGENT: Putting video change comment to event_queue: '{model_response}'")
+                    event_queue.put({"type": "video_change_comment", "comment": model_response})
+            else: # Only if model_response is None
+                print("AGENT: Model did not provide a comment for the video change (send_message returned None).")
+        elif changed:
+            print(f"AGENT: Video change detected ({description}), but frame data is unavailable.")
+
+    def send_message(self, text_prompt: str = None, image_parts: list = None, audio_parts: list = None, video_parts: list = None) -> str | None:
+        if not self.chat:
+            print("AGENT: Chat not started in send_message. Starting chat...")
+            self.start_chat()
+
+        if not text_prompt and not image_parts and not audio_parts and not video_parts:
+            print("AGENT: send_message called with no content.")
+            # raise ValueError("At least one input type (text, image, audio, video) must be provided.")
+            return None # Return None instead of raising error to prevent crash
+
+        prompt_parts = []
+        if text_prompt:
+            prompt_parts.append(Part.from_text(text_prompt))
+
+        if image_parts: prompt_parts.extend(image_parts)
+        if audio_parts: prompt_parts.extend(audio_parts)
+        if video_parts: prompt_parts.extend(video_parts)
+
+        part_types_summary = []
+        for p in prompt_parts:
+            part_dict = p.to_dict() if hasattr(p, 'to_dict') else {}
+            if part_dict.get('text'): # Check if it's a text part
+                 part_types_summary.append('text')
+            elif part_dict.get('inline_data'): # Check if it's an inline_data part
+                 part_types_summary.append(f"inline_data:{part_dict['inline_data'].get('mime_type', 'unknown')}")
+            else:
+                 part_types_summary.append('unknown_part_type') # Should ideally not happen with current Part creation
+        print(f"AGENT: Sending parts to model: {part_types_summary}")
+
+        try:
+            response = self.chat.send_message(prompt_parts, stream=False)
+            # Safe access to response text
+            response_text = ""
+            if response and response.candidates and response.candidates[0].content and response.candidates[0].content.parts:
+                 response_text = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts[0].text else ""
+
+            print(f"AGENT: Received raw response from model. Text content: '{response_text[:100]}...'")
+            return response_text
+        except Exception as e:
+            print(f"AGENT: Error sending multimodal message to model: {e}")
+            # Potentially return None or re-raise specific error types
+            return None
+
+
+if __name__ == '__main__':
+    print("Attempting to run agent.py for direct testing...")
+    dotenv_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env')
+    print(f"AGENT MAIN: Loading .env from {dotenv_path}")
+    load_dotenv(dotenv_path=dotenv_path)
+
+    use_vertex_env = os.getenv('GOOGLE_GENAI_USE_VERTEXAI', 'False').lower() == 'true'
+    project = os.getenv('GOOGLE_CLOUD_PROJECT')
+    location = os.getenv('GOOGLE_CLOUD_LOCATION')
+    model_name_config = "gemini-2.0-flash"
+
+    if use_vertex_env and project and location:
+        print(f"AGENT MAIN: Initializing agent with project='{project}', location='{location}', model='{model_name_config}'")
+        agent = MultimodalAgent(
+            project_id=project,
+            location=location,
+            model_name=model_name_config,
+            camera_index=0
+        )
+        print("AGENT MAIN: Starting chat...")
+        agent.start_chat()
+
+        if not agent.video_monitor.cap or not agent.video_monitor.cap.isOpened():
+            print("AGENT MAIN: Video capture failed to initialize. Video tests will be skipped.")
+        else:
+            print("AGENT MAIN: Video capture active. Monitoring for video changes for ~5 seconds for test.")
+            start_time = time.time()
+            try:
+                while time.time() - start_time < 5:
+                    agent.check_for_video_changes_and_comment()
+                    time.sleep(1)
+            except KeyboardInterrupt:
+                print("AGENT MAIN: Test interrupted.")
+
+        if os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
+            print("\nAGENT MAIN: Attempting a voice interaction cycle (will try to record for 3s).")
+            print("AGENT MAIN: Please speak into your microphone when 'Recording audio...' appears.")
+            agent.handle_voice_interaction(record_duration_seconds=3)
+        else:
+            print("\nAGENT MAIN: GOOGLE_APPLICATION_CREDENTIALS not set. Skipping direct voice interaction test.")
+            print("AGENT MAIN: Testing text-only interaction instead.")
+            response_text = agent.send_text_message("Hello! This is a text-only direct test from agent.py.")
+            print(f"AGENT MAIN: Model Response: {response_text}")
+
+        print("AGENT MAIN: Stopping chat...")
+        agent.stop_chat()
+        print("AGENT MAIN: Direct test finished.")
+    else:
+        print("AGENT MAIN: Environment variables for Vertex AI not set. Skipping agent direct test.")
diff --git a/in_progress/streaming-test/app/audio_utils.py b/in_progress/streaming-test/app/audio_utils.py
new file mode 100644
index 000000000..28064a098
--- /dev/null
+++ b/in_progress/streaming-test/app/audio_utils.py
@@ -0,0 +1,255 @@
+import os
+import sounddevice as sd
+import numpy as np
+from scipy.io.wavfile import write as write_wav # To save audio if needed, or pass bytes directly
+import io
+import time
+
+from google.cloud import speech
+from google.cloud import texttospeech
+
+from app.config import AUDIO_SAMPLE_RATE, AUDIO_CHANNELS
+
+# --- Audio Recording ---
+def record_audio(duration_seconds: int, sample_rate: int = AUDIO_SAMPLE_RATE, channels: int = AUDIO_CHANNELS) -> bytes:
+    """
+    Records audio from the default microphone for a specified duration.
+
+    Args:
+        duration_seconds: The number of seconds to record.
+        sample_rate: The recording sample rate.
+        channels: The number of recording channels.
+
+    Returns:
+        A bytes object containing the recorded audio data in WAV format.
+    """
+    print(f"Recording audio for {duration_seconds} seconds at {sample_rate} Hz...")
+    try:
+        recording = sd.rec(int(duration_seconds * sample_rate), samplerate=sample_rate, channels=channels, dtype='int16')
+        sd.wait()  # Wait until recording is finished
+        print("Recording finished.")
+
+        # Convert to WAV bytes
+        byte_io = io.BytesIO()
+        write_wav(byte_io, sample_rate, recording)
+        return byte_io.getvalue()
+    except Exception as e:
+        print(f"Error during audio recording: {e}")
+        print("Make sure you have a microphone connected and sounddevice is correctly installed.")
+        print("You might need to install system dependencies for PortAudio (used by sounddevice).")
+        print("On Debian/Ubuntu: sudo apt-get install libportaudio2")
+        print("On Fedora: sudo dnf install portaudio-devel")
+        print("On macOS (using Homebrew): brew install portaudio")
+        return None
+
+# --- Live Transcription (Speech-to-Text) ---
+def transcribe_audio_bytes(audio_bytes: bytes, sample_rate: int = AUDIO_SAMPLE_RATE) -> str:
+    """
+    Transcribes the given audio data using Google Cloud Speech-to-Text.
+
+    Args:
+        audio_bytes: Bytes object containing WAV audio data.
+        sample_rate: The sample rate of the audio.
+
+    Returns:
+        The transcribed text, or None if transcription fails.
+    """
+    if not audio_bytes:
+        print("No audio bytes provided for transcription.")
+        return None
+
+    print("Transcribing audio...")
+    try:
+        client = speech.SpeechClient()
+
+        audio = speech.RecognitionAudio(content=audio_bytes)
+        config = speech.RecognitionConfig(
+            encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16, # WAV is typically LINEAR16
+            sample_rate_hertz=sample_rate,
+            language_code="en-US",  # Adjust as needed
+            # model="telephony" or "medical" or other specialized models can be used.
+            # enable_automatic_punctuation=True, # Useful for more natural text
+        )
+
+        response = client.recognize(config=config, audio=audio)
+
+        if response.results and response.results[0].alternatives:
+            transcript = response.results[0].alternatives[0].transcript
+            print(f"Transcription: {transcript}")
+            return transcript
+        else:
+            print("No transcription results found.")
+            return "" # Return empty string if no results
+    except Exception as e:
+        print(f"Error during audio transcription: {e}")
+        return None
+
+# --- Speech Synthesis (Text-to-Speech) ---
+def synthesize_text_to_audio_bytes(text: str) -> bytes:
+    """
+    Synthesizes speech from the given text using Google Cloud Text-to-Speech.
+
+    Args:
+        text: The text to synthesize.
+
+    Returns:
+        A bytes object containing the synthesized audio data (e.g., MP3), or None if synthesis fails.
+    """
+    if not text:
+        print("No text provided for synthesis.")
+        return None
+
+    print(f"Synthesizing audio for text: '{text[:50]}...'")
+    try:
+        client = texttospeech.TextToSpeechClient()
+
+        input_text = texttospeech.SynthesisInput(text=text)
+
+        # Configure the voice request
+        voice = texttospeech.VoiceSelectionParams(
+            language_code="en-US",  # Adjust as needed
+            # name="en-US-Wavenet-D", # Example Wavenet voice
+            # For a list of voices: client.list_voices()
+            # Standard voices are cheaper than Wavenet voices.
+            ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL, # Or FEMALE, MALE
+        )
+
+        # Select the type of audio file you want
+        audio_config = texttospeech.AudioConfig(
+            audio_encoding=texttospeech.AudioEncoding.MP3  # Or LINEAR16 for WAV
+        )
+
+        response = client.synthesize_speech(
+            request={"input": input_text, "voice": voice, "audio_config": audio_config}
+        )
+        print("Audio synthesis finished.")
+        return response.audio_content
+    except Exception as e:
+        print(f"Error during audio synthesis: {e}")
+        return None
+
+# --- Audio Playback ---
+def play_audio_bytes(audio_bytes: bytes, sample_rate: int = AUDIO_SAMPLE_RATE, output_format="mp3"):
+    """
+    Plays back audio bytes.
+    Note: sounddevice.play directly plays numpy arrays (PCM data).
+          If we have MP3 or other formats, we'd need to decode them first.
+          For simplicity, this example assumes we can get PCM if needed,
+          or the system has a way to play MP3s (which `sd.play` does not do directly for MP3 bytes).
+
+          This function is a placeholder and might need a more robust playback solution
+          depending on the `audio_config.audio_encoding` used in synthesis.
+          If LINEAR16 (WAV) is used in synthesis, it's more straightforward.
+    Args:
+        audio_bytes: Bytes object containing audio data.
+        sample_rate: The sample rate for playback (relevant for PCM data).
+        output_format: The format of audio_bytes (e.g., "mp3", "wav").
+    """
+    if not audio_bytes:
+        print("No audio bytes provided for playback.")
+        return
+
+    print(f"Playing audio (format: {output_format})...")
+    try:
+        if output_format.lower() == "mp3":
+            # sounddevice doesn't directly play MP3 bytes.
+            # You'd typically need a library like 'pydub' or 'mpg123'/'ffplay' subprocess to play MP3s.
+            # For this example, we'll print a message.
+            # In a full app, you'd integrate a proper MP3 player.
+            print("Playback of MP3 bytes requires an external player or library like pydub.")
+            print("To actually play, you would save to a temp file and use a system command,")
+            print("or use a library that can decode MP3 to PCM for sounddevice.")
+            # Example with pydub (if installed and ffmpeg is available):
+            # from pydub import AudioSegment
+            # from pydub.playback import play
+            # song = AudioSegment.from_file(io.BytesIO(audio_bytes), format="mp3")
+            # play(song)
+            # For now, just simulate playback time for non-PCM
+            time.sleep(2) # Simulate playback
+
+        elif output_format.lower() == "wav": # Assuming it's PCM data if WAV
+            # If audio_bytes is raw PCM (e.g., from LINEAR16 synthesis decoded)
+            # This needs to be actual PCM data, not WAV file bytes with headers.
+            # For simplicity, assuming if it's 'wav', it's PCM data that sounddevice can handle.
+            # This part needs careful handling of audio formats.
+            # If it's a full WAV file in bytes, we need to parse it.
+            from scipy.io.wavfile import read as read_wav
+            byte_io = io.BytesIO(audio_bytes)
+            rate, data = read_wav(byte_io) # This reads a WAV file, not raw PCM.
+            sd.play(data, samplerate=rate)
+            sd.wait()
+        else:
+            print(f"Unsupported audio format for direct playback: {output_format}")
+
+        print("Playback finished.")
+    except Exception as e:
+        print(f"Error during audio playback: {e}")
+        print("Ensure you have a speaker/audio output device.")
+        print("For MP3 playback, consider installing 'pydub' and 'ffmpeg'.")
+        print("For WAV playback with sounddevice, ensure the data is in a compatible PCM format.")
+
+
+if __name__ == '__main__':
+    # Basic test functions (will require manual interaction or pre-recorded files if run in non-interactive env)
+    print("Testing audio_utils.py...")
+
+    # 1. Record or Load Audio (Skipping live recording in automated subtask)
+    # print("\n--- Testing Recording (manual check needed) ---")
+    # recorded_audio_wav_bytes = record_audio(duration_seconds=3)
+    # if recorded_audio_wav_bytes:
+    #     with open("test_recording.wav", "wb") as f:
+    #         f.write(recorded_audio_wav_bytes)
+    #     print("Test recording saved to test_recording.wav (if microphone worked)")
+    # else:
+    #     print("Skipping recording test or it failed.")
+
+    # For automated testing, let's create a dummy WAV file to use for transcription
+    sample_rate_test = 16000
+    duration_test = 1 # 1 second
+    frequency_test = 440 # A4 note
+    t_test = np.linspace(0, duration_test, int(sample_rate_test * duration_test), False)
+    audio_data_test = (0.5 * np.sin(2 * np.pi * frequency_test * t_test) * 32767).astype(np.int16) # Sine wave
+    dummy_wav_bytes_io = io.BytesIO()
+    write_wav(dummy_wav_bytes_io, sample_rate_test, audio_data_test)
+    dummy_wav_bytes = dummy_wav_bytes_io.getvalue()
+    with open("dummy_audio_for_transcription.wav", "wb") as f:
+        f.write(dummy_wav_bytes)
+    print("Created dummy_audio_for_transcription.wav for testing.")
+
+
+    # 2. Transcribe Audio
+    print("\n--- Testing Transcription ---")
+    # Prerequisite: GOOGLE_APPLICATION_CREDENTIALS must be set in the environment
+    # and the associated account must have Speech-to-Text API enabled.
+    if os.getenv("GOOGLE_APPLICATION_CREDENTIALS") and dummy_wav_bytes:
+        transcribed_text = transcribe_audio_bytes(dummy_wav_bytes, sample_rate=sample_rate_test)
+        if transcribed_text is not None:
+            print(f"Test Transcription Result: '{transcribed_text}' (Note: dummy audio will likely result in empty or random transcription)")
+        else:
+            print("Transcription test failed or returned None.")
+    else:
+        print("Skipping transcription test: GOOGLE_APPLICATION_CREDENTIALS not set or no dummy audio.")
+
+    # 3. Synthesize Text
+    print("\n--- Testing Synthesis ---")
+    # Prerequisite: GOOGLE_APPLICATION_CREDENTIALS must be set.
+    if os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
+        test_text_to_synthesize = "Hello, this is a test of the text to speech system."
+        synthesized_audio_mp3_bytes = synthesize_text_to_audio_bytes(test_text_to_synthesize)
+        if synthesized_audio_mp3_bytes:
+            with open("test_synthesis.mp3", "wb") as f:
+                f.write(synthesized_audio_mp3_bytes)
+            print("Test synthesis saved to test_synthesis.mp3")
+
+            # 4. Play Synthesized Audio (Placeholder in automated subtask)
+            print("\n--- Testing Playback (manual check needed for actual sound) ---")
+            play_audio_bytes(synthesized_audio_mp3_bytes, output_format="mp3")
+            # To test WAV playback (requires synthesized_audio_bytes to be WAV)
+            # synthesized_audio_wav_bytes = synthesize_text_to_audio_bytes(test_text_to_synthesize, audio_format=texttospeech.AudioEncoding.LINEAR16)
+            # play_audio_bytes(synthesized_audio_wav_bytes, output_format="wav", sample_rate=...) # Need to know sample rate from TTS
+        else:
+            print("Synthesis test failed or returned None.")
+    else:
+        print("Skipping synthesis and playback test: GOOGLE_APPLICATION_CREDENTIALS not set.")
+
+    print("\nAudio utils testing finished.")
diff --git a/in_progress/streaming-test/app/config.py b/in_progress/streaming-test/app/config.py
new file mode 100644
index 000000000..7f15592d9
--- /dev/null
+++ b/in_progress/streaming-test/app/config.py
@@ -0,0 +1,23 @@
+# Configuration settings for the application
+
+# Vertex AI / Gemini Model Configuration
+GOOGLE_CLOUD_PROJECT = None # Will be loaded from .env
+GOOGLE_CLOUD_LOCATION = None # Will be loaded from .env
+MODEL_NAME = "gemini-2.0-flash" # Updated based on user feedback for video functionality
+
+# Audio settings
+AUDIO_SAMPLE_RATE = 16000
+AUDIO_CHANNELS = 1
+# Add other audio settings as needed (e.g., chunk size for streaming)
+
+MODEL_NAME = "gemini-2.0-flash"
+
+# Video settings
+VIDEO_FPS = 10 # Default FPS for video processing by VideoMonitor
+# Add other video settings as needed
+
+# Web server settings
+WEB_HOST = "0.0.0.0"
+WEB_PORT = 8080
+
+# You can add more configuration variables here as the project grows.
diff --git a/in_progress/streaming-test/app/main.py b/in_progress/streaming-test/app/main.py
new file mode 100644
index 000000000..e360f7c21
--- /dev/null
+++ b/in_progress/streaming-test/app/main.py
@@ -0,0 +1,52 @@
+import os
+from dotenv import load_dotenv
+from app.agent import MultimodalAgent
+from app.config import MODEL_NAME
+
+def main():
+    # Load environment variables from .env file
+    # This is important for GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION
+    load_dotenv(dotenv_path=os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env'))
+
+    project_id = os.getenv("GOOGLE_CLOUD_PROJECT")
+    location = os.getenv("GOOGLE_CLOUD_LOCATION")
+    use_vertex = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "False").lower() == "true"
+
+    if not use_vertex:
+        print("GOOGLE_GENAI_USE_VERTEXAI is not set to True in .env file. Exiting.")
+        return
+
+    if not project_id or not location:
+        print("GOOGLE_CLOUD_PROJECT or GOOGLE_CLOUD_LOCATION not set in .env file. Exiting.")
+        return
+
+    print(f"Initializing agent in main() with project: {project_id}, location: {location}, model: {MODEL_NAME}")
+
+    try:
+        agent = MultimodalAgent(
+            project_id=project_id,
+            location=location,
+            model_name=MODEL_NAME
+        )
+
+        # Example usage (text-only for now)
+        agent.start_chat()
+        response = agent.send_message(text_prompt="What is the weather like today?")
+        print(f"Agent received response: {response}")
+
+        # In a real application, this would be part of a larger loop
+        # or triggered by UI events.
+
+    except Exception as e:
+        print(f"An error occurred in main: {e}")
+
+if __name__ == "__main__":
+    # This allows running main.py directly for testing,
+    # assuming .env is in the parent directory of app/
+    # For this to work, .env file needs to be created in `streaming-test` directory
+    print("Running main.py...")
+    print("Ensure .env file exists in the 'streaming-test' directory with:")
+    print("GOOGLE_GENAI_USE_VERTEXAI=True")
+    print("GOOGLE_CLOUD_PROJECT=<your-project-id>")
+    print("GOOGLE_CLOUD_LOCATION=<your-location>")
+    main()
diff --git a/in_progress/streaming-test/app/templates/index.html b/in_progress/streaming-test/app/templates/index.html
new file mode 100644
index 000000000..56da3a2af
--- /dev/null
+++ b/in_progress/streaming-test/app/templates/index.html
@@ -0,0 +1,191 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Multimodal Agent Interface</title>
+    <style>
+        body { font-family: sans-serif; margin: 20px; background-color: #f4f4f4; color: #333; }
+        .container { display: flex; flex-direction: column; align-items: center; }
+        .video-container { border: 1px solid #ccc; margin-bottom: 20px; min-width: 320px; min-height: 240px; background-color: #000;}
+        .chat-container { width: 80%; max-width: 600px; margin-bottom: 20px; }
+        .chat-box { height: 300px; border: 1px solid #ccc; overflow-y: scroll; padding: 10px; background-color: #fff; margin-bottom: 10px; }
+        .chat-message { margin-bottom: 8px; padding: 5px; border-radius: 4px; }
+        .user-message { background-color: #e1f5fe; text-align: right; }
+        .model-message { background-color: #f0f4c3; }
+        .controls { display: flex; justify-content: center; align-items: center; width: 80%; max-width: 600px; }
+        #chatInput { flex-grow: 1; padding: 8px; margin-right: 10px; border: 1px solid #ccc; border-radius: 4px;}
+        button { padding: 8px 15px; border: none; border-radius: 4px; cursor: pointer; background-color: #4CAF50; color: white; margin-left: 5px; } /* Added margin-left to button */
+        button:hover { background-color: #45a049; }
+        .status { margin-top: 10px; font-style: italic; color: #555; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Live Multimodal Agent</h1>
+
+        <div class="video-container">
+            <img id="videoFeed" src="/video_feed" width="640" height="480" alt="Video Feed">
+        </div>
+
+        <div class="chat-container">
+            <div id="chatBox" class="chat-box">
+                <!-- Chat messages will appear here -->
+            </div>
+            <div class="controls">
+                <input type="text" id="chatInput" placeholder="Type your message...">
+                <button onclick="sendChatMessage()">Send</button>
+                <button onclick="sendVoiceMessage()" id="sendVoiceBtn">Send Voice (Record 5s)</button>
+            </div>
+            <div class="status" id="statusArea">Agent status will appear here.</div>
+        </div>
+         <button onclick="startAgent()">Start Agent</button>
+         <button onclick="stopAgent()">Stop Agent</button>
+    </div>
+
+    <script>
+        const chatBox = document.getElementById('chatBox');
+        const chatInput = document.getElementById('chatInput');
+        const statusArea = document.getElementById('statusArea');
+
+        function addMessage(text, sender) {
+            const messageDiv = document.createElement('div');
+            messageDiv.classList.add('chat-message', sender + '-message');
+            messageDiv.textContent = text;
+            chatBox.appendChild(messageDiv);
+            chatBox.scrollTop = chatBox.scrollHeight; // Auto-scroll
+        }
+
+        async function sendChatMessage() {
+            const messageText = chatInput.value;
+            if (!messageText.trim()) return;
+
+            addMessage(messageText, 'user');
+            chatInput.value = '';
+
+            try {
+                const response = await fetch('/send_chat_message', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ message: messageText })
+                });
+                const data = await response.json();
+                if (data.reply) {
+                    addMessage(data.reply, 'model');
+                } else if (data.error) {
+                    addMessage('Error: ' + data.error, 'model');
+                }
+            } catch (error) {
+                console.error('Error sending chat message:', error);
+                addMessage('Error connecting to agent.', 'model');
+            }
+        }
+
+        chatInput.addEventListener('keypress', function(event) {
+            if (event.key === 'Enter') {
+                sendChatMessage();
+            }
+        });
+
+        async function startAgent() {
+            statusArea.textContent = "Attempting to start agent...";
+            try {
+                const response = await fetch('/start_interaction');
+                const data = await response.json();
+                statusArea.textContent = data.status || data.error;
+                addMessage(data.status || data.error, 'model');
+                // Connect to SSE after agent is confirmed started
+                if (response.ok && data.status && data.status.includes("started")) {
+                    connectToAgentEvents();
+                }
+            } catch (error) {
+                 statusArea.textContent = "Error starting agent.";
+                 addMessage('Error starting agent.', 'model');
+            }
+        }
+
+        async function stopAgent() {
+            statusArea.textContent = "Attempting to stop agent...";
+            try {
+                const response = await fetch('/stop_interaction');
+                const data = await response.json();
+                statusArea.textContent = data.status || data.error;
+                addMessage(data.status || data.error, 'model');
+                if (eventSource) {
+                    eventSource.close();
+                    statusArea.textContent = "Agent stopped. Event stream closed.";
+                }
+            } catch (error) {
+                 statusArea.textContent = "Error stopping agent.";
+                 addMessage('Error stopping agent.', 'model');
+            }
+        }
+
+        async function sendVoiceMessage() {
+            const sendVoiceButton = document.getElementById("sendVoiceBtn");
+            addMessage("🎤 Recording for 5s... Please speak.", "user");
+            statusArea.textContent = "Recording audio...";
+            sendVoiceButton.disabled = true;
+
+            try {
+                const response = await fetch("/send_voice_message", { method: "POST" });
+                const data = await response.json();
+                if (data.status) {
+                    statusArea.textContent = data.status;
+                    // Messages (transcription, model reply) should come via SSE
+                } else if (data.error) {
+                    addMessage("Voice Error: " + data.error, "model");
+                    statusArea.textContent = "Voice error: " + data.error;
+                }
+            } catch (error) {
+                console.error("Error sending voice message:", error);
+                addMessage("Error processing voice input.", "model");
+                statusArea.textContent = "Error processing voice input.";
+            } finally {
+                sendVoiceButton.disabled = false;
+            }
+        }
+
+        // Function to fetch and display video change notifications from the agent
+        let eventSource = null;
+        function connectToAgentEvents() {
+            if (eventSource) {
+                eventSource.close();
+            }
+            statusArea.textContent = "Connecting to agent events...";
+            eventSource = new EventSource("/agent_events");
+
+            eventSource.onopen = function() {
+                statusArea.textContent = "Connected to agent events.";
+                addMessage("Connected to agent for live updates.", "model");
+            };
+
+            eventSource.onmessage = function(event) {
+                const data = JSON.parse(event.data);
+                if (data.type === "video_change_comment") {
+                    addMessage("Video Event: " + data.comment, "model");
+                } else if (data.type === "status") {
+                     statusArea.textContent = data.message;
+                } else if (data.type === "transcription") {
+                    addMessage("You (transcribed): " + data.text, "user");
+                } else if (data.type === "model_response_audio_text") {
+                    addMessage("Agent: " + data.text, "model");
+                }
+            };
+
+            eventSource.onerror = function(err) {
+                console.error("EventSource failed:", err);
+                statusArea.textContent = "Disconnected from agent events.";
+                addMessage("Lost connection to agent live updates.", "model");
+                if (eventSource) eventSource.close();
+            };
+        }
+
+        window.onload = () => {
+            addMessage("Welcome! Click 'Start Agent' to begin.", "model");
+            // Do not automatically connectToAgentEvents here.
+            // connectToAgentEvents will be called by startAgent().
+        };
+
+    </script>
+</body>
+</html>
diff --git a/in_progress/streaming-test/app/video_utils.py b/in_progress/streaming-test/app/video_utils.py
new file mode 100644
index 000000000..056647e72
--- /dev/null
+++ b/in_progress/streaming-test/app/video_utils.py
@@ -0,0 +1,201 @@
+import cv2
+import numpy as np
+import time
+from app.config import VIDEO_FPS # Placeholder for now
+
+# --- Video Capture ---
+class VideoMonitor:
+    def __init__(self, camera_index=0, fps_limit=None):
+        """
+        Initializes the VideoMonitor.
+
+        Args:
+            camera_index: The index of the camera to use (e.g., 0 for default).
+            fps_limit: Limit processing to this many FPS. None for no limit (process as fast as camera provides).
+        """
+        self.camera_index = camera_index
+        self.cap = None
+        self.previous_frame = None
+        self.last_processed_time = 0
+        self.fps_limit = fps_limit if fps_limit else VIDEO_FPS # Default to VIDEO_FPS from config if not provided
+        self.cv2 = cv2 # Store cv2 module reference for imencode in web_interface
+
+        # For person detection (optional, basic example)
+        # self.hog = cv2.HOGDescriptor()
+        # self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+        # More robust change detection might involve background subtraction
+        self.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+    def start_capture(self):
+        """Starts video capture from the camera."""
+        self.cap = cv2.VideoCapture(self.camera_index)
+        if not self.cap.isOpened():
+            print(f"Error: Could not open video stream from camera index {self.camera_index}.")
+            print("Make sure a camera is connected and accessible.")
+            print("If using a virtual environment, ensure OpenCV can access camera drivers.")
+            self.cap = None # Ensure it's None if failed
+            return False
+        print(f"Video capture started on camera index {self.camera_index}.")
+        self.previous_frame = None # Reset previous frame on start
+        self.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False) # Re-initialize
+        return True
+
+    def stop_capture(self):
+        """Stops video capture and releases the camera."""
+        if self.cap:
+            self.cap.release()
+            self.cap = None
+            print("Video capture stopped.")
+        self.previous_frame = None
+
+    def get_frame(self):
+        """
+        Retrieves a single frame from the camera.
+
+        Returns:
+            A numpy array representing the frame, or None if capture failed.
+        """
+        if not self.cap or not self.cap.isOpened():
+            # print("Capture not started or failed.")
+            return None
+
+        ret, frame = self.cap.read()
+        if not ret:
+            print("Error: Can't receive frame (stream end?). Exiting ...")
+            return None
+        return frame
+
+    def detect_significant_change(self, frame: np.ndarray, threshold_abs_diff=30, min_contour_area=500) -> tuple[bool, str, bytes | None]:
+        """
+        Detects a significant change in the video frame using background subtraction and motion.
+
+        Args:
+            frame: The current video frame.
+            threshold_abs_diff: Sensitivity for absolute difference method. (Not used with MOG2 directly)
+            min_contour_area: Minimum area of a contour to be considered significant motion.
+
+
+        Returns:
+            A tuple: (has_changed: bool, change_description: str, processed_frame_bytes: bytes | None).
+            processed_frame_bytes will be the JPEG encoded frame if a change is detected.
+        """
+        if frame is None:
+            return False, "No frame", None
+
+        # 1. Background Subtraction (more robust than simple frame diff)
+        fg_mask = self.bg_subtractor.apply(frame)
+
+        # 2. Noise reduction - Apply morphological opening
+        kernel = np.ones((5,5),np.uint8)
+        fg_mask_opened = cv2.morphologyEx(fg_mask, cv2.MORPH_OPEN, kernel)
+        fg_mask_closed = cv2.morphologyEx(fg_mask_opened, cv2.MORPH_CLOSE, kernel)
+
+
+        # 3. Find contours of moving objects
+        contours, _ = cv2.findContours(fg_mask_closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        significant_motion_detected = False
+        for contour in contours:
+            if cv2.contourArea(contour) > min_contour_area:
+                significant_motion_detected = True
+                # (x, y, w, h) = cv2.boundingRect(contour)
+                # cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) # Draw on original for viz
+                break # Found one significant motion
+
+        if significant_motion_detected:
+            change_description = "Significant motion detected in the scene."
+            # Encode the current frame to JPEG bytes to send to the model
+            ret, buffer = cv2.imencode('.jpg', frame)
+            if ret:
+                frame_bytes = buffer.tobytes() # This is correct. imencode returns a numpy array.
+                return True, change_description, frame_bytes
+            else:
+                return True, change_description, None # Encoding failed
+
+        return False, "No significant change detected.", None
+
+
+    def process_frame_for_changes(self) -> tuple[bool, str, bytes | None]:
+        """
+        Captures a frame and detects significant changes. Limits processing by FPS.
+
+        Returns:
+            Tuple: (change_detected, change_description, frame_bytes_if_change)
+        """
+        current_time = time.time()
+        if self.fps_limit and (current_time - self.last_processed_time) < (1.0 / self.fps_limit):
+            # Skip processing this frame to maintain FPS limit
+            time.sleep(0.01) # Small sleep to prevent busy-looping if camera is faster
+            return False, "FPS limit, frame skipped", None
+
+        self.last_processed_time = current_time
+
+        frame = self.get_frame()
+        if frame is None:
+            return False, "Failed to get frame", None
+
+        return self.detect_significant_change(frame)
+
+
+    # Optional: Person detection (can be slow)
+    # def detect_persons(self, frame: np.ndarray) -> tuple[bool, str, np.ndarray | None]:
+    #     if frame is None:
+    #         return False, "No frame for person detection", None
+
+    #     (rects, weights) = self.hog.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=1.05)
+
+    #     if len(rects) > 0:
+    #         # For now, just signal if any person is detected.
+    #         # Could be enhanced to count, track entries/exits relative to a previous state.
+    #         # for (x, y, w, h) in rects:
+    #         #     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2) # Draw on original for viz
+
+    #         change_description = f"{len(rects)} person(s) detected in the view."
+    #         ret, buffer = cv2.imencode('.jpg', frame) # Send frame with detected people
+    #         if ret:
+    #             frame_bytes = buffer.tobytes() # Corrected
+    #             return True, change_description, frame_bytes
+    #         else:
+    #             return True, change_description, None
+    #     return False, "No persons detected.", None
+
+
+if __name__ == '__main__':
+    print("Testing video_utils.py...")
+    # This test requires a camera. If no camera, it will try to proceed but fail gracefully.
+    monitor = VideoMonitor(camera_index=0, fps_limit=1) # Limit to 1 FPS for this test
+
+    if not monitor.start_capture():
+        print("Failed to start camera capture. Ensure a camera is connected and drivers are working.")
+        print("If running in a headless environment, this test might not be fully effective without a virtual camera.")
+    else:
+        print("Camera capture started. Will monitor for changes for a few seconds.")
+        start_time = time.time()
+        frames_processed = 0
+        try:
+            while time.time() - start_time < 10: # Run for 10 seconds
+                changed, description, frame_bytes = monitor.process_frame_for_changes()
+                if changed:
+                    print(f"Change detected: {description}")
+                    if frame_bytes:
+                        print(f"Frame data size: {len(frame_bytes)} bytes")
+                        # with open("detected_change.jpg", "wb") as f: # Option to save frame
+                        #     f.write(frame_bytes)
+                        # print("Saved detected_change.jpg")
+                elif description == "Failed to get frame":
+                    print("Stopping test due to frame capture failure.")
+                    break
+                elif description != "FPS limit, frame skipped": # Don't flood with "no change"
+                     print(f"Status: {description} (Frame: {frames_processed})")
+
+                frames_processed +=1
+                # A short sleep is important if not FPS limited in process_frame_for_changes,
+                # or if camera is very fast. Here it's mainly for responsive exit.
+                time.sleep(0.05)
+        except KeyboardInterrupt:
+            print("Test interrupted by user.")
+        finally:
+            monitor.stop_capture()
+            print("Video utils testing finished.")
diff --git a/in_progress/streaming-test/app/web_interface.py b/in_progress/streaming-test/app/web_interface.py
new file mode 100644
index 000000000..818d8f7c0
--- /dev/null
+++ b/in_progress/streaming-test/app/web_interface.py
@@ -0,0 +1,184 @@
+from flask import Flask, render_template, Response, request, jsonify, stream_with_context
+import time
+import json # For SSE
+from app.agent import MultimodalAgent # Assuming agent is in app.agent
+from app.config import WEB_HOST, WEB_PORT, MODEL_NAME # Assuming these are in config
+import os
+from dotenv import load_dotenv
+import queue # For SSE messages
+
+# --- Global Agent Initialization ---
+# Load .env file from the parent directory of 'app'
+dotenv_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env')
+load_dotenv(dotenv_path=dotenv_path)
+
+project_id = os.getenv("GOOGLE_CLOUD_PROJECT")
+location = os.getenv("GOOGLE_CLOUD_LOCATION")
+use_vertex = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "False").lower() == "true"
+
+agent_instance = None
+agent_event_queue = queue.Queue() # Queue for SSE messages from agent
+
+def get_agent():
+    global agent_instance
+    if agent_instance is None:
+        if use_vertex and project_id and location:
+            print(f"WEB_INTERFACE: Initializing Agent with project: {project_id}, loc: {location}, model: {MODEL_NAME}")
+            agent_instance = MultimodalAgent(
+                project_id=project_id,
+                location=location,
+                model_name=MODEL_NAME,
+                camera_index=0
+            )
+        else:
+            print("WEB_INTERFACE: Agent cannot be initialized. Check .env settings.")
+    return agent_instance
+
+# --- Flask App Setup ---
+web_app = Flask(__name__)
+
+def generate_video_frames():
+    local_agent = get_agent()
+    if not local_agent or not local_agent.video_monitor:
+        print("WEB_INTERFACE: Video frames - Agent or video monitor not available.")
+        return
+
+    if not local_agent.video_monitor.cap or not local_agent.video_monitor.cap.isOpened():
+        print("WEB_INTERFACE: Video frames - Camera not started. Attempting to start.")
+        if not local_agent.video_monitor.start_capture():
+            print("WEB_INTERFACE: Video frames - Failed to start camera for web feed.")
+            return
+
+    print("WEB_INTERFACE: Starting video frame generation for feed.")
+    while True:
+        if not local_agent.video_monitor.cap or not local_agent.video_monitor.cap.isOpened():
+            print("WEB_INTERFACE: Video stream - camera became unavailable.")
+            break
+
+        frame = local_agent.video_monitor.get_frame()
+        if frame is None:
+            time.sleep(0.1)
+            continue
+
+        ret, buffer = local_agent.video_monitor.cv2.imencode('.jpg', frame)
+        if not ret:
+            continue
+
+        frame_bytes = buffer.tobytes()
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
+
+        time.sleep(1.0 / local_agent.video_monitor.fps_limit if local_agent.video_monitor.fps_limit and local_agent.video_monitor.fps_limit > 0 else 0.03)
+
+
+@web_app.route('/')
+def index():
+    return render_template('index.html')
+
+@web_app.route('/video_feed')
+def video_feed():
+    return Response(generate_video_frames(),
+                    mimetype='multipart/x-mixed-replace; boundary=frame')
+
+@web_app.route('/start_interaction', methods=['GET'])
+def start_interaction():
+    print("WEB_INTERFACE: /start_interaction called.")
+    local_agent = get_agent()
+    if not local_agent:
+        return jsonify({"error": "Agent not initialized. Check server logs and .env settings."}), 500
+    try:
+        local_agent.start_chat()
+        agent_event_queue.put({"type": "status", "message": "Agent interaction started. Video capture active."})
+        print("WEB_INTERFACE: Agent interaction started successfully.")
+        return jsonify({"status": "Agent interaction started. Video capture active."})
+    except Exception as e:
+        print(f"WEB_INTERFACE: Error starting agent interaction: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/stop_interaction', methods=['GET'])
+def stop_interaction():
+    print("WEB_INTERFACE: /stop_interaction called.")
+    local_agent = get_agent()
+    if not local_agent:
+        # This case might be okay if user stops an agent that couldn't init
+        return jsonify({"status": "Agent was not initialized or already stopped."})
+    try:
+        local_agent.stop_chat()
+        agent_event_queue.put({"type": "status", "message": "Agent stopped. Video capture released."})
+        print("WEB_INTERFACE: Agent interaction stopped successfully.")
+        return jsonify({"status": "Agent interaction stopped."})
+    except Exception as e:
+        print(f"WEB_INTERFACE: Error stopping agent interaction: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/send_chat_message', methods=['POST'])
+def send_chat_message():
+    print("WEB_INTERFACE: /send_chat_message called.")
+    data = request.get_json()
+    user_message = data.get('message')
+    if not user_message:
+        return jsonify({"error": "No message provided."}), 400
+
+    local_agent = get_agent()
+    if not local_agent:
+        return jsonify({"error": "Agent not available."}), 503
+
+    if not local_agent.chat:
+        print("WEB_INTERFACE: Chat not active in send_chat_message, starting chat...")
+        local_agent.start_chat()
+
+    try:
+        model_reply = local_agent.send_text_message(user_message)
+        return jsonify({"reply": model_reply})
+    except Exception as e:
+        print(f"WEB_INTERFACE: Error processing chat message: {e}")
+        return jsonify({"error": str(e)}), 500
+
+@web_app.route('/send_voice_message', methods=['POST'])
+def send_voice_message():
+    print("WEB_INTERFACE: /send_voice_message called.")
+    local_agent = get_agent()
+    if not local_agent:
+        return jsonify({"error": "Agent not available."}), 503
+
+    if not local_agent.chat:
+        print("WEB_INTERFACE: Chat not active in send_voice_message, starting chat...")
+        local_agent.start_chat()
+
+    print("WEB_INTERFACE: Calling agent.handle_voice_interaction...")
+    try:
+        # The agent's handle_voice_interaction method is blocking.
+        # Results (transcription, model text) are pushed to SSE queue from within the agent method.
+        local_agent.handle_voice_interaction(record_duration_seconds=5, event_queue=agent_event_queue)
+        print("WEB_INTERFACE: agent.handle_voice_interaction completed.")
+        return jsonify({"status": "Voice interaction processed. Check chat for updates via SSE."})
+    except Exception as e:
+        print(f"WEB_INTERFACE: Error during voice interaction: {e}")
+        return jsonify({"error": str(e)}), 500
+
+def generate_agent_events():
+    print("WEB_INTERFACE: SSE client connected. Starting event stream.")
+    try:
+        while True:
+            message = agent_event_queue.get()
+            # print(f"WEB_INTERFACE SSE: Sending event: {message}") # Can be noisy
+            yield f"data: {json.dumps(message)}\n\n"
+            agent_event_queue.task_done()
+            time.sleep(0.01) # Prevent busy loop if queue is rapidly filled
+    except GeneratorExit:
+        print("WEB_INTERFACE: SSE client disconnected.")
+    except Exception as e:
+        print(f"WEB_INTERFACE: Error in SSE event generator: {e}")
+
+
+@web_app.route('/agent_events')
+def agent_events():
+    return Response(stream_with_context(generate_agent_events()), mimetype="text/event-stream")
+
+if __name__ == '__main__':
+    print("Starting Flask web server directly from web_interface.py (for testing)...")
+    print(f"Agent use_vertex: {use_vertex}, project: {project_id}, location: {location}")
+    if not (use_vertex and project_id and location):
+        print("WARNING: Agent will not be fully functional due to missing .env vars.")
+
+    web_app.run(host=WEB_HOST, port=WEB_PORT, debug=True, threaded=True, use_reloader=False)
diff --git a/in_progress/streaming-test/config.py b/in_progress/streaming-test/config.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test/get-pip.py b/in_progress/streaming-test/get-pip.py
new file mode 100644
index 000000000..60198eb49
--- /dev/null
+++ b/in_progress/streaming-test/get-pip.py
@@ -0,0 +1,28579 @@
+#!/usr/bin/env python
+#
+# Hi There!
+#
+# You may be wondering what this giant blob of binary data here is, you might
+# even be worried that we're up to something nefarious (good for you for being
+# paranoid!). This is a base85 encoding of a zip file, this zip file contains
+# an entire copy of pip (version 25.1.1).
+#
+# Pip is a thing that installs packages, pip itself is a package that someone
+# might want to install, especially if they're looking to run this get-pip.py
+# script. Pip has a lot of code to deal with the security of installing
+# packages, various edge cases on various platforms, and other such sort of
+# "tribal knowledge" that has been encoded in its code base. Because of this
+# we basically include an entire copy of pip inside this blob. We do this
+# because the alternatives are attempt to implement a "minipip" that probably
+# doesn't do things correctly and has weird edge cases, or compress pip itself
+# down into a single file.
+#
+# If you're wondering how this is created, it is generated using
+# `scripts/generate.py` in https://github.com/pypa/get-pip.
+
+import sys
+
+this_python = sys.version_info[:2]
+min_version = (3, 9)
+if this_python < min_version:
+    message_parts = [
+        "This script does not work on Python {}.{}.".format(*this_python),
+        "The minimum supported Python version is {}.{}.".format(*min_version),
+        "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python),
+    ]
+    print("ERROR: " + " ".join(message_parts))
+    sys.exit(1)
+
+
+import os.path
+import pkgutil
+import shutil
+import tempfile
+import argparse
+import importlib
+from base64 import b85decode
+
+
+def include_setuptools(args):
+    """
+    Install setuptools only if absent, not excluded and when using Python <3.12.
+    """
+    cli = not args.no_setuptools
+    env = not os.environ.get("PIP_NO_SETUPTOOLS")
+    absent = not importlib.util.find_spec("setuptools")
+    python_lt_3_12 = this_python < (3, 12)
+    return cli and env and absent and python_lt_3_12
+
+
+def include_wheel(args):
+    """
+    Install wheel only if absent, not excluded and when using Python <3.12.
+    """
+    cli = not args.no_wheel
+    env = not os.environ.get("PIP_NO_WHEEL")
+    absent = not importlib.util.find_spec("wheel")
+    python_lt_3_12 = this_python < (3, 12)
+    return cli and env and absent and python_lt_3_12
+
+
+def determine_pip_install_arguments():
+    pre_parser = argparse.ArgumentParser()
+    pre_parser.add_argument("--no-setuptools", action="store_true")
+    pre_parser.add_argument("--no-wheel", action="store_true")
+    pre, args = pre_parser.parse_known_args()
+
+    args.append("pip")
+
+    if include_setuptools(pre):
+        args.append("setuptools")
+
+    if include_wheel(pre):
+        args.append("wheel")
+
+    return ["install", "--upgrade", "--force-reinstall"] + args
+
+
+def monkeypatch_for_cert(tmpdir):
+    """Patches `pip install` to provide default certificate with the lowest priority.
+
+    This ensures that the bundled certificates are used unless the user specifies a
+    custom cert via any of pip's option passing mechanisms (config, env-var, CLI).
+
+    A monkeypatch is the easiest way to achieve this, without messing too much with
+    the rest of pip's internals.
+    """
+    from pip._internal.commands.install import InstallCommand
+
+    # We want to be using the internal certificates.
+    cert_path = os.path.join(tmpdir, "cacert.pem")
+    with open(cert_path, "wb") as cert:
+        cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem"))
+
+    install_parse_args = InstallCommand.parse_args
+
+    def cert_parse_args(self, args):
+        if not self.parser.get_default_values().cert:
+            # There are no user provided cert -- force use of bundled cert
+            self.parser.defaults["cert"] = cert_path  # calculated above
+        return install_parse_args(self, args)
+
+    InstallCommand.parse_args = cert_parse_args
+
+
+def bootstrap(tmpdir):
+    monkeypatch_for_cert(tmpdir)
+
+    # Execute the included pip and use it to install the latest pip and
+    # any user-requested packages from PyPI.
+    from pip._internal.cli.main import main as pip_entry_point
+    args = determine_pip_install_arguments()
+    sys.exit(pip_entry_point(args))
+
+
+def main():
+    tmpdir = None
+    try:
+        # Create a temporary working directory
+        tmpdir = tempfile.mkdtemp()
+
+        # Unpack the zipfile into the temporary directory
+        pip_zip = os.path.join(tmpdir, "pip.zip")
+        with open(pip_zip, "wb") as fp:
+            fp.write(b85decode(DATA.replace(b"\n", b"")))
+
+        # Add the zipfile to sys.path so that we can import it
+        sys.path.insert(0, pip_zip)
+
+        # Run the bootstrap
+        bootstrap(tmpdir=tmpdir)
+    finally:
+        # Clean up our temporary working directory
+        if tmpdir:
+            shutil.rmtree(tmpdir, ignore_errors=True)
+
+
+DATA = b"""
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+"""
+
+
+if __name__ == "__main__":
+    main()
diff --git a/in_progress/streaming-test/requirements.txt b/in_progress/streaming-test/requirements.txt
new file mode 100644
index 000000000..f24f6c12c
--- /dev/null
+++ b/in_progress/streaming-test/requirements.txt
@@ -0,0 +1,12 @@
+google-cloud-aiplatform>=1.0.0
+opencv-python-headless
+Flask>=2.0.0
+python-dotenv
+sounddevice
+scipy
+google-cloud-speech
+google-cloud-texttospeech
+# Add other initial dependencies here, e.g., for audio:
+# SoundDevice # This was a comment, actual is sounddevice
+# google-cloud-speech # This was a comment, actual is google-cloud-speech
+# google-cloud-texttospeech # This was a comment, actual is google-cloud-texttospeech
diff --git a/in_progress/streaming-test/run.py b/in_progress/streaming-test/run.py
new file mode 100644
index 000000000..e4236edea
--- /dev/null
+++ b/in_progress/streaming-test/run.py
@@ -0,0 +1,114 @@
+import sys
+import os
+import time # Added time for agent_background_tasks
+import threading # For agent's background tasks
+
+# Add the project root to the Python path
+sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
+
+from app.web_interface import web_app, get_agent, agent_event_queue # Import Flask app and agent instance
+from app.config import WEB_HOST, WEB_PORT
+# We might need to import agent parts if we run its loop here
+
+# --- Agent Background Processing Loop ---
+# This is a conceptual loop. The actual implementation of how the agent
+# processes audio/video continuously and interacts with the model needs refinement.
+# It should place results/events onto the agent_event_queue for the web UI.
+
+agent_stop_event = threading.Event()
+
+def agent_background_tasks():
+    print("Agent background task thread started.")
+    agent = get_agent() # Initialize/get the agent instance
+    if not agent:
+        print("Agent not initialized, background tasks cannot run.")
+        return
+
+    # Ensure agent is "active" (chat started, video capture on)
+    # This might be better controlled via web UI (start/stop buttons)
+    # For now, let's assume if background tasks run, agent should be active.
+    # However, start_chat() initializes the Vertex AI chat, which costs.
+    # So, let's make it start only on web request.
+    # The video feed itself will start capture if needed.
+    # This background task will primarily focus on non-web-request-driven events.
+
+    # if not agent.chat:
+    #    agent.start_chat() # This also starts video capture
+
+    last_audio_check_time = time.time()
+    last_video_check_time = time.time()
+
+    while not agent_stop_event.is_set():
+        current_time = time.time()
+
+        # 1. Check for video changes (if video is active)
+        if agent.video_monitor and agent.video_monitor.cap and agent.video_monitor.cap.isOpened():
+            if current_time - last_video_check_time > 2: # Check every 2 seconds (adjust as needed)
+                # print("Background: Checking for video changes...") # Can be noisy
+                # The check_for_video_changes_and_comment method itself might be slow
+                # if it involves a model call.
+                # It needs to be adapted to put its findings on agent_event_queue.
+
+                # Simplified: Assume agent.check_for_video_changes_and_comment() is modified
+                # to return the comment, which we then put on the queue.
+                # Or, it directly puts on the queue. Let's assume the latter for cleanliness.
+
+                # --- Modification for agent.py's check_for_video_changes_and_comment ---
+                # It should look like:
+                # if changed and frame_bytes:
+                #    ... model_response = self.send_message(...)
+                #    if model_response:
+                #        agent_event_queue.put({"type": "video_change_comment", "comment": model_response})
+                # -------------------------------------------------------------------------
+                try:
+                    # This call will now internally try to put to agent_event_queue if a change is commented on by model
+                    agent.check_for_video_changes_and_comment(event_queue=agent_event_queue)
+                except Exception as e:
+                    print(f"Error in background video check: {e}")
+                last_video_check_time = current_time
+
+        # 2. Placeholder for continuous audio processing (if we were doing live mic streaming)
+        # For example, if agent had a method like `process_live_audio_chunk()`
+        # that transcribed and potentially sent to model, and put results on queue.
+        # if agent.is_listening_for_voice and (current_time - last_audio_check_time > 0.1): # Process frequently
+        #     transcription, model_response_text = agent.process_live_audio_chunk()
+        #     if transcription:
+        #         agent_event_queue.put({"type": "transcription", "text": transcription})
+        #     if model_response_text:
+        #         agent_event_queue.put({"type": "model_response_audio_text", "text": model_response_text})
+        #         # Synthesize and play audio would happen on client or if agent has speakers
+        #     last_audio_check_time = current_time
+
+
+        time.sleep(0.5) # Loop frequency for background checks
+
+    print("Agent background task thread stopped.")
+
+
+if __name__ == "__main__":
+    print("Starting application via run.py...")
+
+    # Initialize agent (important to do this before starting Flask or background thread)
+    print("Initializing agent instance...")
+    agent = get_agent()
+    if not agent:
+        print("CRITICAL: Agent failed to initialize. Web interface may not work correctly.")
+        # sys.exit(1) # Optionally exit if agent is critical for any operation
+
+    # Start agent's background processing tasks in a separate thread
+    # These tasks would handle things like continuous video monitoring for changes,
+    # or live audio stream processing if implemented.
+    print("Starting agent background tasks thread...")
+    background_thread = threading.Thread(target=agent_background_tasks, daemon=True)
+    background_thread.start()
+
+    print(f"Starting Flask web server on {WEB_HOST}:{WEB_PORT}...")
+    # Use threaded=True for Flask dev server to handle concurrent requests (e.g. video feed + SSE)
+    # For production, use a proper WSGI server like Gunicorn or Waitress.
+    web_app.run(host=WEB_HOST, port=WEB_PORT, debug=False, threaded=True, use_reloader=False)
+
+    # Signal the background thread to stop when Flask server exits
+    print("Flask server stopped. Signaling agent background tasks to stop...")
+    agent_stop_event.set()
+    background_thread.join(timeout=5) # Wait for thread to finish
+    print("Application shut down.")
diff --git a/in_progress/streaming-test/run_tests.py b/in_progress/streaming-test/run_tests.py
new file mode 100644
index 000000000..7f55ca921
--- /dev/null
+++ b/in_progress/streaming-test/run_tests.py
@@ -0,0 +1,45 @@
+import unittest
+import sys
+import os
+from unittest.mock import MagicMock
+
+# --- Global mock for sounddevice ---
+# This must be done before any module (app code or test code) tries to import sounddevice
+mock_sd_module = MagicMock(name="GlobalMockSoundDeviceModule")
+# Make common sounddevice functions available on the mock if they are called directly
+mock_sd_module.rec = MagicMock(name="sd_rec_mock")
+mock_sd_module.wait = MagicMock(name="sd_wait_mock")
+mock_sd_module.play = MagicMock(name="sd_play_mock")
+mock_sd_module.query_devices = MagicMock(return_value=[{'name': 'mock_mic', 'max_input_channels': 1, 'default_samplerate': 44100.0}]) # Example
+mock_sd_module.RawInputStream = MagicMock()
+mock_sd_module.InputStream = MagicMock()
+mock_sd_module.OutputStream = MagicMock()
+mock_sd_module.Stream = MagicMock()
+
+
+sys.modules['sounddevice'] = mock_sd_module
+# --- End global mock ---
+
+# Add project root to sys.path to allow imports from app
+# This should come AFTER the global mock is inserted into sys.modules
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+if __name__ == "__main__":
+    # Discover and run tests in the 'tests' directory
+    # Assumes this script is in streaming-test/ (which is the project root for these tests)
+    print("Attempting to discover and run tests...")
+    loader = unittest.TestLoader()
+    try:
+        suite = loader.discover('tests')
+        print(f"Test suite loaded: {suite.countTestCases()} tests found.")
+    except Exception as e:
+        print(f"Error during test discovery: {e}")
+        sys.exit(1)
+
+    runner = unittest.TextTestRunner(verbosity=2) # Increased verbosity
+    result = runner.run(suite)
+
+    # Exit with a non-zero status if any tests failed
+    if not result.wasSuccessful():
+        sys.exit(1)
+    sys.exit(0)
diff --git a/in_progress/streaming-test/tests/__init__.py b/in_progress/streaming-test/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test/tests/test_agent.py b/in_progress/streaming-test/tests/test_agent.py
new file mode 100644
index 000000000..6e4f06e89
--- /dev/null
+++ b/in_progress/streaming-test/tests/test_agent.py
@@ -0,0 +1,156 @@
+import unittest
+from unittest.mock import patch, MagicMock, PropertyMock
+import sys
+import os
+import time # for time.time() in agent if __name__ == '__main__'
+
+# --- Global Mocking for vertexai (BEFORE app.agent is imported) ---
+os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'
+os.environ['GOOGLE_CLOUD_PROJECT'] = 'test-project'
+os.environ['GOOGLE_CLOUD_LOCATION'] = 'test-location'
+
+mock_vertexai_module = MagicMock(name="vertexai_mock")
+mock_generative_models_module = MagicMock(name="generative_models_mock")
+mock_generative_model_class = MagicMock(name="GenerativeModelClassMock")
+mock_model_instance = MagicMock(name="GenerativeModelInstanceMock")
+mock_chat_instance = MagicMock(name="ChatInstanceMock")
+
+# --- Part Mocking ---
+mock_part_class = MagicMock(name="PartMock")
+# Configure from_text and from_data to return mocks that have a to_dict method
+mock_text_part_instance = MagicMock(name="TextPartInstance")
+mock_text_part_instance.to_dict.return_value = {"text": "mocked text"} # Example to_dict content
+mock_data_part_instance = MagicMock(name="DataPartInstance")
+mock_data_part_instance.to_dict.return_value = {"inline_data": {"mime_type": "image/jpeg", "data": "mocked_data"}}
+
+mock_part_class.from_text.return_value = mock_text_part_instance
+mock_part_class.from_data.return_value = mock_data_part_instance
+# --- End Part Mocking ---
+
+mock_vertexai_module.init = MagicMock(name="vertexai_init_mock")
+mock_vertexai_module.generative_models = mock_generative_models_module
+mock_generative_models_module.GenerativeModel = mock_generative_model_class
+mock_generative_model_class.return_value = mock_model_instance
+mock_generative_models_module.Part = mock_part_class
+mock_model_instance.start_chat.return_value = mock_chat_instance
+
+sys.modules['vertexai'] = mock_vertexai_module
+sys.modules['vertexai.generative_models'] = mock_generative_models_module
+# --- End Global Mocking ---
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from app.agent import MultimodalAgent
+
+@patch('app.agent.VideoMonitor')
+@patch('app.agent.record_audio')
+@patch('app.agent.transcribe_audio_bytes')
+@patch('app.agent.synthesize_text_to_audio_bytes')
+@patch('app.agent.play_audio_bytes')
+@patch('app.agent.load_dotenv')
+class TestMultimodalAgent(unittest.TestCase):
+
+    def setUp(self):
+        mock_vertexai_module.init.reset_mock()
+        mock_generative_model_class.reset_mock()
+        mock_generative_model_class.return_value = mock_model_instance
+        mock_model_instance.start_chat.reset_mock()
+        mock_model_instance.start_chat.return_value = mock_chat_instance
+        mock_chat_instance.reset_mock()
+        mock_chat_instance.send_message.reset_mock()
+
+        mock_part_class.from_data.reset_mock()
+        mock_part_class.from_text.reset_mock()
+        # Ensure return values are reassigned for each test if they could be altered
+        mock_part_class.from_text.return_value = mock_text_part_instance
+        mock_part_class.from_data.return_value = mock_data_part_instance
+        # Reset to_dict mocks if necessary (though typically defined once)
+        mock_text_part_instance.to_dict.reset_mock(return_value=MagicMock()) # Avoid state leakage
+        mock_text_part_instance.to_dict.return_value = {"text": "mocked text"}
+        mock_data_part_instance.to_dict.reset_mock(return_value=MagicMock())
+        mock_data_part_instance.to_dict.return_value = {"inline_data": {"mime_type": "image/jpeg", "data": "mocked_data"}}
+
+
+    def test_agent_initialization(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        agent = MultimodalAgent(project_id="test-proj", location="test-loc", model_name="gemini-test")
+        mock_vertexai_module.init.assert_called_once_with(project="test-proj", location="test-loc")
+        mock_generative_model_class.assert_called_once_with("gemini-test")
+        MockVideoMonitor.assert_called_once_with(camera_index=0, fps_limit=1)
+        self.assertIsNotNone(agent.model)
+
+    def test_start_chat(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = None
+        mock_video_monitor_instance.start_capture.return_value = True
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.start_chat()
+        mock_model_instance.start_chat.assert_called_once_with(response_validation=False)
+        self.assertEqual(agent.chat, mock_chat_instance)
+        mock_video_monitor_instance.start_capture.assert_called_once()
+
+    def test_stop_chat(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = True
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.stop_chat()
+        mock_video_monitor_instance.stop_capture.assert_called_once()
+        self.assertIsNone(agent.chat)
+
+    def test_send_text_message_success(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_chat_instance.send_message.return_value = MagicMock(text="Model reply")
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        reply = agent.send_text_message("Hello agent")
+        self.assertEqual(reply, "Model reply")
+        mock_chat_instance.send_message.assert_called_with("Hello agent")
+
+    def test_send_message_with_image(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_chat_instance.send_message.return_value = MagicMock(text="Understood image")
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        # Pass the actual mock instance for image_parts
+        reply = agent.send_message(text_prompt="Describe this", image_parts=[mock_data_part_instance])
+        self.assertEqual(reply, "Understood image")
+        sent_parts_list = mock_chat_instance.send_message.call_args[0][0]
+        self.assertIn(mock_text_part_instance, sent_parts_list)
+        self.assertIn(mock_data_part_instance, sent_parts_list)
+
+    def test_check_for_video_changes_and_comment_detected(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = MagicMock()
+        mock_video_monitor_instance.cap.isOpened.return_value = True
+        mock_video_monitor_instance.process_frame_for_changes.return_value = (True, "Motion detected", b"jpeg_bytes")
+        mock_chat_instance.send_message.return_value = MagicMock(text="I see motion!")
+        mock_event_queue = MagicMock()
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.check_for_video_changes_and_comment(event_queue=mock_event_queue)
+        mock_video_monitor_instance.process_frame_for_changes.assert_called_once()
+        mock_part_class.from_data.assert_called_with(b"jpeg_bytes", mime_type="image/jpeg")
+        sent_parts_list = mock_chat_instance.send_message.call_args[0][0]
+        self.assertIn(mock_text_part_instance, sent_parts_list)
+        self.assertIn(mock_data_part_instance, sent_parts_list)
+        mock_event_queue.put.assert_called_once_with(
+            {"type": "video_change_comment", "comment": "I see motion!"}
+        )
+
+    def test_handle_voice_interaction(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        MockRecord.return_value = b"recorded_audio_bytes"
+        MockTranscribe.return_value = "user speech text"
+        mock_chat_instance.send_message.return_value = MagicMock(text="model speech text")
+        MockSynth.return_value = b"synthesized_audio_bytes"
+        mock_event_queue = MagicMock()
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.handle_voice_interaction(record_duration_seconds=3, event_queue=mock_event_queue)
+        MockRecord.assert_called_once_with(duration_seconds=3)
+        MockTranscribe.assert_called_once_with(b"recorded_audio_bytes")
+        mock_chat_instance.send_message.assert_called_with("user speech text")
+        MockSynth.assert_called_once_with("model speech text")
+        MockPlayAudio.assert_called_once_with(b"synthesized_audio_bytes", output_format="mp3")
+        self.assertIn(unittest.mock.call({"type": "transcription", "text": "user speech text"}), mock_event_queue.put.call_args_list)
+        self.assertIn(unittest.mock.call({"type": "model_response_audio_text", "text": "model speech text"}), mock_event_queue.put.call_args_list)
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test/tests/test_audio_utils.py b/in_progress/streaming-test/tests/test_audio_utils.py
new file mode 100644
index 000000000..091dec45c
--- /dev/null
+++ b/in_progress/streaming-test/tests/test_audio_utils.py
@@ -0,0 +1,153 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import numpy as np
+from scipy.io.wavfile import write as write_wav
+import io
+import sys
+import os
+
+# Mock sounddevice is done globally in run_tests.py.
+# We get a reference to that global mock here.
+# This ensures that app.audio_utils, when it 'import sounddevice as sd', gets the global mock.
+mock_sd_module = sys.modules.get('sounddevice', MagicMock(name="FallbackMockSoundDeviceModule"))
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from app.audio_utils import record_audio, transcribe_audio_bytes, synthesize_text_to_audio_bytes, play_audio_bytes
+from app.config import AUDIO_SAMPLE_RATE
+
+class TestAudioUtils(unittest.TestCase):
+
+    def setUp(self):
+        # Reset the attributes of the global mock_sd_module for each test
+        # This is important because mock_sd_module is shared state modified by run_tests.py
+        mock_sd_module.rec = MagicMock(name="sd_rec_mock_from_setUp")
+        mock_sd_module.wait = MagicMock(name="sd_wait_mock_from_setUp")
+        mock_sd_module.play = MagicMock(name="sd_play_mock_from_setUp")
+        # Ensure other methods that might be called are also reset if necessary
+        mock_sd_module.reset_mock() # Resets call counts etc. on the main mock
+                                    # and also replaces attributes like .rec with new MagicMocks.
+                                    # So, re-assign them if specific naming is desired or if they need to be shared.
+        # Re-establish well-known mocks on the global mock after reset_mock()
+        # These will be the mocks that app.audio_utils.sd.* actually calls.
+        # The 'global' keyword here was causing a SyntaxError and is not needed
+        # as mock_sd_module is already in the module's global scope.
+        mock_sd_module.rec = MagicMock(name="sd.rec")
+        mock_sd_module.wait = MagicMock(name="sd.wait")
+        mock_sd_module.play = MagicMock(name="sd.play")
+
+
+    def generate_dummy_wav_bytes(self, duration_seconds=1, sample_rate=AUDIO_SAMPLE_RATE, channels=1):
+        num_samples = int(duration_seconds * sample_rate)
+        frequency = 440
+        t = np.linspace(0, duration_seconds, num_samples, False)
+        audio_data = (0.5 * np.sin(2 * np.pi * frequency * t) * 32767).astype(np.int16)
+        if channels == 2:
+            audio_data = np.column_stack((audio_data, audio_data))
+        byte_io = io.BytesIO()
+        write_wav(byte_io, sample_rate, audio_data)
+        return byte_io.getvalue()
+
+    def test_record_audio_success(self):
+        duration = 1
+        sample_rate = 16000
+        channels = 1
+        expected_samples = duration * sample_rate
+        # This is a REAL numpy array that scipy.io.wavfile.write expects.
+        dummy_numpy_array = np.random.randint(-32768, 32767, size=(expected_samples, channels), dtype=np.int16)
+
+        # Configure the mock that app.audio_utils.sd.rec will resolve to
+        mock_sd_module.rec.return_value = dummy_numpy_array
+
+        audio_bytes = record_audio(duration_seconds=duration, sample_rate=sample_rate, channels=channels)
+
+        mock_sd_module.rec.assert_called_once_with(int(duration * sample_rate), samplerate=sample_rate, channels=channels, dtype='int16')
+        mock_sd_module.wait.assert_called_once()
+        self.assertIsNotNone(audio_bytes)
+        self.assertIsInstance(audio_bytes, bytes)
+
+    def test_record_audio_failure(self):
+        # Configure the mock that app.audio_utils.sd.rec will resolve to
+        mock_sd_module.rec.side_effect = Exception("Recording failed")
+
+        audio_bytes = record_audio(duration_seconds=1)
+
+        self.assertIsNone(audio_bytes)
+        mock_sd_module.rec.assert_called_once() # Should still be called once
+
+
+    @patch('app.audio_utils.speech.SpeechClient')
+    def test_transcribe_audio_bytes_success(self, MockSpeechClient):
+        mock_client_instance = MockSpeechClient.return_value
+        mock_recognize_response = MagicMock()
+        mock_alternative = MagicMock()
+        mock_alternative.transcript = "hello world"
+        mock_result = MagicMock()
+        mock_result.alternatives = [mock_alternative]
+        mock_recognize_response.results = [mock_result]
+        mock_client_instance.recognize.return_value = mock_recognize_response
+
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio, sample_rate=AUDIO_SAMPLE_RATE)
+        self.assertEqual(transcript, "hello world")
+
+    @patch('app.audio_utils.speech.SpeechClient', side_effect=Exception("GCS API Error"))
+    def test_transcribe_audio_bytes_api_failure(self, MockSpeechClient):
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio)
+        self.assertIsNone(transcript)
+
+    def test_transcribe_audio_bytes_no_input(self):
+        transcript = transcribe_audio_bytes(None)
+        self.assertIsNone(transcript)
+
+    @patch('app.audio_utils.speech.SpeechClient')
+    def test_transcribe_audio_bytes_no_results(self, MockSpeechClient):
+        mock_client_instance = MockSpeechClient.return_value
+        mock_recognize_response = MagicMock()
+        mock_recognize_response.results = []
+        mock_client_instance.recognize.return_value = mock_recognize_response
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio, sample_rate=AUDIO_SAMPLE_RATE)
+        self.assertEqual(transcript, "")
+
+    @patch('app.audio_utils.texttospeech.TextToSpeechClient')
+    def test_synthesize_text_to_audio_bytes_success(self, MockTextToSpeechClient):
+        mock_client_instance = MockTextToSpeechClient.return_value
+        mock_synthesize_response = MagicMock()
+        mock_synthesize_response.audio_content = b"dummy_audio_content"
+        mock_client_instance.synthesize_speech.return_value = mock_synthesize_response
+        audio_bytes = synthesize_text_to_audio_bytes("hello")
+        self.assertEqual(audio_bytes, b"dummy_audio_content")
+
+    @patch('app.audio_utils.texttospeech.TextToSpeechClient', side_effect=Exception("TTS API Error"))
+    def test_synthesize_text_to_audio_bytes_api_failure(self, MockTextToSpeechClient):
+        audio_bytes = synthesize_text_to_audio_bytes("test text")
+        self.assertIsNone(audio_bytes)
+
+    def test_synthesize_text_to_audio_bytes_no_input(self):
+        audio_bytes = synthesize_text_to_audio_bytes("")
+        self.assertIsNone(audio_bytes)
+
+    @patch('scipy.io.wavfile.read')
+    def test_play_audio_bytes_wav(self, mock_scipy_read_wav):
+        dummy_wav_data = np.array([1, 2, 3], dtype=np.int16)
+        dummy_sample_rate = AUDIO_SAMPLE_RATE
+        mock_scipy_read_wav.return_value = (dummy_sample_rate, dummy_wav_data)
+
+        wav_bytes = self.generate_dummy_wav_bytes()
+        play_audio_bytes(wav_bytes, output_format="wav", sample_rate=dummy_sample_rate)
+
+        mock_scipy_read_wav.assert_called_once()
+        self.assertIsInstance(mock_scipy_read_wav.call_args[0][0], io.BytesIO)
+        mock_sd_module.play.assert_called_once_with(dummy_wav_data, samplerate=dummy_sample_rate)
+        mock_sd_module.wait.assert_called_once()
+
+    @patch('app.audio_utils.time.sleep')
+    def test_play_audio_bytes_mp3_placeholder(self, mock_time_sleep):
+        mp3_bytes = b"dummy_mp3_data"
+        play_audio_bytes(mp3_bytes, output_format="mp3")
+        mock_time_sleep.assert_called()
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test/tests/test_video_utils.py b/in_progress/streaming-test/tests/test_video_utils.py
new file mode 100644
index 000000000..803afe174
--- /dev/null
+++ b/in_progress/streaming-test/tests/test_video_utils.py
@@ -0,0 +1,159 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import numpy as np
+import cv2 # OpenCV is a direct dependency here
+import time # Import the time module
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from app.video_utils import VideoMonitor
+from app.config import VIDEO_FPS
+
+class TestVideoUtils(unittest.TestCase):
+
+    def create_dummy_frame(self, width=640, height=480, channels=3, color=(0,0,0)):
+        frame = np.full((height, width, channels), color, dtype=np.uint8)
+        return frame
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_start_capture_success(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+
+        monitor = VideoMonitor(camera_index=0)
+        self.assertTrue(monitor.start_capture())
+        MockVideoCapture.assert_called_once_with(0)
+        self.assertIsNotNone(monitor.cap)
+        self.assertTrue(monitor.cap.isOpened())
+        monitor.stop_capture() # Clean up
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_start_capture_failure(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = False # Simulate camera not opening
+
+        monitor = VideoMonitor(camera_index=0)
+        self.assertFalse(monitor.start_capture())
+        self.assertIsNone(monitor.cap)
+
+    def test_videomonitor_get_frame_no_capture(self):
+        monitor = VideoMonitor()
+        # No call to start_capture
+        self.assertIsNone(monitor.get_frame())
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_get_frame_success(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+        dummy_frame_data = self.create_dummy_frame()
+        mock_cap_instance.read.return_value = (True, dummy_frame_data)
+
+        monitor = VideoMonitor()
+        monitor.start_capture() # Sets up monitor.cap
+        frame = monitor.get_frame()
+
+        self.assertIsNotNone(frame)
+        np.testing.assert_array_equal(frame, dummy_frame_data)
+        mock_cap_instance.read.assert_called_once()
+        monitor.stop_capture()
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_get_frame_read_fail(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+        mock_cap_instance.read.return_value = (False, None) # Simulate read failure
+
+        monitor = VideoMonitor()
+        monitor.start_capture()
+        frame = monitor.get_frame()
+
+        self.assertIsNone(frame)
+        monitor.stop_capture()
+
+    def test_detect_significant_change_no_frame(self):
+        monitor = VideoMonitor()
+        changed, desc, _ = monitor.detect_significant_change(None)
+        self.assertFalse(changed)
+        self.assertEqual(desc, "No frame")
+
+    @patch('cv2.imencode', return_value=(True, np.array([1,2,3], dtype=np.uint8))) # Corrected mock target and return value
+    def test_detect_significant_change_with_motion(self, mock_imencode):
+        monitor = VideoMonitor()
+        # monitor.start_capture() # Initializes bg_subtractor - not needed if only testing detect_significant_change directly with a frame
+        # Let's ensure bg_subtractor is available for this test method if it's not through start_capture
+        monitor.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+        frame1 = self.create_dummy_frame(color=(10,10,10)) # Initial background
+        monitor.bg_subtractor.apply(frame1) # establish baseline
+
+        frame2 = self.create_dummy_frame(color=(10,10,10))
+        # Simulate a moving object by drawing a different color rectangle
+        cv2.rectangle(frame2, (100,100), (200,200), (200,200,200), -1)
+
+        changed, desc, frame_bytes = monitor.detect_significant_change(frame2, min_contour_area=100)
+
+        self.assertTrue(changed, f"Change should have been detected. Description: {desc}")
+        self.assertEqual(desc, "Significant motion detected in the scene.")
+        self.assertIsNotNone(frame_bytes)
+        mock_imencode.assert_called_once()
+        # monitor.stop_capture() # Only if start_capture was called
+
+    def test_detect_significant_change_no_motion(self):
+        monitor = VideoMonitor()
+        # monitor.start_capture() # Initializes bg_subtractor
+        monitor.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+        frame1 = self.create_dummy_frame(color=(10,10,10))
+        monitor.bg_subtractor.apply(frame1) # establish baseline
+
+        # Process same frame again or very similar one
+        frame2 = self.create_dummy_frame(color=(10,10,10))
+        # Make a tiny change that should be filtered out by morphology or area
+        cv2.rectangle(frame2, (0,0), (2,2), (20,20,20), -1)
+
+
+        changed, desc, _ = monitor.detect_significant_change(frame2, min_contour_area=500)
+
+        self.assertFalse(changed, f"No significant change should be detected. Description: {desc}")
+        self.assertEqual(desc, "No significant change detected.")
+        # monitor.stop_capture()
+
+    @patch('app.video_utils.VideoMonitor.get_frame')
+    @patch('app.video_utils.VideoMonitor.detect_significant_change')
+    def test_process_frame_for_changes_fps_limit(self, mock_detect_change, mock_get_frame):
+        monitor = VideoMonitor(fps_limit=10) # Limit to 10 FPS
+        monitor.last_processed_time = time.time() # Pretend we just processed a frame
+
+        # Call immediately, should be skipped due to FPS limit
+        changed, desc, _ = monitor.process_frame_for_changes()
+
+        self.assertFalse(changed)
+        self.assertEqual(desc, "FPS limit, frame skipped")
+        mock_get_frame.assert_not_called()
+        mock_detect_change.assert_not_called()
+
+    @patch('app.video_utils.VideoMonitor.get_frame')
+    @patch('app.video_utils.VideoMonitor.detect_significant_change')
+    def test_process_frame_for_changes_proceeds(self, mock_detect_change, mock_get_frame):
+        monitor = VideoMonitor(fps_limit=1)
+        monitor.last_processed_time = time.time() - 2 # Pretend last frame was 2s ago
+
+        dummy_frame = self.create_dummy_frame()
+        mock_get_frame.return_value = dummy_frame
+        mock_detect_change.return_value = (True, "Test change", b"frame_bytes")
+
+        changed, desc, frame_bytes_out = monitor.process_frame_for_changes()
+
+        self.assertTrue(changed)
+        self.assertEqual(desc, "Test change")
+        self.assertEqual(frame_bytes_out, b"frame_bytes")
+        mock_get_frame.assert_called_once()
+        mock_detect_change.assert_called_once_with(dummy_frame)
+
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test2/.gitignore b/in_progress/streaming-test2/.gitignore
new file mode 100644
index 000000000..f20068ff7
--- /dev/null
+++ b/in_progress/streaming-test2/.gitignore
@@ -0,0 +1,6 @@
+.venv/
+__pycache__/
+*.pyc
+.env
+instance/
+node_modules/
diff --git a/in_progress/streaming-test2/README.md b/in_progress/streaming-test2/README.md
new file mode 100644
index 000000000..c51b8f58e
--- /dev/null
+++ b/in_progress/streaming-test2/README.md
@@ -0,0 +1,116 @@
+# Streaming Test 2 - Multimodal Streaming Agents
+
+This project (`streaming-test2`) demonstrates a multimodal agent system using Python, FastAPI, and Google Agent Development Kit (ADK) for live bi-directional audio, and a separate camera monitoring agent using Gemini.
+
+## Features
+
+- __CameraAgent__: Monitors a live video feed, detects significant visual changes, sends observations to a Gemini model ("gemini-2.0-flash"), and logs interpretations to `/logs/camera_log.json`.
+- __AudioAgent__: Handles live bi-directional audio interaction using Google ADK and the "gemini-2.0-flash-live-preview-04-09" model. Logs transcriptions to `/logs/audio_log.json`.
+- __Agent Communication__: The AudioAgent reads from the CameraAgent's log to gain contextual awareness for its responses.
+- __FastAPI Backend__: Serves the application, handling SSE (Server-Sent Events) for audio streaming, and provides HTTP endpoints for client interaction.
+- __Web Interface__: A basic HTML/CSS/JS frontend to interact with the agents and view logs.
+
+## Prerequisites
+
+- Python 3.8 + (3.9/3.10/3.11 recommended)
+- `pip` for installing packages.
+- Access to a camera (for the CameraAgent).
+-  A microphone (for the AudioAgent).
+- A Google Cloud Project with Vertex AI enabled.
+
+
+## Setup Instructions
+
+1. *__Create a Virtual Environment (Recommended)__*
+   ```sh
+   python -m venv .venv
+   ```
+   Activate the virtual environment:
+   - On macOS/Linux:
+     ```sh
+     source .venv/bin/activate
+     ```
+   - On Windows:
+     ```sh
+     .venv\Scripts\activate
+     ```
+
+2. *__Install Dependencies__*
+   With your virtual environment active, navigate to the `streaming-test2` directory and run the following command to install the required packages:
+   ```sh
+   pip install -r requirements.txt
+   ```
+   (Ensure you are in the `streaming-test2` directory when running this command, as requirements.txt is located there.)
+
+3. *__Create the `.env` File__*
+   Create a file named `.env` within the `streaming-test2` directory.
+   Edit this `.env` file and add the following content, replacing the placeholders with your actual Google Cloud project details:
+   ```        GOOGLE_GENAI_USE_VERTEXAI=True
+        GOOGLE_CLOUD_PROJECT="analog-delight-459210-v0"
+        GOOGLE_CLOUD_LOCATION="us-central1"
+   ```
+   Note: The Python scripts in the `app/` directory are configured to load this particular `.env` file from the `streaming-test2` directory.
+
+4. *__Run the Application__*
+   Navigate into the `streaming-test2` directory in your terminal.
+   ```sh
+   cd path/to/streaming-test2
+   ```
+   Then, run the following command to start the FastAPI application using Uvicorn:
+   ```sh
+   python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
+   ```
+   - `app.main:app` tells Uvicorn to look for the `app` folder, the `main` module (main.py), and the `app` object within that module. This assumes you are running from the `streaming-test2` directory.
+   - `--reload` enables automatic reloading when code changes are detected.
+   - `--host 0.0.0.0` makes the server accessible on your network.
+   - `--port 8000` sets the port the server is running on.
+
+Once the server is running, open your web browser and navigate to `http://localhost:8000`.
+
+
+## Usage
+
+1. The UI will display statuses for the Camera Agent and the Audio Agent.
+2. The Camera Agent will automatically start monitoring.
+   - Its rager will appear in the 'Camera Log' section.
+3. To interact with the AudioAgent:
+   - Click the `Start Audio Interaction` button.
+   - Your server permission to use the microphone.
+   - Speak, or type a message in the input field and click `Send Text`.
+   - The AudioAgent's response (audio and text transcription) will appear.
+   - Click `Stop Audio Interaction` to end the audio session.
+- Audio transcriptions will appear in the 'Audio Log' section.
+
+## Project Structure
+
+```
+/streaming-test2
+    .env                                     # (You create this) Google Cloud config
+    README.md                                # This file
+    requirements.txt                         # Python dependencies
+    app/
+        __init__.py                          # Makes 'app' a Python package
+        audio_agent.py                       # Logic for the AudioAgent
+        camera_agent.py                      # Logic for the CameraAgent
+        config.py                            # Configuration (e.g., VIDEO_FPS)
+        main.py                              # FastAPI application
+
+        static/                              # Static files for the web UI
+            css/
+                style.css
+            js/
+                app.js
+                audio-player.js
+                audio-recorder.js
+                pcm-player-processor.js
+                pcm-recorder-processor.js
+            index.html
+        video_utils.py                       # Utilities for video processing
+    logs/
+        audio_log.json                       # Log of audio transcriptions
+        camera_log.json                      # Log of camera observations
+```
+
+## Notes
+- The JavaScript audio processor files (`audio-player.js`, `audio-recorder.js`, `pcm-player-processor.js`, `pcm-recorder-processor.js`) are adapted from Google ADK examples and are crucial for the audio functionality.
+-  The application is designed for development and may require further configuration and security hardening for production use.
diff --git a/in_progress/streaming-test2/app/__init__.py b/in_progress/streaming-test2/app/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test2/app/audio_agent.py b/in_progress/streaming-test2/app/audio_agent.py
new file mode 100644
index 000000000..f30ab1ca8
--- /dev/null
+++ b/in_progress/streaming-test2/app/audio_agent.py
@@ -0,0 +1,378 @@
+import os # For interacting with the OS
+import json # For JSON data handling
+import base64 # For encoding/decoding binary data for JSON
+import datetime # For timestamping log entries
+import warnings # For handling warnings
+import asyncio # For asynchronous programming, crucial for ADK streaming
+from pathlib import Path # For object-oriented file path handling
+from dotenv import load_dotenv # For loading environment variables from a “.env” file
+
+# Imports from the Google Agent Development Kit (ADK)
+from google.genai.types import Part, Content, Blob # Data structures for content in ADK
+from google.adk.runners import InMemoryRunner # Runner for executing ADK agents in memory
+from google.adk.agents import LiveRequestQueue, Agent # Core ADK Agent class and live request handling
+from google.adk.agents.run_config import RunConfig # Configuration for how an agent runs
+
+# --- Environment Variable Loading ---
+# Set the path to the .env file, expecting it in the `streaming-test2` directory.
+dotenv_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env')
+load_dotenv(dotenv_path=dotenv_path)
+
+# Warnings filtering - suppress specific UserWarnings, often from pydantic used by FastAPI
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+APP_NAME = "ADK_Streaming_Audio_Agent" # A name for this application/module, used by ADK
+
+class AudioAgent:
+    """
+    The AudioAgent handles live bi-directional audio streaming using the Google ADK.
+    It integrates with a FastAPI backend to communicate with a client (e.g., a web browser).
+    It manages ADK sessions, processes incoming and outgoing audio/text, logs
+    transcriptions, and potentially incorporates context from other agents.
+    """
+
+    def __init__(self, project_id: str, location: str, model_name: str = "gemini-2.0-flash-live-preview-04-09"):
+        """
+        Initializes the AudioAgent.
+
+        Args:
+            project_id (str): The Google Cloud Project ID.
+            location (str): The Google Cloud region for Vertex AI services.
+            model_name (str, optional): The name of the Gemini model to use for live audio.
+                                        Defaults to "gemini-2.0-flash-live-preview-04-09".
+        """
+        # Validate project ID and location, they are essential.
+        if not project_id:
+            raise ValueError("CAUDIO_AGENT: Google Cloud Project ID is required.")
+        if not location:
+            raise ValueError("CAUDIO_AGENT: Google Cloud Location is required.")
+
+        self.project_id = project_id
+        self.location = location
+
+        # Ensure that the application is configured to use Vertex AI, as the ADK for large
+        # language models typically relies on it.
+        if os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "False").lower() != "true":
+            raise EnvironmentError("AUDIO_AGENT: GOOGLE_GENAI_USE_VERTEXAI must be set to True in .env")
+
+        # Define the `self.root_agent_adk`. This is the core ADK Agent object.
+        #   - `name`: A descriptive name for the agent.
+        #   - `model`: The specific Gemini model that supports live audio streaming.
+        #   - `description`: A brief description of the agent's purpose.
+        #   - `instruction`: The system-prompt or instructions that guide the agent's behavior.
+        #     It includes a placeholder to be aware of visual context, which will
+        #     be populated from the camera agent's logs.
+        self.root_agent_adk = Agent(
+           name="streaming_audio_root_agent",
+           model=model_name,
+           description="Live audio conversational agent.",
+           instruction="You are a helpful voice assistant. Respond to the user's speech. Be concise and natural. When appropriate, consider the following visual context from your surroundings.",
+        )
+        print(f"AUDIO_AGENT: Initialized with ADK root agent using model: {model_name}")
+
+        # Define paths for log files.
+        # Audio log file stores transcriptions of user and agent speech.
+        self.audio_log_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'logs', 'audio_log.json')
+        # Camera log file is used to read context from the CameraAgent.
+        self.camera_log_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'logs', 'camera_log.json')
+
+        # Ensure the 'logs' directory exists.
+        os.makedirs(os.path.dirname(self.audio_log_file), exist_ok=True)
+        # Initialize the audio log file with an empty JSON list.
+        with open(self.audio_log_file, 'w') as f:
+            json.dump([], f)
+        print(f"AUDIO_AGENT: Logging audio transcriptions to: {self.audio_log_file}")
+
+        # Ensure the camera log file exists for reading, even if it's just initialized as empty.
+        if not os.path.exists(self.camera_log_file):
+            with open(self.camera_log_file, 'w') as f:
+                json.dump([],f)
+            print(f"AUDIO_AGENT: Initialized empty camera log file at: {self.camera_log_file}")
+
+        # `active_adk_sessions`: A dictionary to store active ADK session data per user.
+        # Each user's session will have its own ADK runner, session, queue, and event stream.
+        self.active_adk_sessions = {}
+
+        # `camera_context`: Stores the latest information received from the CameraAgent.
+        # This is used to prime the AudioAgent's conversations.
+        self.camera_context = "No camera updates yet."
+
+    def _log_transcription(self,speaker: str, text: str):
+        """
+        Internal helper method to log audio transcriptions to a JSON file.
+        Each log entry includes a timestamp, the speaker ('user' or 'agent'), and the transcribed text.
+
+        Args:
+            speaker (str): The source of the speech (either "user" or "agent").
+            text (str): The transcribed text of the speech.
+        """
+        timestamp = datetime.datetime.now().isoformat()
+        log_entry = {"timestamp": timestamp, "speaker": speaker, "text": text}
+        try:
+            current_logs = []
+            # CRobustly read, append, and write the JSON log file.
+            if os.path.exists(self.audio_log_file) and os.path.getsize(self.audio_log_file) > 0:
+                with open(self.audio_log_file, 'r') as f:
+                    try:
+                        current_logs = json.load(f)
+                        if not isinstance(current_logs, list): current_logs = []
+                    except json.JSONDecodeError: current_logs = [] # If file is corrupt, start fresh.
+            current_logs.append(log_entry)
+            with open(self.audio_log_file, 'w') as f:
+                json.dump(current_logs, f, indent=4)
+        except IOError as e: print(f'AUDIO_AGENT: Error writing to audio log: {e}')
+        except Exception as e: print(f"AUDIO_AGENT: Unexpected error during audio logging: {e}")
+
+    def update_camera_context(self, context: str):
+        """
+        Allows for external updating of the camera context.
+        This is used by the main FastAPI application to pass information
+        from the CameraAgent to this AudioAgent.
+        """
+        self.camera_context = context
+        print(f"AUDIO_AGENT: Camera context manually updated: {context[:100]}...")
+
+    def refresh_camera_context_from_log(self, max_entries=3):
+        """
+        Reads the `camera_log.json`, poentries should contain
+        a comment from the LLM ("comment_by_llm").
+        If relevant comments are found, `the current camera context to be used in future
+        audio prompts.
+        """
+        try:
+            # Check if camera log file exists and is not empty.
+            if not os.path.exists(self.camera_log_file) or os.path.getsize(self.camera_log_file) == 0:
+                # print(f'AUDIO_AGENT: Camera log missing or empty at {self.camera_log_file}.') # Can be noisy
+                return
+            with open(self.camera_log_file, 'r') as f:
+                try:
+                    all_camera_logs = json.load(f)
+                    if not isinstance(all_camera_logs, list): all_camera_logs = []
+                except json.JSONDecodeError:
+                    # print(f'AUDIO_AGENT: Could not decode camera log: {self.camera_log_file}.') # Can be noisy
+                    return
+            if not all_camera_logs: return
+
+            # Get the last `max_entries` from the log.
+            recent_logs = all_camera_logs[-max_entries:]
+            # Extract LLM comments. Get empty string as default if not found. Strip whitespace.
+            relevant_comments = [log.get("comment_by_llm", "").strip() for log in recent_logs if isinstance(log.get("comment_by_llm"), str)]
+            # Filter out any empty comments that resulted after stripping.
+            relevant_comments = [comment for comment in relevant_comments if comment]
+
+            if relevant_comments:
+                new_context = " ".join(relevant_comments)
+                if new_context != self.camera_context:
+                    self.camera_context = new_context
+                    print(f"AUDIO_AGENT: Camera context refreshed: {self.camera_context[:200]}...")
+        except IOError as e: print(f"AUDIO_AGENT: Error reading camera log: {e}")
+        except Exception as e: print(f"AUDIO_AGENT: Unexpected error refreshing camera context: {e}")
+
+    async def start_adk_session(self, user_id: str, is_audio: bool = True):
+        """
+        Starts a new ADK session for a given user ID.
+        If a requested session for the user.
+        This also refreshes the `camera_context` from the log file before starting.
+        Creates and stores an ADK runner, session, live request queue, and live events stream.
+        Injects the current camera context as an initial message into the ADK agent.
+        """
+        if user_id in self.active_adk_sessions:
+            print(f'AUDIO_AGENT: Session for user {user_id} already exists. Closing old one.')
+            await self.stop_adk_session(user_id)
+
+        self.refresh_camera_context_from_log()
+
+        print(f"AUDIO_AGENT: Starting ADK session for {user_id}, audio: {is_audio}")
+        try:
+            # Create an InMemoryRunner. The `app_name` should be unique per session or user if
+            # multiple runners are used, but for multie users served by one AudioAgent,
+            # using user_id here is a good way to distinguish ADK's internal app needs.
+            runner = InMemoryRunner(app_name=f'{APP_NAME}_{user_id}', agent=self.root_agent_adk)
+
+            # Create an ADK session for the user.
+            session = await runner.session_service.create_session(
+                app_name=f'{APP_NAME}_{user_id}', # Matches the runner's app_name
+                user_id=user_id
+            )
+
+            # Determine the response modality (audio or text).
+            modality = "AUDIO" if is_audio else "TEXT"
+            run_config = RunConfig(response_modalities=[modality])
+
+            # Create a LiveRequestQueue for sending data to the agent.
+            live_request_queue = LiveRequestQueue()
+
+            # Construct an initial message to send to the agent to prime it with
+            # the current camera context. This is sent as the first `user` turn.
+            # The agent's instruction should guide it not to directly reply to this system message.
+            initial_msg = (f"System Info: Visual context: '{self.camera_context}'. Please wait for user.")
+            initial_content = Content(role="user", parts=[Part.from_text(initial_msg)])
+
+            # Start the live ADK session, passing the initial context message.
+            live_events = runner.run_live(
+                session=session,
+                live_request_queue=live_request_queue,
+                run_config=run_config,
+                request=initial_content  # Sends the priming message at session start.
+            )
+
+            # Store the active session components.
+            self.active_adk_sessions[user_id] = {
+                "runner": runner,
+                "session": session,
+                "live_request_queue": live_request_queue,
+                "live_events": live_events,
+                "is_audio": is_audio
+            }
+            print(f"AUDIO_AGENT: ADK session started for {user_id} with context: {self.camera_context[:100]}...")
+            return live_events, live_request_queue
+        except Exception as e:
+            print(f'AUDIO_AGENT: Error starting ADK session for {user_id}: {e}')
+            if user_id in self.active_adk_sessions: del self.active_adk_sessions[user_id]
+            raise
+
+    async def stop_adk_session(self, user_id: str):
+        """
+        Stops and cleans up an active ADK session for the specified user ID.
+        """
+        if user_id in self.active_adk_sessions:
+            print(f"AUDIO_AGENT: Stopping ADK session for {user_id}...")
+            # Get the live request queue to close it. Closing the queue is the
+            # primary means of signaling the background ADK process to terminate.
+            self.active_adk_sessions[user_id]["live_request_queue"].close()
+            # Remove the session data from the active sessions dictionary.
+            del self.active_adk_sessions[user_id]
+            print(f"AUDIO_AGENT: ADK session stopped for {user_id}.")
+        # Else clause isn't very necessary here, as it's not an error if a
+        # it would just make the logs a bit noisier.
+        # else: print(f"AUDIO_AGENT: No active ADK session for user {user_id} to stop.") # Can be noisy
+
+    async def agent_to_client_sse_handler(self, user_id: str):
+        """
+        A FastAPI Server-Sent Events (SSE) handler that streams events from the ADK agent
+        to the client browser. It acts as an asynchronous generator.
+        This method is called by the FastAPI's SSE endpoint.
+        It handles different types of events from ADK:
+            - Turn completion or interruption.
+            - Audio data (e.g., PCM).
+            - Text data (partial or full transcriptions/responses).
+        It logs agent's speech transcriptions.
+
+        Args:
+            user_id (str): The unique identifier of the user who se session is being handled.
+        """
+        if user_id not in self.active_adk_sessions:
+            print(f"AUDIO_AGENT SSE: Session not found for user {user_id}.")
+            yield f"data: {json.dumps({'error': 'Session not found.'})}\n\n"; return
+
+        # Retrieve the live events stream and audio mode flag for the user's session.
+        live_events = self.active_adk_sessions[user_id]["live_events"]
+        is_audio = self.active_adk_sessions[user_id]["is_audio"]
+
+        # `text_turn` accumulates all partial text responses for a single agent turn.
+        text_turn = ""
+
+        print(f"AUDIO_AGENT SSE: Starting event stream for user {user_id}.")
+        async for event in live_events:
+            # Handle events signaling the end of an agent turn.
+            if event.turn_complete or event.interrupted:
+                message = {"turn_complete": event.turn_complete,"interrupted": event.interrupted}
+                yield f"data: {json.dumps(message)}\n\n"
+                # If there was accumulated text, log it.
+                if text_turn and "System Info:" not in text_turn:
+                    self._log_transcription("agent", text_turn)
+                text_turn = "" # Reset for the next turn.
+                continue
+
+            # Extract the first part of the content, if it exists.
+            part = event.content and event.content.parts and event.content.parts[0]
+            if not part: continue
+
+            # Handle audio data if the session is in audio mode.
+            if is_audio and part.inline_data and part.inline_data.mime_type.startswith("audio/"):
+                if part.inline_data.data: # Ensure audio data exists
+                    yield f"data: {json.dumps({'mime_type': part.inline_data.mime_type, 'data': base64.b64encode(part.inline_data.data).decode('ascii')})}\n\n"
+
+            # Handle text data.
+            if part.text:
+                # Filter out the echo of the initial system information message.
+                if "System Info:" in part.text and event.content.role == "model":
+                    continue
+
+                yield f"data: {json.dumps({'mime_type': 'text/plain', 'data': part.text})}\n\n"
+                # Accumulate text for logging at the end of the turn.
+                text_turn += part.text
+
+        # print(f"AUDIO_AGENT SSE: Stream ended for {user_id}.") # Noisy for normal client disconnect
+
+    async def client_to_agent_handler(self, user_id: str, client_msg: dict):
+        """
+        Handles messages sent from the client to the ADK agent.
+        This is typically called from a FastAPI POST endpoint.
+        It parses the client's message (audio or text), logs the user's transcription,
+        and sends the data to the ADK agent via the live request queue.
+
+        Args:
+            user_id (str): The user's unique identifier.
+            client_msg (dict): A dictionary containing 'mime_type' and 'data' from the client.
+
+        Returns:
+            dict: A status dictionary, or an error dictionary.
+        """
+        if user_id not in self.active_adk_sessions:
+            print(f"AUDIO_AGENT MSG: Session not found for user {user_id} in client_to_agent_handler.")
+            return {"error": "Session not found."}
+
+        # The camera context is now primarily set at the start of the ADK session.
+        # If it needs to be updated more frequently during a session, that
+        # would require a different mechanism for injecting into an ongoing ADK run.
+        # self.refresh_camera_context_from_log() # Context is now primarily set at session start
+
+        queue = self.active_adk_sessions[user_id]["live_request_queue"]
+        is_audio = self.active_adk_sessions[user_id]["is_audio"]
+        mime, data = client_msg.get("mime_type"), client_msg.get("data")
+
+        if not mime or data is None: return {"error": "Invalid message."}
+
+        log_text = ""
+        if mime == "text/plain":
+            log_text = data
+            # Send the text data directly. Context is primarily injected at session start.
+            queue.send_content(Content(role="user", parts=[Part.from_text(data)]))
+            print(f"AUDIO_AGENT MSG [CLIENT->AGENT] ({user_id}): Text: '{data[:50]}...'")
+        elif mime.startswith('audio/') and is_audio:
+            try:
+                decoded = base64.b64decode(data)
+                queue.send_realtime(Blob(data=decoded, mime_type=mime))
+                print(f"AUDIO_AGENT MSG [CLIENT->AGENT] ({user_id}): {mime} {len(decoded)} bytes.")
+            except Exception as e: return {"error": f"Audio error: {e}"}
+        else: return {"error": "Unsupported mime/session."}
+
+        if log_text: # Log user's text.
+            self._log_transcription("user", log_text)
+
+        return {"status": "sent"}
+
+# This block allows for conceptual testing of the AudioAgent when the file is run directly.
+if __name__ == '__main__':
+    print("CAUDIO_AGENT: Conceptual test.")
+    project, loc = os.getenv('GOOGLE_CLOUD_PROJECT'), os.getenv('GOOGLE_CLOUD_LOCATION')
+    if os.getenv('GOOGLE_GENAI_USE_VERTEXAI','F').lower()=='t' and project and loc:
+        agent = AudioAgent(project, loc)
+        # agent.camera_log_file # This line seems incomplete, was it meant to be used?
+        # Create a dummy camera log for testing context refresh
+        with open(agent.camera_log_file, 'w') as f: json.dump([{"comment_by_llm": "Red car passed."},{"comment_by_llm": "Dog barked."}], f)
+        print(f"Initial ctx: '{agent.camera_context}'.")
+        agent.refresh_camera_context_from_log()
+        print(f"Refreshed ctx: '{agent.camera_context}'") # Should be "Red car... Dog barked."
+        async def main_test():
+            uid = "test_uid_004"
+            try:
+                await agent.start_adk_session(uid, is_audio=False) # Test with text session
+                await agent.client_to_agent_handler(uid, {"mime_type": "text/plain", "data": "What's new?"})
+                # Add more test interactions here if needed
+            finally: await agent.stop_adk_session(uid)
+        asyncio.run(main_test())
+        if os.path.exists(agent.camera_log_file): os.remove(agent.camera_log_file) # Clean up dummy log
+        print("AUDIO_AGENT: Test done.")
+    else: print("CAUDIO_AGENT: Skip test, Vertex AI env not set.")
diff --git a/in_progress/streaming-test2/app/camera_agent.py b/in_progress/streaming-test2/app/camera_agent.py
new file mode 100644
index 000000000..43d444439
--- /dev/null
+++ b/in_progress/streaming-test2/app/camera_agent.py
@@ -0,0 +1,424 @@
+# Import necessary standard Python libraries
+import os  # For interacting with the operating system (e.g., file paths)
+import time  # For time-related functions (e.g., sleep, timestamps)
+import json  # For working with JSON data (saving and loading logs)
+import datetime  # For getting current date and time
+import threading  # For running tasks in separate threads (non-blocking operations)
+
+# Import libraries for Google Cloud Vertex AI
+import vertexai  # Main Vertex AI SDK
+import vertexai.generative_models as genai_models  # For using generative AI models and creating content parts
+from vertexai.generative_models import GenerativeModel, Part #shorter alias
+
+# Import custom utility for video monitoring
+from .video_utils import VideoMonitor  # Assumes video_utils.py is in the same 'app' directory
+
+# Import library for loading environment variables from a .env file
+from dotenv import load_dotenv
+
+# --- Environment Variable Loading ---
+# Construct the absolute path to the .env file, assuming it's in the 'streaming-test2/' directory (parent of 'app/')
+# __file__ is the path to the current script (camera_agent.py)
+# os.path.dirname(__file__) gives the directory of the current script ('app/')
+# os.path.dirname(os.path.dirname(__file__)) gives the parent directory ('streaming-test2/')
+dotenv_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env')
+# Load the .env file, making its variables available as environment variables
+load_dotenv(dotenv_path=dotenv_path)
+
+class CameraAgent:
+    """
+    The CameraAgent is responsible for monitoring a video feed from a camera,
+    detecting significant visual changes, sending these changes (as images)
+    to a generative AI model for interpretation, and logging the outcomes.
+    It runs its monitoring process in a separate background thread.
+    """
+
+    def __init__(self,
+                 project_id: str,
+                 location: str,
+                 model_name: str = "gemini-2.0-flash",
+                 camera_index: int = 0,
+                 fps_limit: int = 1):
+        """
+        Initializes the CameraAgent.
+
+        Args:
+            project_id (str): The Google Cloud Project ID.
+            location (str): The Google Cloud region for Vertex AI services.
+            model_name (str, optional): The name of the Gemini model to use for image interpretation.
+                                        Defaults to "gemini-2.0-flash".
+            camera_index (int, optional): The index of the camera to use (e.g., 0 for the default system camera).
+                                          Defaults to 0.
+            fps_limit (int, optional): The maximum frames per second to process from the camera.
+                                       Defaults to 1.
+        """
+        # --- Validate Essential Configuration ---
+        if not project_id:
+            raise ValueError("CAMERA_AGENT: Google Cloud Project ID is required for CameraAgent.")
+        if not location:
+            raise ValueError("CAMERA_AGENT: Google Cloud Location is required for CameraAgent.")
+
+        # --- Store Configuration ---
+        self.project_id = project_id
+        self.location = location
+        self.model_name = model_name
+
+        # Define the path for the camera log file
+        # It will be stored in 'streaming-test2/logs/camera_log.json'
+        self.log_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),  # 'streaming-test2/'
+            'logs',  # 'logs/'
+            'camera_log.json'  # 'camera_log.json'
+        )
+
+        # --- Initialize Logging ---
+        # Ensure the 'logs' directory exists; create it if it doesn't.
+        os.makedirs(os.path.dirname(self.log_file), exist_ok=True)
+        # Initialize the log file with an empty JSON list '[]'.
+        # This ensures the file is always a valid JSON, even if empty.
+        # 'w' mode overwrites the file if it exists, effectively clearing old logs on startup.
+        with open(self.log_file, 'w') as f:
+            json.dump([], f)
+
+        # --- Initialize Vertex AI ---
+        # This print statement helps confirm that the agent is starting up and using the correct project/location.
+        print(f"CAMERA_AGENT: Initializing Vertex AI with project: {self.project_id}, location: {self.location}")
+        # Initialize the Vertex AI SDK with the provided project and location.
+        # This needs to be done before using any Vertex AI services.
+        vertexai.init(project=self.project_id, location=self.location)
+
+        print(f"CAMERA_AGENT: Loading generative model: {self.model_name}")
+        # Create an instance of the GenerativeModel class using the specified model name.
+        self.model = GenerativeModel(self.model_name)
+        # `self.chat` will store the chat session object with the model.
+        # It's initialized to None and will be created when the first message needs to be sent.
+        self.chat = None
+
+        # --- Initialize Video Monitoring ---
+        # Create an instance of VideoMonitor from our video_utils.py.
+        # This object will handle the actual camera interaction and frame processing.
+        self.video_monitor = VideoMonitor(camera_index=camera_index, fps_limit=fps_limit)
+
+        # --- Threading Control ---
+        # `self.running` is a boolean flag that controls the main monitoring loop in the background thread.
+        # Setting it to False will signal the thread to stop.
+        self.running = False
+        # `self.thread` will hold the Thread object once it's created.
+        self.thread = None
+
+        print(f"CAMERA_AGENT: Initialized. Logging to: {self.log_file}")
+        print(f"CAMERA_AGENT: Video monitor configured for camera index {camera_index}, FPS limit {fps_limit}.")
+
+    def _start_chat_session(self):
+        """
+        Internal helper method to initialize or re-initialize a chat session with the Vertex AI model.
+        This is called if a chat session is not active when needed.
+        Achat session allows for conversational context to be maintained with the model.
+        """
+        # If a chat session doesn't exist or has been closed (e.g., due to an error)
+        if not self.chat:
+            print("CAMERA_AGENT: Starting new chat session with Vertex AI model...")
+            try:
+                # Start a new chat with the loaded model.
+                # `response_validation=False` can sometimes be useful for models that might return non-standard responses,
+                # though for typical text/image inputs, it's often not strictly necessary.
+                self.chat = self.model.start_chat(response_validation=False)
+                print("CAMERA_AGENT: Chat session started successfully.")
+            except Exception as e:
+                # If starting the chat session fails, print an error and ensure `self.chat` remains None.
+                print(f"CAMERA_AGENT: Critical error starting chat session: {e}")
+                self.chat = None
+                # Re-raise the exception because failing to start a chat session is a critical issue for this agent.
+                raise
+
+    def _log_change(self, description: str, model_comment: str):
+        """
+        Internal helper method to log detected changes and the AI model's comments to the JSON log file.
+        Each log entry is a JSON object containing a timestamp, type of event, the CV's description,
+        and the language model's comment.
+
+        Args:
+            description (str): The description of the change detected by the computer vision algorithm.
+            model_comment (str): The interpretation or comment provided by the AI model.
+        """
+        # Get the current time in ISO 8601 format (e.g., "2023-10-27T10:30:00.123456")
+        timestamp = datetime.datetime.now().isoformat()
+        # Prepare the data to be logged as a Python dictionary.
+        log_entry = {
+            "timestamp": timestamp,
+            "type": "video_change",  # Helps categorize log entries if other types are added later.
+            "description_by_cv": description,  # Description from the local CV processing.
+            "comment_by_llm": model_comment      # Comment generated by the language model.
+        }
+
+        try:
+            # --- Read existing logs, append new entry, and write back ---
+            # This read-modify-write approach is simple. For very high-frequency logging,
+            #  more advanced techniques like a dedicated logging queue or database might be better
+            #  to avoid I/O bottlenecks or race conditions. For this project's expected rate of
+            #  significant visual changes, this method is generally acceptable.
+
+            current_logs = []
+            # Check if the log file already exists and is not empty.
+            if os.path.exists(self.log_file) and os.path.getsize(self.log_file) > 0:
+                # Open the log file in read mode ('r').
+                with open(self.log_file, 'r') as f:
+                    try:
+                        # Attempt to load the existing log entries from the JSON file.
+                        current_logs = json.load(f)
+                        # Ensure the loaded content is a list, as expected.
+                        # If the file was corrupted and contained something else (e.g., a single dictionary),
+                        #  reset to an empty list to prevent errors when appending.
+                        if not isinstance(current_logs, list):
+                            print(f"CAMERA_AGENT: Warning - camera_log.json content was not a list. Resetting log.")
+                            current_logs = []
+                    except json.JSONDecodeError:
+                        # If the JSON is invalid (e.g., file corrupted or improperly formatted),
+                        #  print a warning and start with an empty list. This prevents the agent from crashing.
+                        print(f"CAMERA_AGENT: Warning - camera_log.json was corrupted. Starting with a new log list.")
+                        current_logs = []
+
+            # Add the new log entry to the list of logs (either newly created or loaded).
+            current_logs.append(log_entry)
+
+            # Open the log file in write mode ('w'). This will overwrite the existing file.
+            with open(self.log_file, 'w') as f:
+                # Write the entire updated list of logs back to the file.
+                # `indent=4` formats the JSON output to be human-readable with pretty printing.
+                json.dump(current_logs, f, indent=4)
+
+            # Print a confirmation that the change was logged, showing the first 50 characters of the comment.
+            print(f"CAMERA_AGENT: Logged change. LLM comment: '{model_comment[:50]}...'")
+        except IOError as e:
+            # Handle potential errors during file input/output operations (e.g., disk full, permissions).
+            print(f"CAMERA_AGENT: Error writing to log file {self.log_file}: {e}")
+        except Exception as e:
+            # Catch any other unexpected errors during the logging process.
+            print(f'CAMERA_AGENT: Unexpected error during logging: {e}')
+
+    def _monitor_loop(self):
+        """
+        The main loop for monitoring the video feed. This method is intended to be run in a separate thread.
+        It continuously captures frames using `VideoMonitor`, processes them for significant changes,
+        sends relevant frames to the AI model for interpretation, and logs the results.
+        The loop continues as long as the `self.running` flag is True.
+        """
+        print("CAMERA_AGENT: Video monitoring loop started.")
+
+        # Attempt to start video capture using the VideoMonitor instance.
+        # This initializes the camera.
+        if not self.video_monitor.start_capture():
+            # If video capture cannot be started (e.g., camera not found, permissions error),
+            #  print a critical error message and exit the loop.
+            print("CAMERA_AGENT: CRITICAL - Failed to start video capture. Monitoring loop cannot run.")
+            self.running = False  # Set running flag to False to ensure the loop doesn't try to run.
+            return  # Exit the method.
+
+        # This is the main operational loop of the CameraAgent.
+        while self.running:
+            try:
+                # `process_frame_for_changes` is a method in `VideoMonitor` (from `video_utils.py`).
+                # It handles capturing a frame, comparing it to previous frames (as or a background model)
+                #  to detect significant changes, and applying any FPS limits.
+                # - `changed` (bool): True if a significant change was detected, False otherwise.
+                # - `description` (str): A textual description of the change from the CV algorithm (e.g., "Motion detected in region X").
+                # - `frame_bytes` (bytes | None): The raw image data (e.g., JPEG encoded) if a change was detected and a frame is available, otherwise None.
+                changed, description, frame_bytes = self.video_monitor.process_frame_for_changes()
+
+                # If a change was detected AND animage data (frame_bytes) is available:
+                if changed and frame_bytes:
+                    print(f"CAMERA_AGENT: Video change detected by CV: {description}. Preparing image for model.")
+
+                    # Ensure an active chat session with the AI model exists.
+                    # If not, try to start one.
+                    if not self.chat:
+                        try:
+                            self._start_chat_session()
+                        except Exception as e:
+                            # If starting the chat session fails, log the error and skip processing this change.
+                            # This prevents the agent from getting stuck if the model is temporarily unavailable.
+                            print(f"CAMERA_AGENT: Could not start chat session for processing change: {e}. Skipping this event.")
+                            time.sleep(5)  # Wait for a few seconds before trying again in the next loop iteration.
+                            continue  # Skip the rest of this iteration and go to the next.
+
+                    # Prepare the image data for the AI model.
+                    # The Gemini model expects multimodal inputs (like images) to be wrapped in a `Part` object.
+                    image_part = Part.from_data(frame_bytes, mime_type="image/jpeg")  # Assuming JPEG format from VideoMonitor
+
+                    # Construct a detailed and specific prompt for the AI model.
+                    # A good prompt helps the model understand the context and provide relevant interpretations.
+                    prompt_text = (
+                        "You are an AI assistant observing a live video feed from a security camera. "
+                        "A computer vision algorithm has detected a significant visual change in the scene. "
+                        f"The algorithm's initial description of this change is: '{description}'. "
+                        "Please analyze the provided image carefully and give a concise, human-readable description "
+                        "of what you observe in the image that is relevant to this detected change. "
+                        "Focus on new objects, significant movements, changes in object states, or anything unusual. "
+                        "Avoid generic statements like 'I see an image.' Be specific."
+                    )
+
+                    print(f"CAMERA_AGENT: Sending video change event and image to model. Prompt: '{prompt_text[:100]}...'")
+
+                    try:
+                        # Send the prompt (text) and the image part to the AI model via the active chat session.
+                        # The model will process both and generate a response.
+                        # For this agent, we are expecting a dsingle, non-streamed response.
+                        response = self.chat.send_message([prompt_text, image_part])
+
+                        model_comment = "" # Initialize to an empty string.
+                        # Safely extract the text response from the model.
+                        # The response object can be complex; we need to navigate to the actual text part.
+                        # `response.candidates[0]` usually contains the primary response.
+                        # `content.parts[0]` usually contains the main content block.
+                        # `.text` gives the textual content.
+                        if response and response.candidates and response.candidates[0].content and response.candidates[0].content.parts:
+                            model_comment = response.candidates[0].content.parts[0].text or ""  # Use .text, default to "" if None or empty
+
+                        if model_comment.strip():  # Check if the comment is not empty or just whitespace.
+                            print(f"CAMERA_AGENT: Model provided comment: '{model_comment}'")
+                            # Log the original CV description and the model's interpretation.
+                            self._log_change(description, model_comment)
+                        else:
+                            print("CAMERA_AGENT: Model did not provide a text comment for the video change.")
+                            # Optionally, log that no comment was provided if that's important information.
+                            # self._log_change(description, "LLM provided no comment or an empty comment.")
+
+                    except Exception as e:
+                        # Handle errors that might occur during communication with the AI model (e.g., network issues, API errors).
+                        print(f"CAMERA_AGENT: Error sending message to model or processing its response: {e}")
+                        # It's often a good practice to reset the chat session if an error occurs,
+                        #  as the session state might have become invalid.
+                        self.chat = None
+                        print("CAMERA_AGENT: Chat session reset due to error.")
+                        time.sleep(2)  # Brief pause bnext loop iteration to prevent rapid error cycling.
+
+                elif changed:
+                    # A change was detected by the CV algorithm, but `frame_bytes` is None (e.g., an error occurred during frame encoding).
+                    # We can't send anything to the model without the image.
+                    print(f"CAMERA_AGENT: Video change detected ('{description}'), but frame data is unavailable. Cannot send to model.")
+                    # Optionally, log this situation.
+                    # self._log_change(description, "CV detected change, but frame data was not available for LLM.")
+
+                # --- Loop Pacing ---
+                # The `video_monitor.process_frame_for_changes()` method in `video_utils.py`
+                #  should already incorporate logic (like `time.sleep`) to respect the `fps_limit`.
+                #  However, adding a very small sleep here ensures that this `_monitor_loop`
+                #  yields execution control, preventing it from becoming a "tight loop" that consumes
+                #   100%CPU if `process_frame_for_changes` returns very quickly (e.g., if fps_limit is high or None).
+                time.sleep(0.1)  # 100ms. Adjust as needed based on system performance and desired responsiveness.
+
+            except Exception as e:
+                # Catch any other unexpected errors that might occur within the main monitoring loop.
+                print(f'CAMERA_AGENT: Unexpected error in monitor loop: {e}')
+                # Depending on the severity or type of error, one might implement more sophisticated error handling,
+                #  such as an exponential backoff for retries, or stopping the agent if errors persist.
+                time.sleep(5) # Wait a bit to avoid overwhelming logs or services.
+
+        # --- Loop End & Cleanup ---
+        # This section is reached when `self.running` becomes `False`.
+        # It's important to clean up sresources, especially the camera.
+        if self.video_monitor.cap:  # Check if the video capture object exists and might be open.
+            self.video_monitor.stop_capture()  # Call the method in VideoMonitor to release the camera.
+        print("CAMERA_AGENT: Video monitoring loop stopped.")
+
+    def start(self):
+        """
+        Starts the camera agent's monitoring process.
+        It creates and starts a new background thread that runs the `_monitor_loop` method.
+        If the agent is already running, this method prints a message and does nothing more.
+        """
+        if not self.running:
+            self.running = True  # Set the flag to True to allow the `_monitor_loop` to execute.
+            # Create a new Thread object.
+            # - `target=self._monitor_loop`: Specifies the function to be executed by the thread.
+            # - `daemon=True`: Makes the thread a daemon thread. Daemon threads automatically exit
+            #   when the main program (the one that started them) exits. This is useful for background tasks.
+            self.thread = threading.Thread(target=self._monitor_loop, daemon=True)
+            self.thread.start()  # Start the execution of the thread.
+            print("CAMERA_AGENT: Agent started monitoring in a background thread.")
+        else:
+            print("CAMERA_AGENT: Agent is already running.")
+
+    def stop(self):
+        """
+        Stops the camera agent's monitoring process.
+        It sets the `self.running` flag to False, which signals the `_monitor_loop` in the
+        background thread to terminate its current iteration and exit.
+        It then waits for the thread to finish using `thread.join()`.
+        """
+        if self.running:
+            print("CAMERA_AGENT: Attempting to stop agent...")
+            self.running = False  # Signal the `_monitor_loop` to stop.
+
+            if self.thread and self.thread.is_alive():
+                # `self.thread.join(timeout=10)` will wait for the background thread to complete.
+                # The timeout (e.g., 10 seconds) is a safeguard in case the thread gets stuck
+                #  and doesn't terminate cleanly.
+                self.thread.join(timeout=10)
+                if self.thread.is_alive():
+                    # If the thread is still alive after the timeout, it indicates a problem.
+                    print("CAMERA_AGENT: Warning - background thread did not stop in the allocated time (10 seconds).")
+                else:
+                    print("CAMERA_AGENT: Background thread stopped successfully.")
+            else:
+                # This case handles if the thread was not active or already stopped.
+                print("CAMERA_AGENT: Background thread was not active or already stopped.")
+
+            self.thread = None # Clear the reference to the thread object.
+            print("CAMERA_AGENT: Agent stopped.")
+        else:
+            print("CAMERA_AGENT: Agent is not currently running.")
+
+# --- Main execution block for direct testing (optional) ---
+# This `if __name__ == '__main__':` block allows the script to be run directly
+# (e.g., by executing `python camera_agent.py` from the terminal).
+# It's a common Python idiom for providing test code or a simple command-line interface for a module.
+if __name__ == '__main__':
+    print("CAMERA_AGENT: Running camera_agent.py for direct testing...")
+
+    # Load required environment variables (PROJECT_ID, LOCATION) from the .env file.
+    # `load_dotenv()` should have been called at the top of the script.
+    project_id_env = os.getenv('GOOGLE_CLOUD_PROJECT')
+    location_env = os.getenv('GOOGLE_CLOUD_LOCATION')
+    # Check if Vertex AI is configured to be used.
+    use_vertex_env = os.getenv('GOOGLE_GENAI_USE_VERTEXAI', 'False').lower() == 'true'
+
+    # Proceed only if all necessary environment variables are set.
+    if use_vertex_env and project_id_env and location_env:
+        print(f"CAMERA_AGENT MAIN: Initializing agent with project='{project_id_env}', location='{location_env}', model='gemini-2.0-flash'")
+        # Create an instance of the CameraAgent for testing purposes.
+        test_agent = CameraAgent(
+            project_id=project_id_env,
+            location=location_env,
+            model_name="gemini-2.0-flash",  # Can be overridden here if a different model is needed for testing.
+            camera_index=0,  # Use the default system camera (usually index 0).
+            fps_limit=0.2  # Set a low FPS for testing (1 frame every 5 seconds) to easily observe behavior.
+        )
+
+        print("CAMERA_AGENT MAIN: Starting agent for testing...")
+        test_agent.start()  # Start the agent's monitoring thread.
+
+        try:
+            print("CAMERA_AGENT MAIN: Agent running. Monitoring for video changes for approximately 20 seconds.")
+            print(f"CAMERA_AGENT MAIN: Check the log file '{test_agent.log_file}' for output.")
+            # Keep the main thread (this script) alive for a period to allow the background agent thread to run.
+            time.sleep(20)
+        except KeyboardInterrupt:
+            # Allow the user to stop the test prematurely by pressing Ctrl+C in the terminal.
+            print("CAMERA_AGENT MAIN: Test interrupted by user (KeyboardInterrupt).")
+        finally:
+            # This `finally` block ensures that the agent is stopped cleanly,
+            #  regardless of whether the test completes normally or is interrupted.
+            print("CAMERA_AGENT MAIN: Stopping agent...")
+            test_agent.stop()  # Stop the agent's monitoring thread and release resources.
+            print("CAMERA_AGENT MAIN: Direct test finished.")
+    else:
+        # If required environment variables not set, print an error message and skip the test.
+        print("CAMERA_AGENT MAIN: CRITICAL - Environment variables for Vertex AI "
+              "(GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION) or GOOGLE_GENAI_USE_VERTEXAI=True "
+              "not set correctly in .env file.")
+        print(f"  Loaded Project ID: {project_id_env}")
+        print(f"  Loaded Location: {location_env}")
+        print(f"  Use Vertex AI: {use_vertex_env}")
+        print(f"  The .env file was expected at: {dotenv_path}")
+        print("CAMERA_AGENT MAIN: Skipping direct agent test.")
diff --git a/in_progress/streaming-test2/app/config.py b/in_progress/streaming-test2/app/config.py
new file mode 100644
index 000000000..7f15592d9
--- /dev/null
+++ b/in_progress/streaming-test2/app/config.py
@@ -0,0 +1,23 @@
+# Configuration settings for the application
+
+# Vertex AI / Gemini Model Configuration
+GOOGLE_CLOUD_PROJECT = None # Will be loaded from .env
+GOOGLE_CLOUD_LOCATION = None # Will be loaded from .env
+MODEL_NAME = "gemini-2.0-flash" # Updated based on user feedback for video functionality
+
+# Audio settings
+AUDIO_SAMPLE_RATE = 16000
+AUDIO_CHANNELS = 1
+# Add other audio settings as needed (e.g., chunk size for streaming)
+
+MODEL_NAME = "gemini-2.0-flash"
+
+# Video settings
+VIDEO_FPS = 10 # Default FPS for video processing by VideoMonitor
+# Add other video settings as needed
+
+# Web server settings
+WEB_HOST = "0.0.0.0"
+WEB_PORT = 8080
+
+# You can add more configuration variables here as the project grows.
diff --git a/in_progress/streaming-test2/app/main.py b/in_progress/streaming-test2/app/main.py
new file mode 100644
index 000000000..799f6bdea
--- /dev/null
+++ b/in_progress/streaming-test2/app/main.py
@@ -0,0 +1,200 @@
+# Import necessary Python modules
+import os  # For interacting with the operating system (e.g., environment variables, file paths)
+import asyncio  # For asynchronous programming, core to FastAPI's async features
+import uvicorn  # The ASGI server that will run the FastAPI application; it's used here
+                #  if the script is run directly, but typically you run Uvicorn from
+                #  the command line.
+
+from contextlib import asynccontextmanager  # For creating async context managers (for lifespan)
+
+# Third-party libraries for FastAPI
+from fastapi import FastAPI, Request, HTTPException  # Core FastAPI classes, Request object for incoming requests, & HTTPException for errors.
+from fastapi.responses import HTMLResponse, FileResponse, JSONResponse, StreamingResponse  # Different types of HTTP responses.
+from fastapi.staticfiles import StaticFiles  # For serving static files (that is, files that don't change, like HTML, CSS, JS).
+from fastapi.middleware.cors import CORSMiddleware  # For handling Cross-Origin Resource Sharing (commonly needed for web apps).
+
+# Python standard libraries
+from pathlib import Path  # For object-oriented manipulation of file system paths.
+import json  # For parsing JSON data.
+
+# Application-specific imports from this project
+# The `.` in `.camera_agent` means that the module is in the same directory ("app") as this file.
+from .camera_agent import CameraAgent  # Import the CameraAgent class.
+from .audio_agent import AudioAgent  # Import the AudioAgent class.
+
+# Import for loading environment variables from a `.env` file.
+from dotenv import load_dotenv
+
+# --- Configuration and Environment Setup ---
+# Define the path to the `.env` file.
+# __file___ represents the path to the current file (i.e., `streaming-test2/app/main.py`).
+# Path(__file__).resolve() ensures an absolute path.
+  # .parent takes us from `app/main.py` to `app/`.
+  # .parent.parent takes us from `app/` to `streaming-test2/`.
+  # Then we append '.env' to form the full path.
+dotenv_path = Path(__file__).resolve().parent.parent / '.env'
+load_dotenv(dotenv_path=dotenv_path)  # Load the environment variables from the .env file.
+
+# Read environment variables after loading the .env file.
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+LOCATION = os.getenv("GOOGLE_CLOUD_LOCATION")
+
+# Global variables to hold the singleton instances of the agents.
+camera_agent_instance = None
+audio_agent_instance = None
+
+# --- Lifespan Event Handler ---
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global camera_agent_instance, audio_agent_instance  # Declare that we will modify the global variables.
+    print("MAIN_APP: FastAPI server starting up (with lifespan)...")
+
+    # Critical check for environment variables.
+    if not PROJECT_ID or not LOCATION:
+        print(f"MAIN_APP: FATAL - GOOGLE_CLOUD_PROJECT or GOOGLE_CLOUD_LOCATION not set in .env (path: {dotenv_path}). Agents will not initialize.")
+        camera_agent_instance = None
+        audio_agent_instance = None
+        yield # This is where the application runs. Startup has completed.
+        # Code after yield runs on shutdown.
+        print("MAIN_APP: FastAPI server shutting down (with lifespan)...")
+        if camera_agent_instance: # Should be None if critical env vars were missing
+            print("MAIN_APP: Stopping CameraAgent...")
+            camera_agent_instance.stop()
+        return #Use return instead of exception for normal startup logic
+
+    # Initialize agents here
+    try:
+        print("MAIN_APP: Initializing CameraAgent...")
+        camera_agent_instance = CameraAgent(project_id=PROJECT_ID, location=LOCATION)
+        camera_agent_instance.start()
+        print("MAIN_APP: CameraAgent initialized and started.")
+    except Exception as e:
+        print(f"MAIN_APP: Error initializing CameraAgent: {e}")
+        camera_agent_instance = None
+
+    try:
+        print("MAIN_APP: Initializing AudioAgent...")
+        audio_agent_instance = AudioAgent(project_id=PROJECT_ID, location=LOCATION)
+        print("MAIN_APP: AudioAgent initialized.")
+    except Exception as e:
+        print(f"MAIN_APP: Error initializing AudioAgent: {e}")
+        audio_agent_instance = None
+
+    yield # This is the point where the application is considered running.
+    # After the application has finished, code here will be executed as part of shutdown.
+    print("MAIN_APP: FastAPI server shutting down (after yield in lifespan)...")
+    if camera_agent_instance:
+        print("MAIN_APP: Stopping CameraAgent...")
+        camera_agent_instance.stop()
+
+    # Audio agent sessions are managed by the AudioAgent itself when SSE connections close.
+
+# --- FastAPI Application Initialization with Lifespan ---
+app = FastAPI(lifespan=lifespan)  # Initialize FastAPI with the lifespan handler.
+
+# CORS (Cross-Origin Resource Sharing) Middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Define paths for static files and log files.
+STATIC_DIR = Path(__file__).resolve().parent / "static"
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+LOGS_DIR = Path(__file__).resolve().parent.parent / "logs"
+
+# --- HTML Page Endpoint ---
+@app.get("/", response_class=HTMLResponse)
+async def get_root():
+    return FileResponse(STATIC_DIR / "index.html")
+
+# --- Audio Agent Endpoints ---
+@app.get("/events/{user_id}")
+async def audio_sse_endpoint(user_id: str, request: Request, is_audio: str = "true"):
+    if not audio_agent_instance:
+        print("MAIN_APP SSE: AudioAgent not initialized.")
+        raise HTTPException(status_code=503, detail="AudioAgent not available.")
+
+    print(f"MAIN_APP SSE request for user: {user_id}, audio: {is_audio}")
+    try:
+        live_events, _ = await audio_agent_instance.start_adk_session(user_id, is_audio == "true")
+
+        async def event_generator():
+            """
+Async generator that yields data chunks from the AudioAgent's SSE processor.
+It also monitors if the client disconnects, and performs cleanup.
+            """
+            try:
+                async for data_chunk in audio_agent_instance.agent_to_client_sse_handler(user_id):
+                    if await request.is_disconnected():
+                        break
+                    yield data_chunk
+            except asyncio.CancelledError:
+                print(f"MAIN_APP: SSE for {user_id} cancelled.")
+            except Exception as e:
+                print(f"MAIN_APP: SSE error for {user_id}: {e}")
+            finally:
+                print(f"MAIN_APP: Cleaning ADK session for {user_id} (SSE ended).")
+                await audio_agent_instance.stop_adk_session(user_id)
+
+        # The event_generator must be returned by the endpoint function for StreamingResponse
+        # The StreamingResponse will then iterate over the async generator.
+        return StreamingResponse(event_generator(), media_type="text/event-stream")
+    except Exception as e:
+        print(f"MAIN_APP: Error starting SSE for {user_id}: {e}")
+        raise HTTPException(status_code=500, detail=f'Failed to start session: {str(e)}')
+
+@app.post("/send/{user_id}")
+async def audio_send_message_endpoint(user_id: str, request: Request):
+    if not audio_agent_instance:
+        print("MAIN_APP SEND ERROR: AudioAgent not initialized.")
+        raise HTTPException(status_code=503, detail="AudioAgent not available.")
+
+    try:
+        message_data = await request.json()
+    except json.JSONDecodeError:
+        print("MAIN_APP SEND ERROR: Invalid JSON received.")
+        raise HTTPException(status_code=400, detail="Invalid JSON.")
+
+    response = await audio_agent_instance.client_to_agent_handler(user_id, message_data)
+
+    if response.get("error"):
+        print(f"MAIN_APP SEND ERROR: Agent returned error for user {user_id}: {response['error']}")
+        raise HTTPException(status_code=400, detail=response["error"])
+    return response
+
+# --- Log File Fetching Endpoints ---
+@app.get("/logs/{log_type}")
+async def get_log_content(log_type: str):
+    if log_type not in ["camera", "audio"]:
+        print(f"MAIN_APP LOG ERROR: Invalid log type requested: {log_type}")
+        raise HTTPException(status_code=404, detail="Invalid log type. Use 'camera' or 'audio'.")
+
+    log_file_name = f"{log_type}_log.json"
+    log_file_path = LOGS_DIR / log_file_name
+
+    if not log_file_path.exists() or log_file_path.stat().st_size == 0:
+        return JSONResponse(content=[], status_code=200)
+
+    try:
+        with open(log_file_path, 'r') as f:
+            log_content = json.load(f)
+        return JSONResponse(content=log_content)
+    except Exception as e:
+        print(f'MAIN_APP LOG ERROR: Error reading log file {log_file_path}: {e}')
+        return JSONResponse(content=[{"error": f"Failed to parse {log_file_name}"}], status_code=200)
+
+
+@app.get("/agent/camera/status")
+async def get_camera_agent_status():
+    if camera_agent_instance and camera_agent_instance.running:
+        return {"status": "running", "monitoring": (camera_agent_instance.video_monitor.cap is not None) and camera_agent_instance.video_monitor.cap.isOpened()}
+    return {"status": "stopped"}
+
+# --- Run the Application (for local development) ---
+if __name__ == "__main__":
+    print("MAIN_APP: To run this app, navigate to the `streaming-test2` directory and use Uvicorn. Example:") # Corrected comment
+    print("python -m uvicorn app.main:app --reload --port 8000 --host 0.0.0.0  (from inside streaming-test2)") # Corrected command
diff --git a/in_progress/streaming-test2/app/static/css/style.css b/in_progress/streaming-test2/app/static/css/style.css
new file mode 100644
index 000000000..77abd0d13
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/css/style.css
@@ -0,0 +1,11 @@
+body { font-family: sans-serif; margin: 20px; background-color: #f4f4f4; color: #333; }
+.container { display: flex; gap: 20px; margin-bottom: 20px; }
+.agent-section { background-color: #fff; padding: 15px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); flex: 1; }
+h1, h2, h3 { color: #555; }
+pre { background-color: #eee; padding: 10px; border-radius: 4px; white-space: pre-wrap; word-break: break-all; max-height: 300px; overflow-y: auto; font-family: monospace; }
+button { padding: 10px 15px; margin: 5px; border: none; border-radius: 4px; background-color: #007bff; color: white; cursor: pointer; }
+button:disabled { background-color: #ccc; }
+button:hover:not(:disabled) { background-color: #0056b3; }
+input[type="text"] { padding: 10px; margin-right: 10px; border: 1px solid #ddd; border-radius: 4px; width: calc(100% - 120px); }
+.input-area { margin-top: 20px; display: flex; }
+.agent-speech-bubble { margin-top: 10px; padding: 10px; background-color: #e9f5ff; border-radius: 5px; min-height: 20px; border: 1px solid #cce4ff; }
diff --git a/in_progress/streaming-test2/app/static/index.html b/in_progress/streaming-test2/app/static/index.html
new file mode 100644
index 000000000..2b2d095da
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/index.html
@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Multimodal Streaming Agent</title>
+    <link rel="stylesheet" href="/static/css/style.css">
+</head>
+<body>
+    <h1>Multimodal Streaming Agent</h1>
+    <div class="container">
+        <div class="agent-section">
+            <h2>Camera Agent</h2>
+            <div id="cameraAgentStatus">Status: Unknown</div>
+            <div class="log-display">
+                <h3>Camera Log</h3>
+                <pre id="cameraLogOutput">Loading camera log...</pre>
+            </div>
+        </div>
+        <div class="agent-section">
+            <h2>Audio Agent</h2>
+            <div id="audioAgentStatus">Status: Idle</div>
+            <button id="startAudioButton">Start Audio Interaction</button>
+            <button id="stopAudioButton" disabled>Stop Audio Interaction</button>
+            <div class="log-display">
+                <h3>Audio Log</h3>
+                <pre id="audioLogOutput">Loading audio log...</pre>
+            </div>
+            <div id="currentAgentResponse" class="agent-speech-bubble"></div>
+        </div>
+    </div>
+    <div class="input-area">
+        <input type="text" id="textInput" placeholder="Type message to audio agent..." disabled>
+        <button id="sendTextButton" disabled>Send Text</button>
+    </div>
+    <script src="/static/js/app.js"></script>
+</body>
+</html>
diff --git a/in_progress/streaming-test2/app/static/js/app.js b/in_progress/streaming-test2/app/static/js/app.js
new file mode 100644
index 000000000..3b1333c0d
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/js/app.js
@@ -0,0 +1,184 @@
+// DOM Elements
+const camStatus = document.getElementById('cameraAgentStatus');
+const camLog = document.getElementById('cameraLogOutput');
+const audStatus = document.getElementById('audioAgentStatus');
+const audLog = document.getElementById('audioLogOutput');
+const startBtn = document.getElementById('startAudioButton');
+const stopBtn = document.getElementById('stopAudioButton');
+const textIn = document.getElementById('textInput');
+const sendBtn = document.getElementById('sendTextButton');
+const agentSpeech = document.getElementById('currentAgentResponse');
+
+// Import the audio worklet helpers from ADK example
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet, stopMicrophone } from "./audio-recorder.js";
+
+let sse = null;
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+let is_audio = false; // Tracks audio mode
+const sessId = Date.now().toString() + Math.random().toString().substring(3);
+console.log("Client Session ID:", sessId);
+
+// --- Utility Functions ---
+function b64ToAb(b64) { const s = atob(b64); const l = s.length; const u = new Uint8Array(l); while(l--) u[l] = s.charCodeAt(l); return u.buffer; }
+function abToB64(ab) { let r = ''; const b = new Uint8Array(ab); const l = b.byteLength; for(let i=0;i<l;i++) r+=String.fromCharCode(b[i]); return btoa(r); }
+
+// --- Log Fetching ---
+async function fetchLog(type, el) {
+    try {
+        const res = await fetch(`/logs/${type}`);
+        if(!res.ok) { el.textContent = `Error: ${res.statusText}`; return; }
+        const logs = await res.json();
+        if(logs.error) el.textContent = `Error in log data: ${logs.error}`;
+        else if(Array.isArray(logs)) el.textContent = logs.map(l => `[${l.timestamp || ''}] ${l.speaker || l.type || ''}: ${l.text || l.comment_by_llm || l.description_by_cv || ''}`).join('\n');
+        else el.textContent = "Bad log format.";
+    } catch (e) { el.textContent = `Failed to fetch ${type} log: ${e}`; }
+}
+async function fetchCamStatus() { try { const r = await fetch('/agent/camera/status'); const d = await r.json(); camStatus.textContent = `Status: ${d.status}${d.monitoring?' (Monitoring)':''}`; } catch (e) { camStatus.textContent = "Status: Error"; }}
+
+// --- SSEConnection for Audio Agent ---
+function connectSSE() {
+    if(sse) sse.close();
+    const sseUrl = `/events/${sessId}?is_audio=${is_audio}`; // is_audio is a global
+    sse = new EventSource(sseUrl);
+    sse.onopen = () => {
+        console.log("is_audio = ", is_audio, "SSE connection opened.");
+        audStatus.textContent = is_audio?"Audio Connected, Listening...":"Text Connected";
+        startBtn.disabled=true; // Start button now disabled
+        stopBtn.disabled=false; // Stop button enabled
+        textIn.disabled=false;
+        sendBtn.disabled=false;
+        agentSpeech.textContent="";
+    };
+    sse.onmessage = (evt) => {
+        const msg = JSON.parse(evt.data); // console.log("incoming", msg);
+        if(msg.error) { console.error("SSE Server Error:", msg.error); audStatus.textContent = `Error: ${msg.error}`; agentSpeech.textContent = `Error: ${msg.error}`; return; }
+        if(msg.turn_complete) {
+            console.log("Agent turn done.");
+            if(audStatus.textContent.includes("Speaking") || audStatus.textContent.includes("Responding")) {
+                audStatus.textContent = is_audio?"Audio Connected, Listening...":"Text Connected";
+            }
+            return;
+        }
+        if(!is_audio && msg.mime_type === "text/plain") { // Text only mode
+            agentSpeech.textContent += msg.data;
+            audStatus.textContent = "Agent Responding (text)...";
+        } else if(is_audio) {
+            if((msg.mime_type==="audio/pcm" || msg.mime_type==="audio/opus") && audioPlayerNode && msg.data) {
+                audioPlayerNode.port.postMessage(b64ToAb(msg.data));
+                audStatus.textContent="Agent Speaking...";
+                agentSpeech.textContent=""; // Clear text when audio comes
+            } else if(msg.mime_type==="text/plain") { // Text accompanying audio or transcription
+                // Don't change audioStatus if already speaking, just show transcription
+                agentSpeech.textContent += msg.data;
+            }
+        }
+        fetchLog('audio', audLog);
+    };
+    sse.onerror = (e) => {
+        console.error("SSE connection error or closed.", e);
+        audStatus.textContent = "Status: Connection Error. Retrying...";
+        if(sse) sse.close();
+        // Automatic reconnect without user intervention
+        setTimeout(() => connectSSE(), 3000);
+    };
+}
+
+// --- Sending Messages ---
+async function sendAudio(ab) { // ab is ArrayBuffer
+    if(!sse || sse.readyState !== EventSource.OPEN) { console.warn("SSE not open. Cannot send audio."); return; }
+    try { const r = await fetch(`/send/${sessId}`, { method: 'POST', headers: {'Content-Type':'application/json'}, body: JSON.stringify({mime_type: 'audio/pcm', data:abToB64(ab)})}); if(!r.ok) console.error("Send audio error:", r.statusText); } catch (e) { console.error("Send audio exception:", e); }
+}
+async function sendText(txt) {
+    if(!sse || sse.readyState !== EventSource.OPEN) { alert("Not connected to audio agent!"); return; }
+    if(!txt.trim()) return;
+    try { const r = await fetch(`/send/${sessId}`, { method: 'POST', headers: {'Content-Type':'application/json'}, body: JSON.stringify({mime_type: 'text/plain', data:txt})}); if(!r.ok) alert(`Send error: ${r.statusText}`); else agentSpeech.textContent=""; } catch (e) { alert(`Send exception: ${e}`);}
+    textIn.value = "";
+}
+
+// --- Audio Handling ---
+// Callback for the audio recorder worklet
+function audioRecorderHandler(pcmData) { // pcmData is an ArrayBuffer from audio-recorder.js
+sendAudio(pcmData);
+}
+
+async function startAudioFlow() {
+    is_audio = true;
+    audStatus.textContent = "Status: Initializing audio...";
+    try {
+        if(!audioPlayerNode || !audioPlayerContext) {
+            [audioPlayerNode, audioPlayerContext] = await startAudioPlayerWorklet();
+        }
+        if(!audioRecorderNode || !audioRecorderContext || !micStream) {
+            [audioRecorderNode, audioRecorderContext, micStream] = await startAudioRecorderWorklet(audioRecorderHandler);
+        }
+
+        connectSSE(); // Connect SSE, is_audio is true by default or already set
+        audStatus.textContent = "Status: Microphone active, recording...";
+        startBtn.disabled = true;
+        stopBtn.disabled = false;
+        textIn.disabled = false;
+        sendBtn.disabled = false;
+    } catch (e) {
+        console.error("Error starting audio flow: ", e);
+        audStatus.textContent = "Error: Failed to start audio.";
+        alert("Could not start audio. Check console and microphone permissions.");
+        is_audio = false; // Reset if failed
+    }
+}
+
+function stopAudioFlow() {
+    is_audio = false;
+    if(micStream) {
+        stopMicrophone(micStream);
+        micStream = null; // Reset micStream
+    }
+    if(audioRecorderNode) {
+        audioRecorderNode.disconnect();
+        // AudioRecorderNode and context might be reused or should be closed if no longer needed
+    }
+    if(sse) {
+        sse.close();
+        console.log("is_audio = ", is_audio, "SSE connection closed by client.");
+    }
+    audStatus.textContent = "Status: Idle";
+    startBtn.disabled = false;
+    stopBtn.disabled = true;
+    textIn.disabled = true;
+    sendBtn.disabled = true;
+}
+
+// --- Event Listeners ---
+startBtn.addEventListener('click', startAudioFlow);
+stopBtn.addEventListener('click', stopAudioFlow);
+
+sendBtn.addEventListener('click', () => {
+    if(!sse || sse.readyState !== EventSource.OPEN && is_audio ){
+        // If audio is intended but not connected, try to start it
+        console.warn("Audio not connected, attempting to start before sending text.");
+        startAudioFlow(); // Attempt to re-establish
+        setTimeout(() => sendText(textIn.value), 1000); // Try sending after a delay
+        return;
+    }
+    sendText(textIn.value);
+});
+textIn.addEventListener('keypress', (e) => {
+    if(e.key==='Enter') { sendText(textIn.value); }
+});
+
+// --- Initialization ---
+function init() {
+    console.log("App init, session:", sessId);
+    fetchLog('camera',camLog); fetchLog('audio',audLog); fetchCamStatus();
+    setInterval(()=>{ fetchLog('camera',camLog); fetchLog('audio',audLog); fetchCamStatus(); }, 5000);
+    // Don't automatically setup audio or connect SSE on load.
+    // User needs to click 'Start Audio Interaction'
+    // setupAudio();
+    audStatus.textContent = "Status: Idle";
+}
+init();
diff --git a/in_progress/streaming-test2/app/static/js/audio-player.js b/in_progress/streaming-test2/app/static/js/audio-player.js
new file mode 100644
index 000000000..c2a142530
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+
+
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+
+    // 3. Create an AudioWorkletNode
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
diff --git a/in_progress/streaming-test2/app/static/js/audio-recorder.js b/in_progress/streaming-test2/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..82806e6dd
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
diff --git a/in_progress/streaming-test2/app/static/js/pcm-player-processor.js b/in_progress/streaming-test2/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..824a90b32
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/js/pcm-player-processor.js
@@ -0,0 +1,74 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` |128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex !== this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
diff --git a/in_progress/streaming-test2/app/static/js/pcm-recorder-processor.js b/in_progress/streaming-test2/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..b1c54bd31
--- /dev/null
+++ b/in_progress/streaming-test2/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
diff --git a/in_progress/streaming-test2/app/templates/index.html b/in_progress/streaming-test2/app/templates/index.html
new file mode 100644
index 000000000..56da3a2af
--- /dev/null
+++ b/in_progress/streaming-test2/app/templates/index.html
@@ -0,0 +1,191 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Multimodal Agent Interface</title>
+    <style>
+        body { font-family: sans-serif; margin: 20px; background-color: #f4f4f4; color: #333; }
+        .container { display: flex; flex-direction: column; align-items: center; }
+        .video-container { border: 1px solid #ccc; margin-bottom: 20px; min-width: 320px; min-height: 240px; background-color: #000;}
+        .chat-container { width: 80%; max-width: 600px; margin-bottom: 20px; }
+        .chat-box { height: 300px; border: 1px solid #ccc; overflow-y: scroll; padding: 10px; background-color: #fff; margin-bottom: 10px; }
+        .chat-message { margin-bottom: 8px; padding: 5px; border-radius: 4px; }
+        .user-message { background-color: #e1f5fe; text-align: right; }
+        .model-message { background-color: #f0f4c3; }
+        .controls { display: flex; justify-content: center; align-items: center; width: 80%; max-width: 600px; }
+        #chatInput { flex-grow: 1; padding: 8px; margin-right: 10px; border: 1px solid #ccc; border-radius: 4px;}
+        button { padding: 8px 15px; border: none; border-radius: 4px; cursor: pointer; background-color: #4CAF50; color: white; margin-left: 5px; } /* Added margin-left to button */
+        button:hover { background-color: #45a049; }
+        .status { margin-top: 10px; font-style: italic; color: #555; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Live Multimodal Agent</h1>
+
+        <div class="video-container">
+            <img id="videoFeed" src="/video_feed" width="640" height="480" alt="Video Feed">
+        </div>
+
+        <div class="chat-container">
+            <div id="chatBox" class="chat-box">
+                <!-- Chat messages will appear here -->
+            </div>
+            <div class="controls">
+                <input type="text" id="chatInput" placeholder="Type your message...">
+                <button onclick="sendChatMessage()">Send</button>
+                <button onclick="sendVoiceMessage()" id="sendVoiceBtn">Send Voice (Record 5s)</button>
+            </div>
+            <div class="status" id="statusArea">Agent status will appear here.</div>
+        </div>
+         <button onclick="startAgent()">Start Agent</button>
+         <button onclick="stopAgent()">Stop Agent</button>
+    </div>
+
+    <script>
+        const chatBox = document.getElementById('chatBox');
+        const chatInput = document.getElementById('chatInput');
+        const statusArea = document.getElementById('statusArea');
+
+        function addMessage(text, sender) {
+            const messageDiv = document.createElement('div');
+            messageDiv.classList.add('chat-message', sender + '-message');
+            messageDiv.textContent = text;
+            chatBox.appendChild(messageDiv);
+            chatBox.scrollTop = chatBox.scrollHeight; // Auto-scroll
+        }
+
+        async function sendChatMessage() {
+            const messageText = chatInput.value;
+            if (!messageText.trim()) return;
+
+            addMessage(messageText, 'user');
+            chatInput.value = '';
+
+            try {
+                const response = await fetch('/send_chat_message', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ message: messageText })
+                });
+                const data = await response.json();
+                if (data.reply) {
+                    addMessage(data.reply, 'model');
+                } else if (data.error) {
+                    addMessage('Error: ' + data.error, 'model');
+                }
+            } catch (error) {
+                console.error('Error sending chat message:', error);
+                addMessage('Error connecting to agent.', 'model');
+            }
+        }
+
+        chatInput.addEventListener('keypress', function(event) {
+            if (event.key === 'Enter') {
+                sendChatMessage();
+            }
+        });
+
+        async function startAgent() {
+            statusArea.textContent = "Attempting to start agent...";
+            try {
+                const response = await fetch('/start_interaction');
+                const data = await response.json();
+                statusArea.textContent = data.status || data.error;
+                addMessage(data.status || data.error, 'model');
+                // Connect to SSE after agent is confirmed started
+                if (response.ok && data.status && data.status.includes("started")) {
+                    connectToAgentEvents();
+                }
+            } catch (error) {
+                 statusArea.textContent = "Error starting agent.";
+                 addMessage('Error starting agent.', 'model');
+            }
+        }
+
+        async function stopAgent() {
+            statusArea.textContent = "Attempting to stop agent...";
+            try {
+                const response = await fetch('/stop_interaction');
+                const data = await response.json();
+                statusArea.textContent = data.status || data.error;
+                addMessage(data.status || data.error, 'model');
+                if (eventSource) {
+                    eventSource.close();
+                    statusArea.textContent = "Agent stopped. Event stream closed.";
+                }
+            } catch (error) {
+                 statusArea.textContent = "Error stopping agent.";
+                 addMessage('Error stopping agent.', 'model');
+            }
+        }
+
+        async function sendVoiceMessage() {
+            const sendVoiceButton = document.getElementById("sendVoiceBtn");
+            addMessage("🎤 Recording for 5s... Please speak.", "user");
+            statusArea.textContent = "Recording audio...";
+            sendVoiceButton.disabled = true;
+
+            try {
+                const response = await fetch("/send_voice_message", { method: "POST" });
+                const data = await response.json();
+                if (data.status) {
+                    statusArea.textContent = data.status;
+                    // Messages (transcription, model reply) should come via SSE
+                } else if (data.error) {
+                    addMessage("Voice Error: " + data.error, "model");
+                    statusArea.textContent = "Voice error: " + data.error;
+                }
+            } catch (error) {
+                console.error("Error sending voice message:", error);
+                addMessage("Error processing voice input.", "model");
+                statusArea.textContent = "Error processing voice input.";
+            } finally {
+                sendVoiceButton.disabled = false;
+            }
+        }
+
+        // Function to fetch and display video change notifications from the agent
+        let eventSource = null;
+        function connectToAgentEvents() {
+            if (eventSource) {
+                eventSource.close();
+            }
+            statusArea.textContent = "Connecting to agent events...";
+            eventSource = new EventSource("/agent_events");
+
+            eventSource.onopen = function() {
+                statusArea.textContent = "Connected to agent events.";
+                addMessage("Connected to agent for live updates.", "model");
+            };
+
+            eventSource.onmessage = function(event) {
+                const data = JSON.parse(event.data);
+                if (data.type === "video_change_comment") {
+                    addMessage("Video Event: " + data.comment, "model");
+                } else if (data.type === "status") {
+                     statusArea.textContent = data.message;
+                } else if (data.type === "transcription") {
+                    addMessage("You (transcribed): " + data.text, "user");
+                } else if (data.type === "model_response_audio_text") {
+                    addMessage("Agent: " + data.text, "model");
+                }
+            };
+
+            eventSource.onerror = function(err) {
+                console.error("EventSource failed:", err);
+                statusArea.textContent = "Disconnected from agent events.";
+                addMessage("Lost connection to agent live updates.", "model");
+                if (eventSource) eventSource.close();
+            };
+        }
+
+        window.onload = () => {
+            addMessage("Welcome! Click 'Start Agent' to begin.", "model");
+            // Do not automatically connectToAgentEvents here.
+            // connectToAgentEvents will be called by startAgent().
+        };
+
+    </script>
+</body>
+</html>
diff --git a/in_progress/streaming-test2/app/video_utils.py b/in_progress/streaming-test2/app/video_utils.py
new file mode 100644
index 000000000..0da00d1b6
--- /dev/null
+++ b/in_progress/streaming-test2/app/video_utils.py
@@ -0,0 +1,304 @@
+import cv2 # OpenCV library for computer vision tasks
+import numpy as np # For numerical operations, especially with images
+import time # For handling FPS limiting
+
+# Attempt to import VIDEO_FPS from app.config
+# If app.config or VIDEO_FPS is not found, use a default value.
+try:
+    from app.config import VIDEO_FPS
+except ImportError:
+    print("VIDEO_UTILS: Could not import VIDEO_FPS from app.config. Using default value 1.")
+    VIDEO_FPS = 1
+
+
+# --- Video Capture Class ---
+class VideoMonitor:
+    """
+    VideoMonitor encapsulates operations related to capturing and processing
+    video frames from a camera. It can start and stop video capture, retrieve
+    individual frames, and detect significant changes or motion between frames.
+    """
+    def __init__(self, camera_index=0, fps_limit=None):
+        """
+        Initializes the VideoMonitor.
+
+        Args:
+            camera_index (int, optional): The index of the camera to use (e.g., 0 for default).
+                                        Defaults to 0.
+            fps_limit (int or None, optional): Limit processing to this many frames per
+                                               second. None for no limit (process as
+                                               fast as camera provides).
+                                               Defaults to `VIDEO_FPS` from `config.py` if None is provided.
+        """
+        self.camera_index = camera_index  # Store the camera index
+        self.cap = None  # Initialize the capture object to None; opened in `start_capture`
+        self.previous_frame = None # Stores the previous frame for change detection
+        self.last_processed_time = 0  # Timestamp of the last processed frame, for FPS limiting
+
+        # Set the FPS limit. If `fps_limit` is not provided (None), use the VIDEO_FPS value
+        # from the `app.config` file (or its default if import failed). This allows a centralized default FPS configuration.
+        self.fps_limit = fps_limit if fps_limit is not None else VIDEO_FPS
+
+        # Store a reference to the `cv2` module, primarily for using `cv2.imencode` without
+        # having to import it in other files that might use this class.
+        self.cv2 = cv2
+
+        # Optional: Hog Feature Descriptor for person detection. Commented out as it
+        # can be slow and is not tthe rimary focus of this module.
+        # self.hog = cv2.HOGDescriptor()
+        # self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+        # Background subtractor: This is a common OpenCV technique for detecting motion.
+        # It builds a model of the static background and then compares new frames to it.
+        #   - `history`: Number of frames used to build the background model.
+        #   - `varThreshold`: Threshold on the Mahalanobis distance to classify a pixel as foreground.
+        #   - `detectShadows`: Whether to detect and mark shadows. Set to False as shadows can be
+        #     incorrectly detected as foreground objects.
+        self.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+    def start_capture(self):
+        """
+        Starts video capture from the specified camera.
+        It initializes the OpenCV `cv2.VideoCapture` object.
+
+        Returns:
+            bool: True if capture started successfully, False otherwise.
+        """
+        # Create a VideoCapture object. The argument is the camera index.
+        self.cap = cv2.VideoCapture(self.camera_index)
+
+        # Check if the camera was opened successfully.
+        if not self.cap.isOpened():
+            print(f"Error: Could not open video stream from camera index {self.camera_index}.")
+            print("Make sure a camera is connected and accessible.")
+            print("If using a virtual environment, ensure OpenCV can access camera drivers.")
+            self.cap = None  # Ensure `self.cap` is set to None if the operation failed.
+            return False
+
+        print(f"Video capture started on camera index {self.camera_index}.")
+        # Reset the previous frame when starting a new capture session.
+        self.previous_frame = None
+        # Re-initialize the background subtractor to ensure it has a fresh background
+        # history when capture starts.
+        self.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+        return True
+
+    def stop_capture(self):
+        """
+        Stops video capture and releases the camera resource.
+        """
+        if self.cap:
+            self.cap.release()  # Release the camera.
+            self.cap = None  # Set the capture object to None.
+            print("Video capture stopped.")
+        # Reset the previous frame as it's no longer relevant.
+        self.previous_frame = None
+
+    def get_frame(self):
+        """
+        Retrieves a single frame from the camera.
+
+        Returns:
+            np.ndarray: A numpy array representing the frame, or None if capture failed.
+        """
+        # Check if the capture object is valid and the camera is open.
+        if not self.cap or not self.cap.isOpened():
+            # print("Capture not started or failed.") # Can be noisy if called frequently.
+            return None
+
+        # Read a frame from the camera.
+        #   - `ret`: A boolean indicating if the frame was read successfully.
+        #   - `frame`: The captured frame (a np.ndarray array).
+        ret, frame = self.cap.read()
+
+        # If the frame was not received successfully (e.g., end of a video file).
+        if not ret:
+            print("Error: Can't receive frame (stream end?). Exiting ...")
+            return None
+        return frame
+
+    def detect_significant_change(self, frame: np.ndarray, threshold_abs_diff=30, min_contour_area=500) -> tuple[bool, str, bytes | None]:
+        """
+        Detects a significant change in the video frame using background subtraction,
+        morphological operations for noise reduction, and contour analysis for motion.
+        Color conversion to grayscale is not used here as background subtraction
+        often works better on color frames directly or has built-in mechanisms.
+
+        Args:
+            frame (np.ndarray): The current video frame.
+            threshold_abs_diff (int, optional): Sensitivity for absolute difference
+                method. (Not used directly with MOG2 but kept for potential alternatives).
+            min_contour_area (int, optional): Minimum area of a contour to be considered
+                as significant motion. Defaults to 500.
+
+        Returns:
+            tuple[bool, str, bytes | None]:
+                - has_changed (bool): True if a significant change was detected, False otherwise.
+                - change_description (str): A textual description of the change.
+                - processed_frame_bytes (bytes | None): The JPEG encoded frame if
+                  a change is detected, otherwise None.
+        """
+        if frame is None:
+            return False, "No frame", None
+
+        # 1. Background Subtraction
+        # The bg_subtractor.apply() method computes a foreground mask.
+        # The mask consists of white pixels for foreground and black for background.
+        fgmask = self.bg_subtractor.apply(frame)
+
+        # 2. Noise Reduction
+        # Morphological operations are used to reduce noise and improve the foreground mask.
+        # - `cv2.MORPH_OPEN`: Removes small white noise from the foreground.
+        #   It is an erosion followed by a dilation.
+        # kernel: A small matrix used for the morphological operations.
+        kernel = np.ones((5,5), np.uint8)
+        fgmask_opened = self.cv2.morphologyEx(fgmask, self.cv2.MORPH_OPEN, kernel)
+        # - `cv2.MORPH_CLOSE`: Fills small holes in the foreground objects.
+        #   It is a dilation followed by an erosion.
+        fgmask_closed = cv2.morphologyEx(fgmask_opened, self.cv2.MORPH_CLOSE, kernel)
+
+        # 3. Find Contours of Moved Objects
+        # Contours are the boundaries of the white regions (foreground objects) in the mask.
+        #   - `cv2.RETR_EXTERNAL`: Retrieves only the extreme outer contours.
+        #   - `cv2.CHAIN_APPROX_SIMPLE`: Compresses horizontal, vertical, and diagonal segments
+        #     and leaves only their end points. For example, a rectangle's is encoded with 4 points.
+        contours, _ = cv2.findContours(fgmask_closed, self.cv2.RETR_EXTERNAL, self.cv2.CHAIN_APPROX_SIMPLE)
+
+        significant_motion_detected = False
+        for contour in contours:
+            # Calculate the area of each contour.
+            if cv2.contourArea(contour) > min_contour_area:
+                # If the area is greater than the threshold, consider it significant motion.
+                significant_motion_detected = True
+                # Optional: Draw bounding box on the original frame for visualization or describe as "Visual motion"
+                # description.
+                # (x, y, w, h) = cv2.boundingRect(contour)
+                # cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255,0), 2) # Draw on original for viz
+                break # Found one significant motion, so no need to check further contours.
+
+        if significant_motion_detected:
+            change_description = "Significant motion detected in the scene."
+            # Encode the current frame to JPEG bytes to send to the model.
+            #   - `ret`: True if encoding was successful.
+            #   - `buffer`: The numpy array containing the encoded bytes.
+            ret, buffer = self.cv2.imencode('.jpg', frame)
+            if ret:
+                frame_bytes = buffer.tobytes()  # Convert the numpy array to bytes.
+                return True, change_description, frame_bytes
+            else:
+                print("VideoMonitor: Error encoding frame to JPEG.")
+                return True, change_description, None # Encoding failed.
+
+        # If no significant motion was detected.
+        return False, "No significant change detected.", None
+
+
+    def process_frame_for_changes(self) -> tuple[bool, str, bytes | None]:
+        """
+        Captures a frame from the camera, detects significant changes, and limits
+        the processing rate according to the `self.fps_limit`.
+
+        Returns:
+            tuple(bool, str, bytes | None):
+                - change_detected (bool): True if a change was detected, False otherwise.
+                - change_description (str): A description of the change or a status message.
+                - frame_bytes_if_change (bytes | None): The JPEG encoded frame bytes
+                  if a change was detected, otherwise None.
+        """
+        current_time = time.time()
+
+        # FPS limiting logic
+        if self.fps_limit and (current_time - self.last_processed_time) < (1.0 / self.fps_limit):
+            # If the time since the last processed frame is less than the desired frame
+            # interval (calculated from fps_limit), then skip processing this frame.
+            time.sleep(0.01)  # Add a small sleep to prevent a busy-loop if the camera is much
+                             # faster than the fps_limit, or if the camera capture itself is non-blocking.
+            return False, "FPS limit, frame skipped", None
+
+        self.last_processed_time = current_time  # Update the last processed time
+
+        frame = self.get_frame()  # Get a frame from the camera
+        if frame is None:
+            return False, "Failed to get frame", None
+
+        # Detect changes in the frame.
+        return self.detect_significant_change(frame)
+
+
+    # Optional: Person detection method.
+    # This is commented out because it can be slow and computationally intensive.
+    # It uses a Pre-trained HOG (Histogram of Oriented Gradients) detector.
+    # def detect_persons(self,frame: np.ndarray) -> tuple[bool, str, np.ndarray | None]:
+    #     if frame is None:
+    #         return False, "No frame for person detection", None
+
+    #     # To speed up detection, resize the frame to decrease the number of pixels to be processed
+    #     frame_resized = cv2.resize(frame, (640, 480))
+    #     # (rects, weights) = self.hog.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=1.05)
+
+    #     if len(rects) > 0:
+    #         # For now, just signal if any person is detected.
+    #         # Could be enhanced to count, track entries/exits relative to a previous state.
+    #         # for (x, y, w, h) in rects:
+    #         #     cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2) # Draw on original for viz
+
+    #         change_description = f"{len(rects)} person(s) detected in the view."
+    #         ret, buffer = self.cv2.imencode('.jpg', frame) # Send frame with detected people
+    #         if ret:
+    #             frame_bytes = buffer.tobytes() # Corrected
+    #             return True, change_description, frame_bytes
+    #         else:
+    #             return True, change_description, None
+    #     return False, "No persons detected.", None
+
+# This block allows the script to be run directly for testing.
+def main_test():
+    print("Testing video_utils.py...")
+    # This test requires a camera. If no camera, it will try to proceed but fail gracefully.
+    # The VideoMonitor class is defined in the same file, so it's in scope here.
+    monitor = VideoMonitor(camera_index=0, fps_limit=1) # Limit to 1 FPS for this test
+
+    if not monitor.start_capture():
+        print("Failed to start camera capture. Ensure a camera is connected and drivers are working.")
+        print("If running in a headless environment, this test might not be fully effective without a virtual camera.")
+    else:
+        print("Camera capture started. Will monitor for changes for a few seconds.")
+        start_time = time.time()
+        frames_processed = 0
+        try:
+            while time.time() - start_time < 10: # Run for 10 seconds
+                changed, description, frame_bytes = monitor.process_frame_for_changes()
+                if changed:
+                    print(f'Change detected: {description}')
+                    if frame_bytes:
+                        print(f"Frame data size: {len(frame_bytes)} bytes")
+                        # with open("detected_change.jpg", "wb") as f: # Option to save frame
+                        #     f.write(frame_bytes)
+                        # print("Saved detected_change.jpg")
+                elif description == "Failed to get frame":
+                    print("Stopping test due to frame capture failure.")
+                    break
+                elif description != "FPS limit, frame skipped": # Don't print "FPS limit" too often, but other status messages
+                    print(f'Status: {description} (Frame: {frames_processed})')
+
+                frames_processed +=1
+                # A short sleep is important if not FPS limited in process_frame_for_changes,
+                # if camera is very fast. Here it's mainly for responsive exit.
+                time.sleep(0.05)
+        except KeyboardInterrupt:
+            print("Test interrupted by user.")
+        finally:
+            monitor.stop_capture()
+            print("Video utils testing finished.")
+
+# The `if __name__ == '__main__':` block ensures that `main_test()` is called only when the script is executed directly,
+# not when it's imported as a module. This is a standard Python practice.
+# By the time the `if __name__ == '__main__':` block is executed,
+# the class definition has already been processed and the class name is in the module global scope.
+# The original error (NameErro) often occurs in more complex scenarios like circular imports
+# or if the runnable block tries to access the class in a way that depends on import-at time side-effects
+# that aren't complete when the `if __name__ == '__main__':` check happens.
+# Wrapping it in a function is a good practice for cleanliness and to avoid potential scoping issues,
+# especially with tools like Uvicorn's reloader that might import modules in specific ways.
+if __name__ == '__main__':
+    main_test()
diff --git a/in_progress/streaming-test2/requirements.txt b/in_progress/streaming-test2/requirements.txt
new file mode 100644
index 000000000..5aad16aed
--- /dev/null
+++ b/in_progress/streaming-test2/requirements.txt
@@ -0,0 +1,13 @@
+fastapi
+uvicorn[standard]
+google-cloud-aiplatform
+python-dotenv
+google-generativeai
+google-cloud-texttospeech
+google-cloud-speech
+opencv-python
+numpy
+# Add any other specific versions or packages if known e.g. google-adk
+google-adk==1.0.0
+certifi # Often needed for SSL with ADK
+deprecated
diff --git a/in_progress/streaming-test2/tests/__init__.py b/in_progress/streaming-test2/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test2/tests/test_agent.py b/in_progress/streaming-test2/tests/test_agent.py
new file mode 100644
index 000000000..6e4f06e89
--- /dev/null
+++ b/in_progress/streaming-test2/tests/test_agent.py
@@ -0,0 +1,156 @@
+import unittest
+from unittest.mock import patch, MagicMock, PropertyMock
+import sys
+import os
+import time # for time.time() in agent if __name__ == '__main__'
+
+# --- Global Mocking for vertexai (BEFORE app.agent is imported) ---
+os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'
+os.environ['GOOGLE_CLOUD_PROJECT'] = 'test-project'
+os.environ['GOOGLE_CLOUD_LOCATION'] = 'test-location'
+
+mock_vertexai_module = MagicMock(name="vertexai_mock")
+mock_generative_models_module = MagicMock(name="generative_models_mock")
+mock_generative_model_class = MagicMock(name="GenerativeModelClassMock")
+mock_model_instance = MagicMock(name="GenerativeModelInstanceMock")
+mock_chat_instance = MagicMock(name="ChatInstanceMock")
+
+# --- Part Mocking ---
+mock_part_class = MagicMock(name="PartMock")
+# Configure from_text and from_data to return mocks that have a to_dict method
+mock_text_part_instance = MagicMock(name="TextPartInstance")
+mock_text_part_instance.to_dict.return_value = {"text": "mocked text"} # Example to_dict content
+mock_data_part_instance = MagicMock(name="DataPartInstance")
+mock_data_part_instance.to_dict.return_value = {"inline_data": {"mime_type": "image/jpeg", "data": "mocked_data"}}
+
+mock_part_class.from_text.return_value = mock_text_part_instance
+mock_part_class.from_data.return_value = mock_data_part_instance
+# --- End Part Mocking ---
+
+mock_vertexai_module.init = MagicMock(name="vertexai_init_mock")
+mock_vertexai_module.generative_models = mock_generative_models_module
+mock_generative_models_module.GenerativeModel = mock_generative_model_class
+mock_generative_model_class.return_value = mock_model_instance
+mock_generative_models_module.Part = mock_part_class
+mock_model_instance.start_chat.return_value = mock_chat_instance
+
+sys.modules['vertexai'] = mock_vertexai_module
+sys.modules['vertexai.generative_models'] = mock_generative_models_module
+# --- End Global Mocking ---
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from app.agent import MultimodalAgent
+
+@patch('app.agent.VideoMonitor')
+@patch('app.agent.record_audio')
+@patch('app.agent.transcribe_audio_bytes')
+@patch('app.agent.synthesize_text_to_audio_bytes')
+@patch('app.agent.play_audio_bytes')
+@patch('app.agent.load_dotenv')
+class TestMultimodalAgent(unittest.TestCase):
+
+    def setUp(self):
+        mock_vertexai_module.init.reset_mock()
+        mock_generative_model_class.reset_mock()
+        mock_generative_model_class.return_value = mock_model_instance
+        mock_model_instance.start_chat.reset_mock()
+        mock_model_instance.start_chat.return_value = mock_chat_instance
+        mock_chat_instance.reset_mock()
+        mock_chat_instance.send_message.reset_mock()
+
+        mock_part_class.from_data.reset_mock()
+        mock_part_class.from_text.reset_mock()
+        # Ensure return values are reassigned for each test if they could be altered
+        mock_part_class.from_text.return_value = mock_text_part_instance
+        mock_part_class.from_data.return_value = mock_data_part_instance
+        # Reset to_dict mocks if necessary (though typically defined once)
+        mock_text_part_instance.to_dict.reset_mock(return_value=MagicMock()) # Avoid state leakage
+        mock_text_part_instance.to_dict.return_value = {"text": "mocked text"}
+        mock_data_part_instance.to_dict.reset_mock(return_value=MagicMock())
+        mock_data_part_instance.to_dict.return_value = {"inline_data": {"mime_type": "image/jpeg", "data": "mocked_data"}}
+
+
+    def test_agent_initialization(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        agent = MultimodalAgent(project_id="test-proj", location="test-loc", model_name="gemini-test")
+        mock_vertexai_module.init.assert_called_once_with(project="test-proj", location="test-loc")
+        mock_generative_model_class.assert_called_once_with("gemini-test")
+        MockVideoMonitor.assert_called_once_with(camera_index=0, fps_limit=1)
+        self.assertIsNotNone(agent.model)
+
+    def test_start_chat(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = None
+        mock_video_monitor_instance.start_capture.return_value = True
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.start_chat()
+        mock_model_instance.start_chat.assert_called_once_with(response_validation=False)
+        self.assertEqual(agent.chat, mock_chat_instance)
+        mock_video_monitor_instance.start_capture.assert_called_once()
+
+    def test_stop_chat(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = True
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.stop_chat()
+        mock_video_monitor_instance.stop_capture.assert_called_once()
+        self.assertIsNone(agent.chat)
+
+    def test_send_text_message_success(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_chat_instance.send_message.return_value = MagicMock(text="Model reply")
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        reply = agent.send_text_message("Hello agent")
+        self.assertEqual(reply, "Model reply")
+        mock_chat_instance.send_message.assert_called_with("Hello agent")
+
+    def test_send_message_with_image(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_chat_instance.send_message.return_value = MagicMock(text="Understood image")
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        # Pass the actual mock instance for image_parts
+        reply = agent.send_message(text_prompt="Describe this", image_parts=[mock_data_part_instance])
+        self.assertEqual(reply, "Understood image")
+        sent_parts_list = mock_chat_instance.send_message.call_args[0][0]
+        self.assertIn(mock_text_part_instance, sent_parts_list)
+        self.assertIn(mock_data_part_instance, sent_parts_list)
+
+    def test_check_for_video_changes_and_comment_detected(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = MagicMock()
+        mock_video_monitor_instance.cap.isOpened.return_value = True
+        mock_video_monitor_instance.process_frame_for_changes.return_value = (True, "Motion detected", b"jpeg_bytes")
+        mock_chat_instance.send_message.return_value = MagicMock(text="I see motion!")
+        mock_event_queue = MagicMock()
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.check_for_video_changes_and_comment(event_queue=mock_event_queue)
+        mock_video_monitor_instance.process_frame_for_changes.assert_called_once()
+        mock_part_class.from_data.assert_called_with(b"jpeg_bytes", mime_type="image/jpeg")
+        sent_parts_list = mock_chat_instance.send_message.call_args[0][0]
+        self.assertIn(mock_text_part_instance, sent_parts_list)
+        self.assertIn(mock_data_part_instance, sent_parts_list)
+        mock_event_queue.put.assert_called_once_with(
+            {"type": "video_change_comment", "comment": "I see motion!"}
+        )
+
+    def test_handle_voice_interaction(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        MockRecord.return_value = b"recorded_audio_bytes"
+        MockTranscribe.return_value = "user speech text"
+        mock_chat_instance.send_message.return_value = MagicMock(text="model speech text")
+        MockSynth.return_value = b"synthesized_audio_bytes"
+        mock_event_queue = MagicMock()
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.handle_voice_interaction(record_duration_seconds=3, event_queue=mock_event_queue)
+        MockRecord.assert_called_once_with(duration_seconds=3)
+        MockTranscribe.assert_called_once_with(b"recorded_audio_bytes")
+        mock_chat_instance.send_message.assert_called_with("user speech text")
+        MockSynth.assert_called_once_with("model speech text")
+        MockPlayAudio.assert_called_once_with(b"synthesized_audio_bytes", output_format="mp3")
+        self.assertIn(unittest.mock.call({"type": "transcription", "text": "user speech text"}), mock_event_queue.put.call_args_list)
+        self.assertIn(unittest.mock.call({"type": "model_response_audio_text", "text": "model speech text"}), mock_event_queue.put.call_args_list)
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test2/tests/test_audio_utils.py b/in_progress/streaming-test2/tests/test_audio_utils.py
new file mode 100644
index 000000000..091dec45c
--- /dev/null
+++ b/in_progress/streaming-test2/tests/test_audio_utils.py
@@ -0,0 +1,153 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import numpy as np
+from scipy.io.wavfile import write as write_wav
+import io
+import sys
+import os
+
+# Mock sounddevice is done globally in run_tests.py.
+# We get a reference to that global mock here.
+# This ensures that app.audio_utils, when it 'import sounddevice as sd', gets the global mock.
+mock_sd_module = sys.modules.get('sounddevice', MagicMock(name="FallbackMockSoundDeviceModule"))
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from app.audio_utils import record_audio, transcribe_audio_bytes, synthesize_text_to_audio_bytes, play_audio_bytes
+from app.config import AUDIO_SAMPLE_RATE
+
+class TestAudioUtils(unittest.TestCase):
+
+    def setUp(self):
+        # Reset the attributes of the global mock_sd_module for each test
+        # This is important because mock_sd_module is shared state modified by run_tests.py
+        mock_sd_module.rec = MagicMock(name="sd_rec_mock_from_setUp")
+        mock_sd_module.wait = MagicMock(name="sd_wait_mock_from_setUp")
+        mock_sd_module.play = MagicMock(name="sd_play_mock_from_setUp")
+        # Ensure other methods that might be called are also reset if necessary
+        mock_sd_module.reset_mock() # Resets call counts etc. on the main mock
+                                    # and also replaces attributes like .rec with new MagicMocks.
+                                    # So, re-assign them if specific naming is desired or if they need to be shared.
+        # Re-establish well-known mocks on the global mock after reset_mock()
+        # These will be the mocks that app.audio_utils.sd.* actually calls.
+        # The 'global' keyword here was causing a SyntaxError and is not needed
+        # as mock_sd_module is already in the module's global scope.
+        mock_sd_module.rec = MagicMock(name="sd.rec")
+        mock_sd_module.wait = MagicMock(name="sd.wait")
+        mock_sd_module.play = MagicMock(name="sd.play")
+
+
+    def generate_dummy_wav_bytes(self, duration_seconds=1, sample_rate=AUDIO_SAMPLE_RATE, channels=1):
+        num_samples = int(duration_seconds * sample_rate)
+        frequency = 440
+        t = np.linspace(0, duration_seconds, num_samples, False)
+        audio_data = (0.5 * np.sin(2 * np.pi * frequency * t) * 32767).astype(np.int16)
+        if channels == 2:
+            audio_data = np.column_stack((audio_data, audio_data))
+        byte_io = io.BytesIO()
+        write_wav(byte_io, sample_rate, audio_data)
+        return byte_io.getvalue()
+
+    def test_record_audio_success(self):
+        duration = 1
+        sample_rate = 16000
+        channels = 1
+        expected_samples = duration * sample_rate
+        # This is a REAL numpy array that scipy.io.wavfile.write expects.
+        dummy_numpy_array = np.random.randint(-32768, 32767, size=(expected_samples, channels), dtype=np.int16)
+
+        # Configure the mock that app.audio_utils.sd.rec will resolve to
+        mock_sd_module.rec.return_value = dummy_numpy_array
+
+        audio_bytes = record_audio(duration_seconds=duration, sample_rate=sample_rate, channels=channels)
+
+        mock_sd_module.rec.assert_called_once_with(int(duration * sample_rate), samplerate=sample_rate, channels=channels, dtype='int16')
+        mock_sd_module.wait.assert_called_once()
+        self.assertIsNotNone(audio_bytes)
+        self.assertIsInstance(audio_bytes, bytes)
+
+    def test_record_audio_failure(self):
+        # Configure the mock that app.audio_utils.sd.rec will resolve to
+        mock_sd_module.rec.side_effect = Exception("Recording failed")
+
+        audio_bytes = record_audio(duration_seconds=1)
+
+        self.assertIsNone(audio_bytes)
+        mock_sd_module.rec.assert_called_once() # Should still be called once
+
+
+    @patch('app.audio_utils.speech.SpeechClient')
+    def test_transcribe_audio_bytes_success(self, MockSpeechClient):
+        mock_client_instance = MockSpeechClient.return_value
+        mock_recognize_response = MagicMock()
+        mock_alternative = MagicMock()
+        mock_alternative.transcript = "hello world"
+        mock_result = MagicMock()
+        mock_result.alternatives = [mock_alternative]
+        mock_recognize_response.results = [mock_result]
+        mock_client_instance.recognize.return_value = mock_recognize_response
+
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio, sample_rate=AUDIO_SAMPLE_RATE)
+        self.assertEqual(transcript, "hello world")
+
+    @patch('app.audio_utils.speech.SpeechClient', side_effect=Exception("GCS API Error"))
+    def test_transcribe_audio_bytes_api_failure(self, MockSpeechClient):
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio)
+        self.assertIsNone(transcript)
+
+    def test_transcribe_audio_bytes_no_input(self):
+        transcript = transcribe_audio_bytes(None)
+        self.assertIsNone(transcript)
+
+    @patch('app.audio_utils.speech.SpeechClient')
+    def test_transcribe_audio_bytes_no_results(self, MockSpeechClient):
+        mock_client_instance = MockSpeechClient.return_value
+        mock_recognize_response = MagicMock()
+        mock_recognize_response.results = []
+        mock_client_instance.recognize.return_value = mock_recognize_response
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio, sample_rate=AUDIO_SAMPLE_RATE)
+        self.assertEqual(transcript, "")
+
+    @patch('app.audio_utils.texttospeech.TextToSpeechClient')
+    def test_synthesize_text_to_audio_bytes_success(self, MockTextToSpeechClient):
+        mock_client_instance = MockTextToSpeechClient.return_value
+        mock_synthesize_response = MagicMock()
+        mock_synthesize_response.audio_content = b"dummy_audio_content"
+        mock_client_instance.synthesize_speech.return_value = mock_synthesize_response
+        audio_bytes = synthesize_text_to_audio_bytes("hello")
+        self.assertEqual(audio_bytes, b"dummy_audio_content")
+
+    @patch('app.audio_utils.texttospeech.TextToSpeechClient', side_effect=Exception("TTS API Error"))
+    def test_synthesize_text_to_audio_bytes_api_failure(self, MockTextToSpeechClient):
+        audio_bytes = synthesize_text_to_audio_bytes("test text")
+        self.assertIsNone(audio_bytes)
+
+    def test_synthesize_text_to_audio_bytes_no_input(self):
+        audio_bytes = synthesize_text_to_audio_bytes("")
+        self.assertIsNone(audio_bytes)
+
+    @patch('scipy.io.wavfile.read')
+    def test_play_audio_bytes_wav(self, mock_scipy_read_wav):
+        dummy_wav_data = np.array([1, 2, 3], dtype=np.int16)
+        dummy_sample_rate = AUDIO_SAMPLE_RATE
+        mock_scipy_read_wav.return_value = (dummy_sample_rate, dummy_wav_data)
+
+        wav_bytes = self.generate_dummy_wav_bytes()
+        play_audio_bytes(wav_bytes, output_format="wav", sample_rate=dummy_sample_rate)
+
+        mock_scipy_read_wav.assert_called_once()
+        self.assertIsInstance(mock_scipy_read_wav.call_args[0][0], io.BytesIO)
+        mock_sd_module.play.assert_called_once_with(dummy_wav_data, samplerate=dummy_sample_rate)
+        mock_sd_module.wait.assert_called_once()
+
+    @patch('app.audio_utils.time.sleep')
+    def test_play_audio_bytes_mp3_placeholder(self, mock_time_sleep):
+        mp3_bytes = b"dummy_mp3_data"
+        play_audio_bytes(mp3_bytes, output_format="mp3")
+        mock_time_sleep.assert_called()
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test2/tests/test_video_utils.py b/in_progress/streaming-test2/tests/test_video_utils.py
new file mode 100644
index 000000000..803afe174
--- /dev/null
+++ b/in_progress/streaming-test2/tests/test_video_utils.py
@@ -0,0 +1,159 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import numpy as np
+import cv2 # OpenCV is a direct dependency here
+import time # Import the time module
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from app.video_utils import VideoMonitor
+from app.config import VIDEO_FPS
+
+class TestVideoUtils(unittest.TestCase):
+
+    def create_dummy_frame(self, width=640, height=480, channels=3, color=(0,0,0)):
+        frame = np.full((height, width, channels), color, dtype=np.uint8)
+        return frame
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_start_capture_success(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+
+        monitor = VideoMonitor(camera_index=0)
+        self.assertTrue(monitor.start_capture())
+        MockVideoCapture.assert_called_once_with(0)
+        self.assertIsNotNone(monitor.cap)
+        self.assertTrue(monitor.cap.isOpened())
+        monitor.stop_capture() # Clean up
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_start_capture_failure(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = False # Simulate camera not opening
+
+        monitor = VideoMonitor(camera_index=0)
+        self.assertFalse(monitor.start_capture())
+        self.assertIsNone(monitor.cap)
+
+    def test_videomonitor_get_frame_no_capture(self):
+        monitor = VideoMonitor()
+        # No call to start_capture
+        self.assertIsNone(monitor.get_frame())
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_get_frame_success(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+        dummy_frame_data = self.create_dummy_frame()
+        mock_cap_instance.read.return_value = (True, dummy_frame_data)
+
+        monitor = VideoMonitor()
+        monitor.start_capture() # Sets up monitor.cap
+        frame = monitor.get_frame()
+
+        self.assertIsNotNone(frame)
+        np.testing.assert_array_equal(frame, dummy_frame_data)
+        mock_cap_instance.read.assert_called_once()
+        monitor.stop_capture()
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_get_frame_read_fail(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+        mock_cap_instance.read.return_value = (False, None) # Simulate read failure
+
+        monitor = VideoMonitor()
+        monitor.start_capture()
+        frame = monitor.get_frame()
+
+        self.assertIsNone(frame)
+        monitor.stop_capture()
+
+    def test_detect_significant_change_no_frame(self):
+        monitor = VideoMonitor()
+        changed, desc, _ = monitor.detect_significant_change(None)
+        self.assertFalse(changed)
+        self.assertEqual(desc, "No frame")
+
+    @patch('cv2.imencode', return_value=(True, np.array([1,2,3], dtype=np.uint8))) # Corrected mock target and return value
+    def test_detect_significant_change_with_motion(self, mock_imencode):
+        monitor = VideoMonitor()
+        # monitor.start_capture() # Initializes bg_subtractor - not needed if only testing detect_significant_change directly with a frame
+        # Let's ensure bg_subtractor is available for this test method if it's not through start_capture
+        monitor.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+        frame1 = self.create_dummy_frame(color=(10,10,10)) # Initial background
+        monitor.bg_subtractor.apply(frame1) # establish baseline
+
+        frame2 = self.create_dummy_frame(color=(10,10,10))
+        # Simulate a moving object by drawing a different color rectangle
+        cv2.rectangle(frame2, (100,100), (200,200), (200,200,200), -1)
+
+        changed, desc, frame_bytes = monitor.detect_significant_change(frame2, min_contour_area=100)
+
+        self.assertTrue(changed, f"Change should have been detected. Description: {desc}")
+        self.assertEqual(desc, "Significant motion detected in the scene.")
+        self.assertIsNotNone(frame_bytes)
+        mock_imencode.assert_called_once()
+        # monitor.stop_capture() # Only if start_capture was called
+
+    def test_detect_significant_change_no_motion(self):
+        monitor = VideoMonitor()
+        # monitor.start_capture() # Initializes bg_subtractor
+        monitor.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+        frame1 = self.create_dummy_frame(color=(10,10,10))
+        monitor.bg_subtractor.apply(frame1) # establish baseline
+
+        # Process same frame again or very similar one
+        frame2 = self.create_dummy_frame(color=(10,10,10))
+        # Make a tiny change that should be filtered out by morphology or area
+        cv2.rectangle(frame2, (0,0), (2,2), (20,20,20), -1)
+
+
+        changed, desc, _ = monitor.detect_significant_change(frame2, min_contour_area=500)
+
+        self.assertFalse(changed, f"No significant change should be detected. Description: {desc}")
+        self.assertEqual(desc, "No significant change detected.")
+        # monitor.stop_capture()
+
+    @patch('app.video_utils.VideoMonitor.get_frame')
+    @patch('app.video_utils.VideoMonitor.detect_significant_change')
+    def test_process_frame_for_changes_fps_limit(self, mock_detect_change, mock_get_frame):
+        monitor = VideoMonitor(fps_limit=10) # Limit to 10 FPS
+        monitor.last_processed_time = time.time() # Pretend we just processed a frame
+
+        # Call immediately, should be skipped due to FPS limit
+        changed, desc, _ = monitor.process_frame_for_changes()
+
+        self.assertFalse(changed)
+        self.assertEqual(desc, "FPS limit, frame skipped")
+        mock_get_frame.assert_not_called()
+        mock_detect_change.assert_not_called()
+
+    @patch('app.video_utils.VideoMonitor.get_frame')
+    @patch('app.video_utils.VideoMonitor.detect_significant_change')
+    def test_process_frame_for_changes_proceeds(self, mock_detect_change, mock_get_frame):
+        monitor = VideoMonitor(fps_limit=1)
+        monitor.last_processed_time = time.time() - 2 # Pretend last frame was 2s ago
+
+        dummy_frame = self.create_dummy_frame()
+        mock_get_frame.return_value = dummy_frame
+        mock_detect_change.return_value = (True, "Test change", b"frame_bytes")
+
+        changed, desc, frame_bytes_out = monitor.process_frame_for_changes()
+
+        self.assertTrue(changed)
+        self.assertEqual(desc, "Test change")
+        self.assertEqual(frame_bytes_out, b"frame_bytes")
+        mock_get_frame.assert_called_once()
+        mock_detect_change.assert_called_once_with(dummy_frame)
+
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test3/.gitignore b/in_progress/streaming-test3/.gitignore
new file mode 100644
index 000000000..f20068ff7
--- /dev/null
+++ b/in_progress/streaming-test3/.gitignore
@@ -0,0 +1,6 @@
+.venv/
+__pycache__/
+*.pyc
+.env
+instance/
+node_modules/
diff --git a/in_progress/streaming-test3/README.md b/in_progress/streaming-test3/README.md
new file mode 100644
index 000000000..c51b8f58e
--- /dev/null
+++ b/in_progress/streaming-test3/README.md
@@ -0,0 +1,116 @@
+# Streaming Test 2 - Multimodal Streaming Agents
+
+This project (`streaming-test2`) demonstrates a multimodal agent system using Python, FastAPI, and Google Agent Development Kit (ADK) for live bi-directional audio, and a separate camera monitoring agent using Gemini.
+
+## Features
+
+- __CameraAgent__: Monitors a live video feed, detects significant visual changes, sends observations to a Gemini model ("gemini-2.0-flash"), and logs interpretations to `/logs/camera_log.json`.
+- __AudioAgent__: Handles live bi-directional audio interaction using Google ADK and the "gemini-2.0-flash-live-preview-04-09" model. Logs transcriptions to `/logs/audio_log.json`.
+- __Agent Communication__: The AudioAgent reads from the CameraAgent's log to gain contextual awareness for its responses.
+- __FastAPI Backend__: Serves the application, handling SSE (Server-Sent Events) for audio streaming, and provides HTTP endpoints for client interaction.
+- __Web Interface__: A basic HTML/CSS/JS frontend to interact with the agents and view logs.
+
+## Prerequisites
+
+- Python 3.8 + (3.9/3.10/3.11 recommended)
+- `pip` for installing packages.
+- Access to a camera (for the CameraAgent).
+-  A microphone (for the AudioAgent).
+- A Google Cloud Project with Vertex AI enabled.
+
+
+## Setup Instructions
+
+1. *__Create a Virtual Environment (Recommended)__*
+   ```sh
+   python -m venv .venv
+   ```
+   Activate the virtual environment:
+   - On macOS/Linux:
+     ```sh
+     source .venv/bin/activate
+     ```
+   - On Windows:
+     ```sh
+     .venv\Scripts\activate
+     ```
+
+2. *__Install Dependencies__*
+   With your virtual environment active, navigate to the `streaming-test2` directory and run the following command to install the required packages:
+   ```sh
+   pip install -r requirements.txt
+   ```
+   (Ensure you are in the `streaming-test2` directory when running this command, as requirements.txt is located there.)
+
+3. *__Create the `.env` File__*
+   Create a file named `.env` within the `streaming-test2` directory.
+   Edit this `.env` file and add the following content, replacing the placeholders with your actual Google Cloud project details:
+   ```        GOOGLE_GENAI_USE_VERTEXAI=True
+        GOOGLE_CLOUD_PROJECT="analog-delight-459210-v0"
+        GOOGLE_CLOUD_LOCATION="us-central1"
+   ```
+   Note: The Python scripts in the `app/` directory are configured to load this particular `.env` file from the `streaming-test2` directory.
+
+4. *__Run the Application__*
+   Navigate into the `streaming-test2` directory in your terminal.
+   ```sh
+   cd path/to/streaming-test2
+   ```
+   Then, run the following command to start the FastAPI application using Uvicorn:
+   ```sh
+   python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
+   ```
+   - `app.main:app` tells Uvicorn to look for the `app` folder, the `main` module (main.py), and the `app` object within that module. This assumes you are running from the `streaming-test2` directory.
+   - `--reload` enables automatic reloading when code changes are detected.
+   - `--host 0.0.0.0` makes the server accessible on your network.
+   - `--port 8000` sets the port the server is running on.
+
+Once the server is running, open your web browser and navigate to `http://localhost:8000`.
+
+
+## Usage
+
+1. The UI will display statuses for the Camera Agent and the Audio Agent.
+2. The Camera Agent will automatically start monitoring.
+   - Its rager will appear in the 'Camera Log' section.
+3. To interact with the AudioAgent:
+   - Click the `Start Audio Interaction` button.
+   - Your server permission to use the microphone.
+   - Speak, or type a message in the input field and click `Send Text`.
+   - The AudioAgent's response (audio and text transcription) will appear.
+   - Click `Stop Audio Interaction` to end the audio session.
+- Audio transcriptions will appear in the 'Audio Log' section.
+
+## Project Structure
+
+```
+/streaming-test2
+    .env                                     # (You create this) Google Cloud config
+    README.md                                # This file
+    requirements.txt                         # Python dependencies
+    app/
+        __init__.py                          # Makes 'app' a Python package
+        audio_agent.py                       # Logic for the AudioAgent
+        camera_agent.py                      # Logic for the CameraAgent
+        config.py                            # Configuration (e.g., VIDEO_FPS)
+        main.py                              # FastAPI application
+
+        static/                              # Static files for the web UI
+            css/
+                style.css
+            js/
+                app.js
+                audio-player.js
+                audio-recorder.js
+                pcm-player-processor.js
+                pcm-recorder-processor.js
+            index.html
+        video_utils.py                       # Utilities for video processing
+    logs/
+        audio_log.json                       # Log of audio transcriptions
+        camera_log.json                      # Log of camera observations
+```
+
+## Notes
+- The JavaScript audio processor files (`audio-player.js`, `audio-recorder.js`, `pcm-player-processor.js`, `pcm-recorder-processor.js`) are adapted from Google ADK examples and are crucial for the audio functionality.
+-  The application is designed for development and may require further configuration and security hardening for production use.
diff --git a/in_progress/streaming-test3/app/__init__.py b/in_progress/streaming-test3/app/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test3/app/audio_agent.py b/in_progress/streaming-test3/app/audio_agent.py
new file mode 100644
index 000000000..36a736fd4
--- /dev/null
+++ b/in_progress/streaming-test3/app/audio_agent.py
@@ -0,0 +1,43 @@
+# audio_agent.py
+
+from google.adk.agents.llm_agent import LlmAgent
+import json
+from pathlib import Path
+
+LOG_FILE = Path("logs/audio_log.json")
+
+class AudioAgent(LlmAgent):
+    """
+    An agent that receives a live audio stream and transcribes it to text
+    using a specialized Gemini streaming model.
+    """
+    # **BUG FIX:** Changed the agent name to be a valid Python identifier.
+    name: str = "Audio_Agent"
+    model: str = "gemini-2.0-flash-live-preview-04-09"
+    prompt: str = "You are a live transcriptionist. Transcribe the user's speech accurately."
+
+    def on_agent_response(
+        self,
+        response: str,
+        *,
+        is_tool_code: bool = False,
+        is_final: bool = False,
+        turn_id: str,
+        session_id: str,
+        run_config_id: str,
+    ) -> None:
+        super().on_agent_response(
+            response,
+            is_tool_code=is_tool_code,
+            is_final=is_final,
+            turn_id=turn_id,
+            session_id=session_id,
+            run_config_id=run_config_id,
+        )
+        if is_final and response:
+            try:
+                with open(LOG_FILE, "a") as f:
+                    json.dump({"transcription": response}, f)
+                    f.write("\n")
+            except Exception as e:
+                print(f"Error writing to audio log file: {e}")
\ No newline at end of file
diff --git a/in_progress/streaming-test3/app/camera_agent.py b/in_progress/streaming-test3/app/camera_agent.py
new file mode 100644
index 000000000..3c6b0ea91
--- /dev/null
+++ b/in_progress/streaming-test3/app/camera_agent.py
@@ -0,0 +1,43 @@
+# camera_agent.py
+
+from google.adk.agents.llm_agent import LlmAgent
+import json
+from pathlib import Path
+
+LOG_FILE = Path("logs/video_log.json")
+
+class CameraAgent(LlmAgent):
+    """
+    An agent that receives video frames, interprets them using a Gemini model,
+    and logs the interpretation.
+    """
+    # **BUG FIX:** Changed the agent name to be a valid Python identifier.
+    name: str = "Camera_Agent"
+    model: str = "gemini-2.0-flash"
+    prompt: str = "You are a helpful assistant. Describe what you see in this image in one short sentence."
+
+    def on_agent_response(
+        self,
+        response: str,
+        *,
+        is_tool_code: bool = False,
+        is_final: bool = False,
+        turn_id: str,
+        session_id: str,
+        run_config_id: str,
+    ) -> None:
+        super().on_agent_response(
+            response,
+            is_tool_code=is_tool_code,
+            is_final=is_final,
+            turn_id=turn_id,
+            session_id=session_id,
+            run_config_id=run_config_id,
+        )
+        if is_final:
+            try:
+                with open(LOG_FILE, "a") as f:
+                    json.dump({"interpretation": response}, f)
+                    f.write("\n")
+            except Exception as e:
+                print(f"Error writing to video log file: {e}")
\ No newline at end of file
diff --git a/in_progress/streaming-test3/app/config.py b/in_progress/streaming-test3/app/config.py
new file mode 100644
index 000000000..7f15592d9
--- /dev/null
+++ b/in_progress/streaming-test3/app/config.py
@@ -0,0 +1,23 @@
+# Configuration settings for the application
+
+# Vertex AI / Gemini Model Configuration
+GOOGLE_CLOUD_PROJECT = None # Will be loaded from .env
+GOOGLE_CLOUD_LOCATION = None # Will be loaded from .env
+MODEL_NAME = "gemini-2.0-flash" # Updated based on user feedback for video functionality
+
+# Audio settings
+AUDIO_SAMPLE_RATE = 16000
+AUDIO_CHANNELS = 1
+# Add other audio settings as needed (e.g., chunk size for streaming)
+
+MODEL_NAME = "gemini-2.0-flash"
+
+# Video settings
+VIDEO_FPS = 10 # Default FPS for video processing by VideoMonitor
+# Add other video settings as needed
+
+# Web server settings
+WEB_HOST = "0.0.0.0"
+WEB_PORT = 8080
+
+# You can add more configuration variables here as the project grows.
diff --git a/in_progress/streaming-test3/app/main.py b/in_progress/streaming-test3/app/main.py
new file mode 100644
index 000000000..2295fd11e
--- /dev/null
+++ b/in_progress/streaming-test3/app/main.py
@@ -0,0 +1,99 @@
+# main.py
+
+import asyncio
+import logging
+from pathlib import Path
+
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, StreamingResponse
+
+# This is the LlmAgent that can stream
+from google.adk.agents.llm_agent import LlmAgent
+from google.adk.agents.run_config import RunConfig
+
+from .audio_agent import AudioAgent
+from .camera_agent import CameraAgent
+from .web_interface import generate_frames, get_camera_instance
+
+# --- Basic Setup ---
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# --- API and WebSocket Routes ---
+@app.get("/video_feed", include_in_schema=False)
+def video_feed():
+    camera = get_camera_instance()
+    return StreamingResponse(
+        generate_frames(camera),
+        media_type="multipart/x-mixed-replace; boundary=frame",
+    )
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+    logger.info("WebSocket connection established.")
+
+    # Create separate agent instances
+    audio_agent = AudioAgent()
+    camera_agent = CameraAgent()
+
+    # Define the single callback for both agents
+    async def response_handler(response: str, agent_name: str):
+        if response:
+            await websocket.send_json({"agent": agent_name, "response": response})
+
+    # Create RunConfigs that pass the agent's name to the callback
+    audio_run_config = RunConfig(
+        response_handler=lambda res: response_handler(res, "Audio_Agent")
+    )
+    camera_run_config = RunConfig(
+        response_handler=lambda res: response_handler(res, "Camera_Agent")
+    )
+
+    try:
+        # Start both agents. The `run_live` method in the working example
+        # is a blocking call that we run in the background.
+        audio_task = asyncio.create_task(audio_agent.run_live(audio_run_config))
+        camera_task = asyncio.create_task(camera_agent.run_live(camera_run_config))
+
+        logger.info("Agent live runs initiated in background tasks.")
+
+        # Loop to process incoming messages
+        while True:
+            data = await websocket.receive_json()
+            agent_name = data["agent"]
+            payload = data["payload"]
+
+            if agent_name == "Audio_Agent":
+                await audio_agent.send_live_request(payload, audio_run_config)
+            elif agent_name == "Camera_Agent":
+                await camera_agent.send_live_request(payload, camera_run_config)
+
+    except WebSocketDisconnect:
+        logger.info("Client disconnected.")
+    except Exception as e:
+        logger.error(f"EXCEPTION in websocket_endpoint: {e}", exc_info=True)
+    finally:
+        logger.info("Cleaning up resources...")
+        # Cancel the background agent tasks
+        if 'audio_task' in locals() and not audio_task.done():
+            audio_task.cancel()
+        if 'camera_task' in locals() and not camera_task.done():
+            camera_task.cancel()
+        logger.info("Agent tasks cancelled.")
+
+
+# --- Static File and Root HTML Routes ---
+static_dir = Path(__file__).parent / "static"
+app.mount(
+    "/static",
+    StaticFiles(directory=static_dir),
+    name="static",
+)
+
+@app.get("/", include_in_schema=False)
+async def root() -> FileResponse:
+    return FileResponse(static_dir / "index.html")
\ No newline at end of file
diff --git a/in_progress/streaming-test3/app/static/css/style.css b/in_progress/streaming-test3/app/static/css/style.css
new file mode 100644
index 000000000..77abd0d13
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/css/style.css
@@ -0,0 +1,11 @@
+body { font-family: sans-serif; margin: 20px; background-color: #f4f4f4; color: #333; }
+.container { display: flex; gap: 20px; margin-bottom: 20px; }
+.agent-section { background-color: #fff; padding: 15px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); flex: 1; }
+h1, h2, h3 { color: #555; }
+pre { background-color: #eee; padding: 10px; border-radius: 4px; white-space: pre-wrap; word-break: break-all; max-height: 300px; overflow-y: auto; font-family: monospace; }
+button { padding: 10px 15px; margin: 5px; border: none; border-radius: 4px; background-color: #007bff; color: white; cursor: pointer; }
+button:disabled { background-color: #ccc; }
+button:hover:not(:disabled) { background-color: #0056b3; }
+input[type="text"] { padding: 10px; margin-right: 10px; border: 1px solid #ddd; border-radius: 4px; width: calc(100% - 120px); }
+.input-area { margin-top: 20px; display: flex; }
+.agent-speech-bubble { margin-top: 10px; padding: 10px; background-color: #e9f5ff; border-radius: 5px; min-height: 20px; border: 1px solid #cce4ff; }
diff --git a/in_progress/streaming-test3/app/static/index.html b/in_progress/streaming-test3/app/static/index.html
new file mode 100644
index 000000000..e7a2fba59
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/index.html
@@ -0,0 +1,35 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Multimodal Agent Stream</title>
+    <link rel="stylesheet" href="/static/css/style.css">
+</head>
+<body>
+    <h1>Multimodal Streaming Agent</h1>
+    <div class="container">
+        <div class="controls">
+            <button id="startButton" disabled>Start Streaming</button>
+            <button id="stopButton" disabled>Stop Streaming</button>
+        </div>
+
+        <div class="main-content">
+            <div class="video-container">
+                <h2>Live Camera Feed</h2>
+                <img id="video" src="/video_feed" width="480" height="360" alt="Video Feed">
+                <canvas id="videoCanvas" width="480" height="360" style="display:none;"></canvas>
+            </div>
+            
+            <div id="response" class="response-container">
+                </div>
+        </div>
+    </div>
+
+    <script src="/static/js/pcm-recorder-processor.js"></script>
+    <script src="/static/js/audio-recorder.js"></script>
+    <script src="/static/js/pcm-player-processor.js"></script>
+    <script src="/static/js/audio-player.js"></script>
+    <script src="/static/js/app.js"></script>
+</body>
+</html>
\ No newline at end of file
diff --git a/in_progress/streaming-test3/app/static/js/app.js b/in_progress/streaming-test3/app/static/js/app.js
new file mode 100644
index 000000000..b1e654efb
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/js/app.js
@@ -0,0 +1,104 @@
+// app.js
+
+const socket = new WebSocket("ws://" + location.host + "/ws");
+
+const startButton = document.getElementById("startButton");
+const stopButton = document.getElementById("stopButton");
+const responseDiv = document.getElementById("response");
+
+const recorder = new AudioRecorder();
+const videoCanvas = document.getElementById('videoCanvas');
+const videoElement = document.getElementById('video');
+const context = videoCanvas.getContext('2d');
+
+let cameraInterval;
+
+// --- WebSocket Event Handlers ---
+
+socket.onopen = () => {
+    console.log("WebSocket connection opened.");
+    startButton.disabled = false;
+};
+
+socket.onmessage = (event) => {
+    const data = JSON.parse(event.data);
+    
+    // Use the agent name (now with underscores) to create a valid ID
+    const agentId = data.agent.replace(/_/g, '-');
+    let agentDiv = document.getElementById(`agent-${agentId}`);
+    
+    if (!agentDiv) {
+        agentDiv = document.createElement("div");
+        agentDiv.id = `agent-${agentId}`;
+        agentDiv.className = 'agent-response';
+        
+        const title = document.createElement('h3');
+        // Replace underscores with spaces for a nicer display name
+        title.textContent = `${data.agent.replace(/_/g, ' ')} Output:`;
+        agentDiv.appendChild(title);
+        
+        const contentDiv = document.createElement('div');
+        contentDiv.className = 'agent-content';
+        agentDiv.appendChild(contentDiv);
+        
+        responseDiv.appendChild(agentDiv);
+    }
+
+    const contentDiv = agentDiv.querySelector('.agent-content');
+    contentDiv.innerHTML += `<span>${data.response}</span>`;
+    
+    responseDiv.scrollTop = responseDiv.scrollHeight;
+};
+
+socket.onclose = () => {
+    console.log("WebSocket connection closed.");
+    startButton.disabled = true;
+    stopButton.disabled = true;
+};
+
+// --- Camera and Video Logic ---
+
+async function setupCamera() {
+    const stream = await navigator.mediaDevices.getUserMedia({ video: true, audio: false });
+    videoElement.srcObject = stream;
+
+    cameraInterval = setInterval(() => {
+        context.drawImage(videoElement, 0, 0, videoCanvas.width, videoCanvas.height);
+        const data = videoCanvas.toDataURL('image/jpeg');
+        
+        // **BUG FIX:** Use the new, valid agent name.
+        socket.send(JSON.stringify({
+            agent: "Camera_Agent",
+            payload: data
+        }));
+    }, 500); 
+}
+
+
+// --- Button Event Handlers ---
+
+startButton.onclick = async () => {
+    await recorder.start(
+        (audioData) => {
+            // **BUG FIX:** Use the new, valid agent name.
+            socket.send(JSON.stringify({
+                agent: "Audio_Agent",
+                payload: audioData
+            }));
+        }
+    );
+    await setupCamera();
+    
+    startButton.disabled = true;
+    stopButton.disabled = false;
+    console.log("Recording and camera started.");
+};
+
+stopButton.onclick = () => {
+    recorder.stop();
+    clearInterval(cameraInterval);
+    
+    startButton.disabled = false;
+    stopButton.disabled = true;
+    console.log("Recording and camera stopped.");
+};
\ No newline at end of file
diff --git a/in_progress/streaming-test3/app/static/js/audio-player.js b/in_progress/streaming-test3/app/static/js/audio-player.js
new file mode 100644
index 000000000..c2a142530
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+
+
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+
+    // 3. Create an AudioWorkletNode
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
diff --git a/in_progress/streaming-test3/app/static/js/audio-recorder.js b/in_progress/streaming-test3/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..82806e6dd
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
diff --git a/in_progress/streaming-test3/app/static/js/pcm-player-processor.js b/in_progress/streaming-test3/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..824a90b32
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/js/pcm-player-processor.js
@@ -0,0 +1,74 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` |128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex !== this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
diff --git a/in_progress/streaming-test3/app/static/js/pcm-recorder-processor.js b/in_progress/streaming-test3/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..b1c54bd31
--- /dev/null
+++ b/in_progress/streaming-test3/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
diff --git a/in_progress/streaming-test3/app/templates/index.html b/in_progress/streaming-test3/app/templates/index.html
new file mode 100644
index 000000000..56da3a2af
--- /dev/null
+++ b/in_progress/streaming-test3/app/templates/index.html
@@ -0,0 +1,191 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Multimodal Agent Interface</title>
+    <style>
+        body { font-family: sans-serif; margin: 20px; background-color: #f4f4f4; color: #333; }
+        .container { display: flex; flex-direction: column; align-items: center; }
+        .video-container { border: 1px solid #ccc; margin-bottom: 20px; min-width: 320px; min-height: 240px; background-color: #000;}
+        .chat-container { width: 80%; max-width: 600px; margin-bottom: 20px; }
+        .chat-box { height: 300px; border: 1px solid #ccc; overflow-y: scroll; padding: 10px; background-color: #fff; margin-bottom: 10px; }
+        .chat-message { margin-bottom: 8px; padding: 5px; border-radius: 4px; }
+        .user-message { background-color: #e1f5fe; text-align: right; }
+        .model-message { background-color: #f0f4c3; }
+        .controls { display: flex; justify-content: center; align-items: center; width: 80%; max-width: 600px; }
+        #chatInput { flex-grow: 1; padding: 8px; margin-right: 10px; border: 1px solid #ccc; border-radius: 4px;}
+        button { padding: 8px 15px; border: none; border-radius: 4px; cursor: pointer; background-color: #4CAF50; color: white; margin-left: 5px; } /* Added margin-left to button */
+        button:hover { background-color: #45a049; }
+        .status { margin-top: 10px; font-style: italic; color: #555; }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Live Multimodal Agent</h1>
+
+        <div class="video-container">
+            <img id="videoFeed" src="/video_feed" width="640" height="480" alt="Video Feed">
+        </div>
+
+        <div class="chat-container">
+            <div id="chatBox" class="chat-box">
+                <!-- Chat messages will appear here -->
+            </div>
+            <div class="controls">
+                <input type="text" id="chatInput" placeholder="Type your message...">
+                <button onclick="sendChatMessage()">Send</button>
+                <button onclick="sendVoiceMessage()" id="sendVoiceBtn">Send Voice (Record 5s)</button>
+            </div>
+            <div class="status" id="statusArea">Agent status will appear here.</div>
+        </div>
+         <button onclick="startAgent()">Start Agent</button>
+         <button onclick="stopAgent()">Stop Agent</button>
+    </div>
+
+    <script>
+        const chatBox = document.getElementById('chatBox');
+        const chatInput = document.getElementById('chatInput');
+        const statusArea = document.getElementById('statusArea');
+
+        function addMessage(text, sender) {
+            const messageDiv = document.createElement('div');
+            messageDiv.classList.add('chat-message', sender + '-message');
+            messageDiv.textContent = text;
+            chatBox.appendChild(messageDiv);
+            chatBox.scrollTop = chatBox.scrollHeight; // Auto-scroll
+        }
+
+        async function sendChatMessage() {
+            const messageText = chatInput.value;
+            if (!messageText.trim()) return;
+
+            addMessage(messageText, 'user');
+            chatInput.value = '';
+
+            try {
+                const response = await fetch('/send_chat_message', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ message: messageText })
+                });
+                const data = await response.json();
+                if (data.reply) {
+                    addMessage(data.reply, 'model');
+                } else if (data.error) {
+                    addMessage('Error: ' + data.error, 'model');
+                }
+            } catch (error) {
+                console.error('Error sending chat message:', error);
+                addMessage('Error connecting to agent.', 'model');
+            }
+        }
+
+        chatInput.addEventListener('keypress', function(event) {
+            if (event.key === 'Enter') {
+                sendChatMessage();
+            }
+        });
+
+        async function startAgent() {
+            statusArea.textContent = "Attempting to start agent...";
+            try {
+                const response = await fetch('/start_interaction');
+                const data = await response.json();
+                statusArea.textContent = data.status || data.error;
+                addMessage(data.status || data.error, 'model');
+                // Connect to SSE after agent is confirmed started
+                if (response.ok && data.status && data.status.includes("started")) {
+                    connectToAgentEvents();
+                }
+            } catch (error) {
+                 statusArea.textContent = "Error starting agent.";
+                 addMessage('Error starting agent.', 'model');
+            }
+        }
+
+        async function stopAgent() {
+            statusArea.textContent = "Attempting to stop agent...";
+            try {
+                const response = await fetch('/stop_interaction');
+                const data = await response.json();
+                statusArea.textContent = data.status || data.error;
+                addMessage(data.status || data.error, 'model');
+                if (eventSource) {
+                    eventSource.close();
+                    statusArea.textContent = "Agent stopped. Event stream closed.";
+                }
+            } catch (error) {
+                 statusArea.textContent = "Error stopping agent.";
+                 addMessage('Error stopping agent.', 'model');
+            }
+        }
+
+        async function sendVoiceMessage() {
+            const sendVoiceButton = document.getElementById("sendVoiceBtn");
+            addMessage("🎤 Recording for 5s... Please speak.", "user");
+            statusArea.textContent = "Recording audio...";
+            sendVoiceButton.disabled = true;
+
+            try {
+                const response = await fetch("/send_voice_message", { method: "POST" });
+                const data = await response.json();
+                if (data.status) {
+                    statusArea.textContent = data.status;
+                    // Messages (transcription, model reply) should come via SSE
+                } else if (data.error) {
+                    addMessage("Voice Error: " + data.error, "model");
+                    statusArea.textContent = "Voice error: " + data.error;
+                }
+            } catch (error) {
+                console.error("Error sending voice message:", error);
+                addMessage("Error processing voice input.", "model");
+                statusArea.textContent = "Error processing voice input.";
+            } finally {
+                sendVoiceButton.disabled = false;
+            }
+        }
+
+        // Function to fetch and display video change notifications from the agent
+        let eventSource = null;
+        function connectToAgentEvents() {
+            if (eventSource) {
+                eventSource.close();
+            }
+            statusArea.textContent = "Connecting to agent events...";
+            eventSource = new EventSource("/agent_events");
+
+            eventSource.onopen = function() {
+                statusArea.textContent = "Connected to agent events.";
+                addMessage("Connected to agent for live updates.", "model");
+            };
+
+            eventSource.onmessage = function(event) {
+                const data = JSON.parse(event.data);
+                if (data.type === "video_change_comment") {
+                    addMessage("Video Event: " + data.comment, "model");
+                } else if (data.type === "status") {
+                     statusArea.textContent = data.message;
+                } else if (data.type === "transcription") {
+                    addMessage("You (transcribed): " + data.text, "user");
+                } else if (data.type === "model_response_audio_text") {
+                    addMessage("Agent: " + data.text, "model");
+                }
+            };
+
+            eventSource.onerror = function(err) {
+                console.error("EventSource failed:", err);
+                statusArea.textContent = "Disconnected from agent events.";
+                addMessage("Lost connection to agent live updates.", "model");
+                if (eventSource) eventSource.close();
+            };
+        }
+
+        window.onload = () => {
+            addMessage("Welcome! Click 'Start Agent' to begin.", "model");
+            // Do not automatically connectToAgentEvents here.
+            // connectToAgentEvents will be called by startAgent().
+        };
+
+    </script>
+</body>
+</html>
diff --git a/in_progress/streaming-test3/app/video_utils.py b/in_progress/streaming-test3/app/video_utils.py
new file mode 100644
index 000000000..0da00d1b6
--- /dev/null
+++ b/in_progress/streaming-test3/app/video_utils.py
@@ -0,0 +1,304 @@
+import cv2 # OpenCV library for computer vision tasks
+import numpy as np # For numerical operations, especially with images
+import time # For handling FPS limiting
+
+# Attempt to import VIDEO_FPS from app.config
+# If app.config or VIDEO_FPS is not found, use a default value.
+try:
+    from app.config import VIDEO_FPS
+except ImportError:
+    print("VIDEO_UTILS: Could not import VIDEO_FPS from app.config. Using default value 1.")
+    VIDEO_FPS = 1
+
+
+# --- Video Capture Class ---
+class VideoMonitor:
+    """
+    VideoMonitor encapsulates operations related to capturing and processing
+    video frames from a camera. It can start and stop video capture, retrieve
+    individual frames, and detect significant changes or motion between frames.
+    """
+    def __init__(self, camera_index=0, fps_limit=None):
+        """
+        Initializes the VideoMonitor.
+
+        Args:
+            camera_index (int, optional): The index of the camera to use (e.g., 0 for default).
+                                        Defaults to 0.
+            fps_limit (int or None, optional): Limit processing to this many frames per
+                                               second. None for no limit (process as
+                                               fast as camera provides).
+                                               Defaults to `VIDEO_FPS` from `config.py` if None is provided.
+        """
+        self.camera_index = camera_index  # Store the camera index
+        self.cap = None  # Initialize the capture object to None; opened in `start_capture`
+        self.previous_frame = None # Stores the previous frame for change detection
+        self.last_processed_time = 0  # Timestamp of the last processed frame, for FPS limiting
+
+        # Set the FPS limit. If `fps_limit` is not provided (None), use the VIDEO_FPS value
+        # from the `app.config` file (or its default if import failed). This allows a centralized default FPS configuration.
+        self.fps_limit = fps_limit if fps_limit is not None else VIDEO_FPS
+
+        # Store a reference to the `cv2` module, primarily for using `cv2.imencode` without
+        # having to import it in other files that might use this class.
+        self.cv2 = cv2
+
+        # Optional: Hog Feature Descriptor for person detection. Commented out as it
+        # can be slow and is not tthe rimary focus of this module.
+        # self.hog = cv2.HOGDescriptor()
+        # self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+        # Background subtractor: This is a common OpenCV technique for detecting motion.
+        # It builds a model of the static background and then compares new frames to it.
+        #   - `history`: Number of frames used to build the background model.
+        #   - `varThreshold`: Threshold on the Mahalanobis distance to classify a pixel as foreground.
+        #   - `detectShadows`: Whether to detect and mark shadows. Set to False as shadows can be
+        #     incorrectly detected as foreground objects.
+        self.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+    def start_capture(self):
+        """
+        Starts video capture from the specified camera.
+        It initializes the OpenCV `cv2.VideoCapture` object.
+
+        Returns:
+            bool: True if capture started successfully, False otherwise.
+        """
+        # Create a VideoCapture object. The argument is the camera index.
+        self.cap = cv2.VideoCapture(self.camera_index)
+
+        # Check if the camera was opened successfully.
+        if not self.cap.isOpened():
+            print(f"Error: Could not open video stream from camera index {self.camera_index}.")
+            print("Make sure a camera is connected and accessible.")
+            print("If using a virtual environment, ensure OpenCV can access camera drivers.")
+            self.cap = None  # Ensure `self.cap` is set to None if the operation failed.
+            return False
+
+        print(f"Video capture started on camera index {self.camera_index}.")
+        # Reset the previous frame when starting a new capture session.
+        self.previous_frame = None
+        # Re-initialize the background subtractor to ensure it has a fresh background
+        # history when capture starts.
+        self.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+        return True
+
+    def stop_capture(self):
+        """
+        Stops video capture and releases the camera resource.
+        """
+        if self.cap:
+            self.cap.release()  # Release the camera.
+            self.cap = None  # Set the capture object to None.
+            print("Video capture stopped.")
+        # Reset the previous frame as it's no longer relevant.
+        self.previous_frame = None
+
+    def get_frame(self):
+        """
+        Retrieves a single frame from the camera.
+
+        Returns:
+            np.ndarray: A numpy array representing the frame, or None if capture failed.
+        """
+        # Check if the capture object is valid and the camera is open.
+        if not self.cap or not self.cap.isOpened():
+            # print("Capture not started or failed.") # Can be noisy if called frequently.
+            return None
+
+        # Read a frame from the camera.
+        #   - `ret`: A boolean indicating if the frame was read successfully.
+        #   - `frame`: The captured frame (a np.ndarray array).
+        ret, frame = self.cap.read()
+
+        # If the frame was not received successfully (e.g., end of a video file).
+        if not ret:
+            print("Error: Can't receive frame (stream end?). Exiting ...")
+            return None
+        return frame
+
+    def detect_significant_change(self, frame: np.ndarray, threshold_abs_diff=30, min_contour_area=500) -> tuple[bool, str, bytes | None]:
+        """
+        Detects a significant change in the video frame using background subtraction,
+        morphological operations for noise reduction, and contour analysis for motion.
+        Color conversion to grayscale is not used here as background subtraction
+        often works better on color frames directly or has built-in mechanisms.
+
+        Args:
+            frame (np.ndarray): The current video frame.
+            threshold_abs_diff (int, optional): Sensitivity for absolute difference
+                method. (Not used directly with MOG2 but kept for potential alternatives).
+            min_contour_area (int, optional): Minimum area of a contour to be considered
+                as significant motion. Defaults to 500.
+
+        Returns:
+            tuple[bool, str, bytes | None]:
+                - has_changed (bool): True if a significant change was detected, False otherwise.
+                - change_description (str): A textual description of the change.
+                - processed_frame_bytes (bytes | None): The JPEG encoded frame if
+                  a change is detected, otherwise None.
+        """
+        if frame is None:
+            return False, "No frame", None
+
+        # 1. Background Subtraction
+        # The bg_subtractor.apply() method computes a foreground mask.
+        # The mask consists of white pixels for foreground and black for background.
+        fgmask = self.bg_subtractor.apply(frame)
+
+        # 2. Noise Reduction
+        # Morphological operations are used to reduce noise and improve the foreground mask.
+        # - `cv2.MORPH_OPEN`: Removes small white noise from the foreground.
+        #   It is an erosion followed by a dilation.
+        # kernel: A small matrix used for the morphological operations.
+        kernel = np.ones((5,5), np.uint8)
+        fgmask_opened = self.cv2.morphologyEx(fgmask, self.cv2.MORPH_OPEN, kernel)
+        # - `cv2.MORPH_CLOSE`: Fills small holes in the foreground objects.
+        #   It is a dilation followed by an erosion.
+        fgmask_closed = cv2.morphologyEx(fgmask_opened, self.cv2.MORPH_CLOSE, kernel)
+
+        # 3. Find Contours of Moved Objects
+        # Contours are the boundaries of the white regions (foreground objects) in the mask.
+        #   - `cv2.RETR_EXTERNAL`: Retrieves only the extreme outer contours.
+        #   - `cv2.CHAIN_APPROX_SIMPLE`: Compresses horizontal, vertical, and diagonal segments
+        #     and leaves only their end points. For example, a rectangle's is encoded with 4 points.
+        contours, _ = cv2.findContours(fgmask_closed, self.cv2.RETR_EXTERNAL, self.cv2.CHAIN_APPROX_SIMPLE)
+
+        significant_motion_detected = False
+        for contour in contours:
+            # Calculate the area of each contour.
+            if cv2.contourArea(contour) > min_contour_area:
+                # If the area is greater than the threshold, consider it significant motion.
+                significant_motion_detected = True
+                # Optional: Draw bounding box on the original frame for visualization or describe as "Visual motion"
+                # description.
+                # (x, y, w, h) = cv2.boundingRect(contour)
+                # cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255,0), 2) # Draw on original for viz
+                break # Found one significant motion, so no need to check further contours.
+
+        if significant_motion_detected:
+            change_description = "Significant motion detected in the scene."
+            # Encode the current frame to JPEG bytes to send to the model.
+            #   - `ret`: True if encoding was successful.
+            #   - `buffer`: The numpy array containing the encoded bytes.
+            ret, buffer = self.cv2.imencode('.jpg', frame)
+            if ret:
+                frame_bytes = buffer.tobytes()  # Convert the numpy array to bytes.
+                return True, change_description, frame_bytes
+            else:
+                print("VideoMonitor: Error encoding frame to JPEG.")
+                return True, change_description, None # Encoding failed.
+
+        # If no significant motion was detected.
+        return False, "No significant change detected.", None
+
+
+    def process_frame_for_changes(self) -> tuple[bool, str, bytes | None]:
+        """
+        Captures a frame from the camera, detects significant changes, and limits
+        the processing rate according to the `self.fps_limit`.
+
+        Returns:
+            tuple(bool, str, bytes | None):
+                - change_detected (bool): True if a change was detected, False otherwise.
+                - change_description (str): A description of the change or a status message.
+                - frame_bytes_if_change (bytes | None): The JPEG encoded frame bytes
+                  if a change was detected, otherwise None.
+        """
+        current_time = time.time()
+
+        # FPS limiting logic
+        if self.fps_limit and (current_time - self.last_processed_time) < (1.0 / self.fps_limit):
+            # If the time since the last processed frame is less than the desired frame
+            # interval (calculated from fps_limit), then skip processing this frame.
+            time.sleep(0.01)  # Add a small sleep to prevent a busy-loop if the camera is much
+                             # faster than the fps_limit, or if the camera capture itself is non-blocking.
+            return False, "FPS limit, frame skipped", None
+
+        self.last_processed_time = current_time  # Update the last processed time
+
+        frame = self.get_frame()  # Get a frame from the camera
+        if frame is None:
+            return False, "Failed to get frame", None
+
+        # Detect changes in the frame.
+        return self.detect_significant_change(frame)
+
+
+    # Optional: Person detection method.
+    # This is commented out because it can be slow and computationally intensive.
+    # It uses a Pre-trained HOG (Histogram of Oriented Gradients) detector.
+    # def detect_persons(self,frame: np.ndarray) -> tuple[bool, str, np.ndarray | None]:
+    #     if frame is None:
+    #         return False, "No frame for person detection", None
+
+    #     # To speed up detection, resize the frame to decrease the number of pixels to be processed
+    #     frame_resized = cv2.resize(frame, (640, 480))
+    #     # (rects, weights) = self.hog.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=1.05)
+
+    #     if len(rects) > 0:
+    #         # For now, just signal if any person is detected.
+    #         # Could be enhanced to count, track entries/exits relative to a previous state.
+    #         # for (x, y, w, h) in rects:
+    #         #     cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2) # Draw on original for viz
+
+    #         change_description = f"{len(rects)} person(s) detected in the view."
+    #         ret, buffer = self.cv2.imencode('.jpg', frame) # Send frame with detected people
+    #         if ret:
+    #             frame_bytes = buffer.tobytes() # Corrected
+    #             return True, change_description, frame_bytes
+    #         else:
+    #             return True, change_description, None
+    #     return False, "No persons detected.", None
+
+# This block allows the script to be run directly for testing.
+def main_test():
+    print("Testing video_utils.py...")
+    # This test requires a camera. If no camera, it will try to proceed but fail gracefully.
+    # The VideoMonitor class is defined in the same file, so it's in scope here.
+    monitor = VideoMonitor(camera_index=0, fps_limit=1) # Limit to 1 FPS for this test
+
+    if not monitor.start_capture():
+        print("Failed to start camera capture. Ensure a camera is connected and drivers are working.")
+        print("If running in a headless environment, this test might not be fully effective without a virtual camera.")
+    else:
+        print("Camera capture started. Will monitor for changes for a few seconds.")
+        start_time = time.time()
+        frames_processed = 0
+        try:
+            while time.time() - start_time < 10: # Run for 10 seconds
+                changed, description, frame_bytes = monitor.process_frame_for_changes()
+                if changed:
+                    print(f'Change detected: {description}')
+                    if frame_bytes:
+                        print(f"Frame data size: {len(frame_bytes)} bytes")
+                        # with open("detected_change.jpg", "wb") as f: # Option to save frame
+                        #     f.write(frame_bytes)
+                        # print("Saved detected_change.jpg")
+                elif description == "Failed to get frame":
+                    print("Stopping test due to frame capture failure.")
+                    break
+                elif description != "FPS limit, frame skipped": # Don't print "FPS limit" too often, but other status messages
+                    print(f'Status: {description} (Frame: {frames_processed})')
+
+                frames_processed +=1
+                # A short sleep is important if not FPS limited in process_frame_for_changes,
+                # if camera is very fast. Here it's mainly for responsive exit.
+                time.sleep(0.05)
+        except KeyboardInterrupt:
+            print("Test interrupted by user.")
+        finally:
+            monitor.stop_capture()
+            print("Video utils testing finished.")
+
+# The `if __name__ == '__main__':` block ensures that `main_test()` is called only when the script is executed directly,
+# not when it's imported as a module. This is a standard Python practice.
+# By the time the `if __name__ == '__main__':` block is executed,
+# the class definition has already been processed and the class name is in the module global scope.
+# The original error (NameErro) often occurs in more complex scenarios like circular imports
+# or if the runnable block tries to access the class in a way that depends on import-at time side-effects
+# that aren't complete when the `if __name__ == '__main__':` check happens.
+# Wrapping it in a function is a good practice for cleanliness and to avoid potential scoping issues,
+# especially with tools like Uvicorn's reloader that might import modules in specific ways.
+if __name__ == '__main__':
+    main_test()
diff --git a/in_progress/streaming-test3/app/web_interface.py b/in_progress/streaming-test3/app/web_interface.py
new file mode 100644
index 000000000..102d536af
--- /dev/null
+++ b/in_progress/streaming-test3/app/web_interface.py
@@ -0,0 +1,59 @@
+# web_interface.py
+# This module contains the logic for handling the camera and generating
+# a video stream for the web interface.
+
+import cv2
+import threading
+
+# --- Singleton Camera Instance ---
+# We use a singleton pattern here to ensure that only one camera object
+# is created and used throughout the application. This prevents conflicts
+# where both the video feed and the video interpretation agent try to
+# access the camera simultaneously.
+
+_camera = None
+_camera_lock = threading.Lock()
+
+def get_camera_instance():
+    """
+    Initializes and returns a single instance of the camera.
+    Uses a lock to make it thread-safe.
+    """
+    global _camera
+    with _camera_lock:
+        if _camera is None:
+            # Initialize the camera. 0 is typically the default built-in webcam.
+            _camera = cv2.VideoCapture(0)
+            if not _camera.isOpened():
+                raise RuntimeError("Could not start camera.")
+    return _camera
+
+# --- Video Frame Generation ---
+
+def generate_frames(camera):
+    """
+    A generator function that continuously captures frames from the camera,
+    encodes them as JPEGs, and yields them for streaming.
+    This is what creates the live video feed.
+    """
+    while True:
+        # Read a frame from the camera.
+        success, frame = camera.read()
+        if not success:
+            # If we fail to get a frame, break the loop.
+            print("Failed to grab frame")
+            break
+        else:
+            # Encode the frame into JPEG format.
+            ret, buffer = cv2.imencode('.jpg', frame)
+            if not ret:
+                print("Failed to encode frame")
+                continue
+            
+            # Convert the buffer to bytes.
+            frame_bytes = buffer.tobytes()
+            
+            # Yield the frame in the format required for a multipart response.
+            # The browser will recognize this and display the stream of images.
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
\ No newline at end of file
diff --git a/in_progress/streaming-test3/requirements.txt b/in_progress/streaming-test3/requirements.txt
new file mode 100644
index 000000000..5aad16aed
--- /dev/null
+++ b/in_progress/streaming-test3/requirements.txt
@@ -0,0 +1,13 @@
+fastapi
+uvicorn[standard]
+google-cloud-aiplatform
+python-dotenv
+google-generativeai
+google-cloud-texttospeech
+google-cloud-speech
+opencv-python
+numpy
+# Add any other specific versions or packages if known e.g. google-adk
+google-adk==1.0.0
+certifi # Often needed for SSL with ADK
+deprecated
diff --git a/in_progress/streaming-test3/tests/__init__.py b/in_progress/streaming-test3/tests/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/in_progress/streaming-test3/tests/test_agent.py b/in_progress/streaming-test3/tests/test_agent.py
new file mode 100644
index 000000000..6e4f06e89
--- /dev/null
+++ b/in_progress/streaming-test3/tests/test_agent.py
@@ -0,0 +1,156 @@
+import unittest
+from unittest.mock import patch, MagicMock, PropertyMock
+import sys
+import os
+import time # for time.time() in agent if __name__ == '__main__'
+
+# --- Global Mocking for vertexai (BEFORE app.agent is imported) ---
+os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'
+os.environ['GOOGLE_CLOUD_PROJECT'] = 'test-project'
+os.environ['GOOGLE_CLOUD_LOCATION'] = 'test-location'
+
+mock_vertexai_module = MagicMock(name="vertexai_mock")
+mock_generative_models_module = MagicMock(name="generative_models_mock")
+mock_generative_model_class = MagicMock(name="GenerativeModelClassMock")
+mock_model_instance = MagicMock(name="GenerativeModelInstanceMock")
+mock_chat_instance = MagicMock(name="ChatInstanceMock")
+
+# --- Part Mocking ---
+mock_part_class = MagicMock(name="PartMock")
+# Configure from_text and from_data to return mocks that have a to_dict method
+mock_text_part_instance = MagicMock(name="TextPartInstance")
+mock_text_part_instance.to_dict.return_value = {"text": "mocked text"} # Example to_dict content
+mock_data_part_instance = MagicMock(name="DataPartInstance")
+mock_data_part_instance.to_dict.return_value = {"inline_data": {"mime_type": "image/jpeg", "data": "mocked_data"}}
+
+mock_part_class.from_text.return_value = mock_text_part_instance
+mock_part_class.from_data.return_value = mock_data_part_instance
+# --- End Part Mocking ---
+
+mock_vertexai_module.init = MagicMock(name="vertexai_init_mock")
+mock_vertexai_module.generative_models = mock_generative_models_module
+mock_generative_models_module.GenerativeModel = mock_generative_model_class
+mock_generative_model_class.return_value = mock_model_instance
+mock_generative_models_module.Part = mock_part_class
+mock_model_instance.start_chat.return_value = mock_chat_instance
+
+sys.modules['vertexai'] = mock_vertexai_module
+sys.modules['vertexai.generative_models'] = mock_generative_models_module
+# --- End Global Mocking ---
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from app.agent import MultimodalAgent
+
+@patch('app.agent.VideoMonitor')
+@patch('app.agent.record_audio')
+@patch('app.agent.transcribe_audio_bytes')
+@patch('app.agent.synthesize_text_to_audio_bytes')
+@patch('app.agent.play_audio_bytes')
+@patch('app.agent.load_dotenv')
+class TestMultimodalAgent(unittest.TestCase):
+
+    def setUp(self):
+        mock_vertexai_module.init.reset_mock()
+        mock_generative_model_class.reset_mock()
+        mock_generative_model_class.return_value = mock_model_instance
+        mock_model_instance.start_chat.reset_mock()
+        mock_model_instance.start_chat.return_value = mock_chat_instance
+        mock_chat_instance.reset_mock()
+        mock_chat_instance.send_message.reset_mock()
+
+        mock_part_class.from_data.reset_mock()
+        mock_part_class.from_text.reset_mock()
+        # Ensure return values are reassigned for each test if they could be altered
+        mock_part_class.from_text.return_value = mock_text_part_instance
+        mock_part_class.from_data.return_value = mock_data_part_instance
+        # Reset to_dict mocks if necessary (though typically defined once)
+        mock_text_part_instance.to_dict.reset_mock(return_value=MagicMock()) # Avoid state leakage
+        mock_text_part_instance.to_dict.return_value = {"text": "mocked text"}
+        mock_data_part_instance.to_dict.reset_mock(return_value=MagicMock())
+        mock_data_part_instance.to_dict.return_value = {"inline_data": {"mime_type": "image/jpeg", "data": "mocked_data"}}
+
+
+    def test_agent_initialization(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        agent = MultimodalAgent(project_id="test-proj", location="test-loc", model_name="gemini-test")
+        mock_vertexai_module.init.assert_called_once_with(project="test-proj", location="test-loc")
+        mock_generative_model_class.assert_called_once_with("gemini-test")
+        MockVideoMonitor.assert_called_once_with(camera_index=0, fps_limit=1)
+        self.assertIsNotNone(agent.model)
+
+    def test_start_chat(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = None
+        mock_video_monitor_instance.start_capture.return_value = True
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.start_chat()
+        mock_model_instance.start_chat.assert_called_once_with(response_validation=False)
+        self.assertEqual(agent.chat, mock_chat_instance)
+        mock_video_monitor_instance.start_capture.assert_called_once()
+
+    def test_stop_chat(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = True
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.stop_chat()
+        mock_video_monitor_instance.stop_capture.assert_called_once()
+        self.assertIsNone(agent.chat)
+
+    def test_send_text_message_success(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_chat_instance.send_message.return_value = MagicMock(text="Model reply")
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        reply = agent.send_text_message("Hello agent")
+        self.assertEqual(reply, "Model reply")
+        mock_chat_instance.send_message.assert_called_with("Hello agent")
+
+    def test_send_message_with_image(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_chat_instance.send_message.return_value = MagicMock(text="Understood image")
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        # Pass the actual mock instance for image_parts
+        reply = agent.send_message(text_prompt="Describe this", image_parts=[mock_data_part_instance])
+        self.assertEqual(reply, "Understood image")
+        sent_parts_list = mock_chat_instance.send_message.call_args[0][0]
+        self.assertIn(mock_text_part_instance, sent_parts_list)
+        self.assertIn(mock_data_part_instance, sent_parts_list)
+
+    def test_check_for_video_changes_and_comment_detected(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        mock_video_monitor_instance = MockVideoMonitor.return_value
+        mock_video_monitor_instance.cap = MagicMock()
+        mock_video_monitor_instance.cap.isOpened.return_value = True
+        mock_video_monitor_instance.process_frame_for_changes.return_value = (True, "Motion detected", b"jpeg_bytes")
+        mock_chat_instance.send_message.return_value = MagicMock(text="I see motion!")
+        mock_event_queue = MagicMock()
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.check_for_video_changes_and_comment(event_queue=mock_event_queue)
+        mock_video_monitor_instance.process_frame_for_changes.assert_called_once()
+        mock_part_class.from_data.assert_called_with(b"jpeg_bytes", mime_type="image/jpeg")
+        sent_parts_list = mock_chat_instance.send_message.call_args[0][0]
+        self.assertIn(mock_text_part_instance, sent_parts_list)
+        self.assertIn(mock_data_part_instance, sent_parts_list)
+        mock_event_queue.put.assert_called_once_with(
+            {"type": "video_change_comment", "comment": "I see motion!"}
+        )
+
+    def test_handle_voice_interaction(self, MockLoadDotenv, MockPlayAudio, MockSynth, MockTranscribe, MockRecord, MockVideoMonitor):
+        MockRecord.return_value = b"recorded_audio_bytes"
+        MockTranscribe.return_value = "user speech text"
+        mock_chat_instance.send_message.return_value = MagicMock(text="model speech text")
+        MockSynth.return_value = b"synthesized_audio_bytes"
+        mock_event_queue = MagicMock()
+        agent = MultimodalAgent(project_id="p", location="l", model_name="m")
+        agent.chat = mock_chat_instance
+        agent.handle_voice_interaction(record_duration_seconds=3, event_queue=mock_event_queue)
+        MockRecord.assert_called_once_with(duration_seconds=3)
+        MockTranscribe.assert_called_once_with(b"recorded_audio_bytes")
+        mock_chat_instance.send_message.assert_called_with("user speech text")
+        MockSynth.assert_called_once_with("model speech text")
+        MockPlayAudio.assert_called_once_with(b"synthesized_audio_bytes", output_format="mp3")
+        self.assertIn(unittest.mock.call({"type": "transcription", "text": "user speech text"}), mock_event_queue.put.call_args_list)
+        self.assertIn(unittest.mock.call({"type": "model_response_audio_text", "text": "model speech text"}), mock_event_queue.put.call_args_list)
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test3/tests/test_audio_utils.py b/in_progress/streaming-test3/tests/test_audio_utils.py
new file mode 100644
index 000000000..091dec45c
--- /dev/null
+++ b/in_progress/streaming-test3/tests/test_audio_utils.py
@@ -0,0 +1,153 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import numpy as np
+from scipy.io.wavfile import write as write_wav
+import io
+import sys
+import os
+
+# Mock sounddevice is done globally in run_tests.py.
+# We get a reference to that global mock here.
+# This ensures that app.audio_utils, when it 'import sounddevice as sd', gets the global mock.
+mock_sd_module = sys.modules.get('sounddevice', MagicMock(name="FallbackMockSoundDeviceModule"))
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from app.audio_utils import record_audio, transcribe_audio_bytes, synthesize_text_to_audio_bytes, play_audio_bytes
+from app.config import AUDIO_SAMPLE_RATE
+
+class TestAudioUtils(unittest.TestCase):
+
+    def setUp(self):
+        # Reset the attributes of the global mock_sd_module for each test
+        # This is important because mock_sd_module is shared state modified by run_tests.py
+        mock_sd_module.rec = MagicMock(name="sd_rec_mock_from_setUp")
+        mock_sd_module.wait = MagicMock(name="sd_wait_mock_from_setUp")
+        mock_sd_module.play = MagicMock(name="sd_play_mock_from_setUp")
+        # Ensure other methods that might be called are also reset if necessary
+        mock_sd_module.reset_mock() # Resets call counts etc. on the main mock
+                                    # and also replaces attributes like .rec with new MagicMocks.
+                                    # So, re-assign them if specific naming is desired or if they need to be shared.
+        # Re-establish well-known mocks on the global mock after reset_mock()
+        # These will be the mocks that app.audio_utils.sd.* actually calls.
+        # The 'global' keyword here was causing a SyntaxError and is not needed
+        # as mock_sd_module is already in the module's global scope.
+        mock_sd_module.rec = MagicMock(name="sd.rec")
+        mock_sd_module.wait = MagicMock(name="sd.wait")
+        mock_sd_module.play = MagicMock(name="sd.play")
+
+
+    def generate_dummy_wav_bytes(self, duration_seconds=1, sample_rate=AUDIO_SAMPLE_RATE, channels=1):
+        num_samples = int(duration_seconds * sample_rate)
+        frequency = 440
+        t = np.linspace(0, duration_seconds, num_samples, False)
+        audio_data = (0.5 * np.sin(2 * np.pi * frequency * t) * 32767).astype(np.int16)
+        if channels == 2:
+            audio_data = np.column_stack((audio_data, audio_data))
+        byte_io = io.BytesIO()
+        write_wav(byte_io, sample_rate, audio_data)
+        return byte_io.getvalue()
+
+    def test_record_audio_success(self):
+        duration = 1
+        sample_rate = 16000
+        channels = 1
+        expected_samples = duration * sample_rate
+        # This is a REAL numpy array that scipy.io.wavfile.write expects.
+        dummy_numpy_array = np.random.randint(-32768, 32767, size=(expected_samples, channels), dtype=np.int16)
+
+        # Configure the mock that app.audio_utils.sd.rec will resolve to
+        mock_sd_module.rec.return_value = dummy_numpy_array
+
+        audio_bytes = record_audio(duration_seconds=duration, sample_rate=sample_rate, channels=channels)
+
+        mock_sd_module.rec.assert_called_once_with(int(duration * sample_rate), samplerate=sample_rate, channels=channels, dtype='int16')
+        mock_sd_module.wait.assert_called_once()
+        self.assertIsNotNone(audio_bytes)
+        self.assertIsInstance(audio_bytes, bytes)
+
+    def test_record_audio_failure(self):
+        # Configure the mock that app.audio_utils.sd.rec will resolve to
+        mock_sd_module.rec.side_effect = Exception("Recording failed")
+
+        audio_bytes = record_audio(duration_seconds=1)
+
+        self.assertIsNone(audio_bytes)
+        mock_sd_module.rec.assert_called_once() # Should still be called once
+
+
+    @patch('app.audio_utils.speech.SpeechClient')
+    def test_transcribe_audio_bytes_success(self, MockSpeechClient):
+        mock_client_instance = MockSpeechClient.return_value
+        mock_recognize_response = MagicMock()
+        mock_alternative = MagicMock()
+        mock_alternative.transcript = "hello world"
+        mock_result = MagicMock()
+        mock_result.alternatives = [mock_alternative]
+        mock_recognize_response.results = [mock_result]
+        mock_client_instance.recognize.return_value = mock_recognize_response
+
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio, sample_rate=AUDIO_SAMPLE_RATE)
+        self.assertEqual(transcript, "hello world")
+
+    @patch('app.audio_utils.speech.SpeechClient', side_effect=Exception("GCS API Error"))
+    def test_transcribe_audio_bytes_api_failure(self, MockSpeechClient):
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio)
+        self.assertIsNone(transcript)
+
+    def test_transcribe_audio_bytes_no_input(self):
+        transcript = transcribe_audio_bytes(None)
+        self.assertIsNone(transcript)
+
+    @patch('app.audio_utils.speech.SpeechClient')
+    def test_transcribe_audio_bytes_no_results(self, MockSpeechClient):
+        mock_client_instance = MockSpeechClient.return_value
+        mock_recognize_response = MagicMock()
+        mock_recognize_response.results = []
+        mock_client_instance.recognize.return_value = mock_recognize_response
+        dummy_audio = self.generate_dummy_wav_bytes()
+        transcript = transcribe_audio_bytes(dummy_audio, sample_rate=AUDIO_SAMPLE_RATE)
+        self.assertEqual(transcript, "")
+
+    @patch('app.audio_utils.texttospeech.TextToSpeechClient')
+    def test_synthesize_text_to_audio_bytes_success(self, MockTextToSpeechClient):
+        mock_client_instance = MockTextToSpeechClient.return_value
+        mock_synthesize_response = MagicMock()
+        mock_synthesize_response.audio_content = b"dummy_audio_content"
+        mock_client_instance.synthesize_speech.return_value = mock_synthesize_response
+        audio_bytes = synthesize_text_to_audio_bytes("hello")
+        self.assertEqual(audio_bytes, b"dummy_audio_content")
+
+    @patch('app.audio_utils.texttospeech.TextToSpeechClient', side_effect=Exception("TTS API Error"))
+    def test_synthesize_text_to_audio_bytes_api_failure(self, MockTextToSpeechClient):
+        audio_bytes = synthesize_text_to_audio_bytes("test text")
+        self.assertIsNone(audio_bytes)
+
+    def test_synthesize_text_to_audio_bytes_no_input(self):
+        audio_bytes = synthesize_text_to_audio_bytes("")
+        self.assertIsNone(audio_bytes)
+
+    @patch('scipy.io.wavfile.read')
+    def test_play_audio_bytes_wav(self, mock_scipy_read_wav):
+        dummy_wav_data = np.array([1, 2, 3], dtype=np.int16)
+        dummy_sample_rate = AUDIO_SAMPLE_RATE
+        mock_scipy_read_wav.return_value = (dummy_sample_rate, dummy_wav_data)
+
+        wav_bytes = self.generate_dummy_wav_bytes()
+        play_audio_bytes(wav_bytes, output_format="wav", sample_rate=dummy_sample_rate)
+
+        mock_scipy_read_wav.assert_called_once()
+        self.assertIsInstance(mock_scipy_read_wav.call_args[0][0], io.BytesIO)
+        mock_sd_module.play.assert_called_once_with(dummy_wav_data, samplerate=dummy_sample_rate)
+        mock_sd_module.wait.assert_called_once()
+
+    @patch('app.audio_utils.time.sleep')
+    def test_play_audio_bytes_mp3_placeholder(self, mock_time_sleep):
+        mp3_bytes = b"dummy_mp3_data"
+        play_audio_bytes(mp3_bytes, output_format="mp3")
+        mock_time_sleep.assert_called()
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test3/tests/test_video_utils.py b/in_progress/streaming-test3/tests/test_video_utils.py
new file mode 100644
index 000000000..803afe174
--- /dev/null
+++ b/in_progress/streaming-test3/tests/test_video_utils.py
@@ -0,0 +1,159 @@
+import unittest
+from unittest.mock import patch, MagicMock
+import numpy as np
+import cv2 # OpenCV is a direct dependency here
+import time # Import the time module
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from app.video_utils import VideoMonitor
+from app.config import VIDEO_FPS
+
+class TestVideoUtils(unittest.TestCase):
+
+    def create_dummy_frame(self, width=640, height=480, channels=3, color=(0,0,0)):
+        frame = np.full((height, width, channels), color, dtype=np.uint8)
+        return frame
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_start_capture_success(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+
+        monitor = VideoMonitor(camera_index=0)
+        self.assertTrue(monitor.start_capture())
+        MockVideoCapture.assert_called_once_with(0)
+        self.assertIsNotNone(monitor.cap)
+        self.assertTrue(monitor.cap.isOpened())
+        monitor.stop_capture() # Clean up
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_start_capture_failure(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = False # Simulate camera not opening
+
+        monitor = VideoMonitor(camera_index=0)
+        self.assertFalse(monitor.start_capture())
+        self.assertIsNone(monitor.cap)
+
+    def test_videomonitor_get_frame_no_capture(self):
+        monitor = VideoMonitor()
+        # No call to start_capture
+        self.assertIsNone(monitor.get_frame())
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_get_frame_success(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+        dummy_frame_data = self.create_dummy_frame()
+        mock_cap_instance.read.return_value = (True, dummy_frame_data)
+
+        monitor = VideoMonitor()
+        monitor.start_capture() # Sets up monitor.cap
+        frame = monitor.get_frame()
+
+        self.assertIsNotNone(frame)
+        np.testing.assert_array_equal(frame, dummy_frame_data)
+        mock_cap_instance.read.assert_called_once()
+        monitor.stop_capture()
+
+    @patch('app.video_utils.cv2.VideoCapture')
+    def test_videomonitor_get_frame_read_fail(self, MockVideoCapture):
+        mock_cap_instance = MockVideoCapture.return_value
+        mock_cap_instance.isOpened.return_value = True
+        mock_cap_instance.read.return_value = (False, None) # Simulate read failure
+
+        monitor = VideoMonitor()
+        monitor.start_capture()
+        frame = monitor.get_frame()
+
+        self.assertIsNone(frame)
+        monitor.stop_capture()
+
+    def test_detect_significant_change_no_frame(self):
+        monitor = VideoMonitor()
+        changed, desc, _ = monitor.detect_significant_change(None)
+        self.assertFalse(changed)
+        self.assertEqual(desc, "No frame")
+
+    @patch('cv2.imencode', return_value=(True, np.array([1,2,3], dtype=np.uint8))) # Corrected mock target and return value
+    def test_detect_significant_change_with_motion(self, mock_imencode):
+        monitor = VideoMonitor()
+        # monitor.start_capture() # Initializes bg_subtractor - not needed if only testing detect_significant_change directly with a frame
+        # Let's ensure bg_subtractor is available for this test method if it's not through start_capture
+        monitor.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+        frame1 = self.create_dummy_frame(color=(10,10,10)) # Initial background
+        monitor.bg_subtractor.apply(frame1) # establish baseline
+
+        frame2 = self.create_dummy_frame(color=(10,10,10))
+        # Simulate a moving object by drawing a different color rectangle
+        cv2.rectangle(frame2, (100,100), (200,200), (200,200,200), -1)
+
+        changed, desc, frame_bytes = monitor.detect_significant_change(frame2, min_contour_area=100)
+
+        self.assertTrue(changed, f"Change should have been detected. Description: {desc}")
+        self.assertEqual(desc, "Significant motion detected in the scene.")
+        self.assertIsNotNone(frame_bytes)
+        mock_imencode.assert_called_once()
+        # monitor.stop_capture() # Only if start_capture was called
+
+    def test_detect_significant_change_no_motion(self):
+        monitor = VideoMonitor()
+        # monitor.start_capture() # Initializes bg_subtractor
+        monitor.bg_subtractor = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False)
+
+
+        frame1 = self.create_dummy_frame(color=(10,10,10))
+        monitor.bg_subtractor.apply(frame1) # establish baseline
+
+        # Process same frame again or very similar one
+        frame2 = self.create_dummy_frame(color=(10,10,10))
+        # Make a tiny change that should be filtered out by morphology or area
+        cv2.rectangle(frame2, (0,0), (2,2), (20,20,20), -1)
+
+
+        changed, desc, _ = monitor.detect_significant_change(frame2, min_contour_area=500)
+
+        self.assertFalse(changed, f"No significant change should be detected. Description: {desc}")
+        self.assertEqual(desc, "No significant change detected.")
+        # monitor.stop_capture()
+
+    @patch('app.video_utils.VideoMonitor.get_frame')
+    @patch('app.video_utils.VideoMonitor.detect_significant_change')
+    def test_process_frame_for_changes_fps_limit(self, mock_detect_change, mock_get_frame):
+        monitor = VideoMonitor(fps_limit=10) # Limit to 10 FPS
+        monitor.last_processed_time = time.time() # Pretend we just processed a frame
+
+        # Call immediately, should be skipped due to FPS limit
+        changed, desc, _ = monitor.process_frame_for_changes()
+
+        self.assertFalse(changed)
+        self.assertEqual(desc, "FPS limit, frame skipped")
+        mock_get_frame.assert_not_called()
+        mock_detect_change.assert_not_called()
+
+    @patch('app.video_utils.VideoMonitor.get_frame')
+    @patch('app.video_utils.VideoMonitor.detect_significant_change')
+    def test_process_frame_for_changes_proceeds(self, mock_detect_change, mock_get_frame):
+        monitor = VideoMonitor(fps_limit=1)
+        monitor.last_processed_time = time.time() - 2 # Pretend last frame was 2s ago
+
+        dummy_frame = self.create_dummy_frame()
+        mock_get_frame.return_value = dummy_frame
+        mock_detect_change.return_value = (True, "Test change", b"frame_bytes")
+
+        changed, desc, frame_bytes_out = monitor.process_frame_for_changes()
+
+        self.assertTrue(changed)
+        self.assertEqual(desc, "Test change")
+        self.assertEqual(frame_bytes_out, b"frame_bytes")
+        mock_get_frame.assert_called_once()
+        mock_detect_change.assert_called_once_with(dummy_frame)
+
+
+if __name__ == '__main__':
+    unittest.main(argv=['first-arg-is-ignored'], exit=False)
diff --git a/in_progress/streaming-test4/README.md b/in_progress/streaming-test4/README.md
new file mode 100644
index 000000000..f1dcd27de
--- /dev/null
+++ b/in_progress/streaming-test4/README.md
@@ -0,0 +1,578 @@
+this document is pasted from url:  https://google.github.io/adk-docs/streaming/custom-streaming/
+IMPORTANT NOTE:  I ran: pip install "fastapi[standard]"   
+     app/main.py uses fastapi    
+fastAPI documentation:  https://fastapi.tiangolo.com/  
+
+*EDITS  - I had to remove the google_search_tool calls due to errors:  "RESOURCE_EXHAUSTED: Maximum google search tool usage exceeded."  This error occured when streaming with this tool in other examples too.
+
+-Verified as working with audio conversation and allows for interuptions.   However the responeses seems limited to be only a couple lines at the moment.
+
+Custom Audio Streaming app (SSE)¶
+This article overviews the server and client code for a custom asynchronous web app built with ADK Streaming and FastAPI, enabling real-time, bidirectional audio and text communication with Server-Sent Events (SSE). The key features are:
+
+Server-Side (Python/FastAPI): - FastAPI + ADK integration - Server-Sent Events for real-time streaming - Session management with isolated user contexts - Support for both text and audio communication modes - Google Search tool integration for grounded responses
+
+Client-Side (JavaScript/Web Audio API): - Real-time bidirectional communication via SSE and HTTP POST - Professional audio processing using AudioWorklet processors - Seamless mode switching between text and audio - Automatic reconnection and error handling - Base64 encoding for audio data transmission
+
+1. Install ADK¶
+Create & Activate Virtual Environment (Recommended):
+
+
+# Create
+python -m venv .venv
+# Activate (each new terminal)
+# macOS/Linux: source .venv/bin/activate
+# Windows CMD: .venv\Scripts\activate.bat
+# Windows PowerShell: .venv\Scripts\Activate.ps1
+Install ADK:
+
+
+pip install google-adk==1.0.0
+Set SSL_CERT_FILE variable with the following command.
+
+
+in bash:    export SSL_CERT_FILE=$(python -m certifi)
+in powershell:   $env:SSL_CERT_FILE = (python -m certifi)
+
+
+Download the sample code:
+
+
+git clone --no-checkout https://github.com/google/adk-docs.git
+cd adk-docs
+git sparse-checkout init --cone
+git sparse-checkout set examples/python/snippets/streaming/adk-streaming
+git checkout main
+cd examples/python/snippets/streaming/adk-streaming/app
+This sample code has the following files and folders:
+
+
+adk-streaming/
+└── app/ # the web app folder
+    ├── .env # Gemini API key / Google Cloud Project ID
+    ├── main.py # FastAPI web app
+    ├── static/ # Static content folder
+    |   ├── js # JavaScript files folder (includes app.js)
+    |   └── index.html # The web client page
+    └── google_search_agent/ # Agent folder
+        ├── __init__.py # Python package
+        └── agent.py # Agent definition
+2. Set up the platform¶
+To run the sample app, choose a platform from either Google AI Studio or Google Cloud Vertex AI:
+
+
+Gemini - Google AI Studio
+Gemini - Google Cloud Vertex AI
+Get an API key from Google AI Studio.
+Open the .env file located inside (app/) and copy-paste the following code.
+
+.env
+
+GOOGLE_GENAI_USE_VERTEXAI=FALSE
+GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
+Replace PASTE_YOUR_ACTUAL_API_KEY_HERE with your actual API KEY.
+
+
+3. Interact with Your Streaming app¶
+1. Navigate to the Correct Directory:
+
+To run your agent effectively, make sure you are in the app folder (adk-streaming/app)
+
+2. Start the Fast API: Run the following command to start CLI interface with
+
+
+uvicorn main:app --reload
+3. Access the app with the text mode: Once the app starts, the terminal will display a local URL (e.g., http://localhost:8000). Click this link to open the UI in your browser.
+
+Now you should see the UI like this:
+
+ADK Streaming app
+
+Try asking a question What time is it now?. The agent will use Google Search to respond to your queries. You would notice that the UI shows the agent's response as streaming text. You can also send messages to the agent at any time, even while the agent is still responding. This demonstrates the bidirectional communication capability of ADK Streaming.
+
+4. Access the app with the audio mode: Now click the Start Audio button. The app reconnects with the server in an audio mode, and the UI will show the following dialog for the first time:
+
+ADK Streaming app
+
+Click Allow while visiting the site, then you will see the microphone icon will be shown at the top of the browser:
+
+ADK Streaming app
+
+Now you can talk to the agent with voice. Ask questions like What time is it now? with voice and you will hear the agent responding in voice too. As Streaming for ADK supports multiple languages, it can also respond to question in the supported languages.
+
+5. Check console logs
+
+If you are using the Chrome browser, use the right click and select Inspect to open the DevTools. On the Console, you can see the incoming and outgoing audio data such as [CLIENT TO AGENT] and [AGENT TO CLIENT], representing the audio data streaming in and out between the browser and the server.
+
+At the same time, in the app server console, you should see something like this:
+
+
+Client #90766266 connected via SSE, audio mode: false
+INFO:     127.0.0.1:52692 - "GET /events/90766266?is_audio=false HTTP/1.1" 200 OK
+[CLIENT TO AGENT]: hi
+INFO:     127.0.0.1:52696 - "POST /send/90766266 HTTP/1.1" 200 OK
+[AGENT TO CLIENT]: text/plain: {'mime_type': 'text/plain', 'data': 'Hi'}
+[AGENT TO CLIENT]: text/plain: {'mime_type': 'text/plain', 'data': ' there! How can I help you today?\n'}
+[AGENT TO CLIENT]: {'turn_complete': True, 'interrupted': None}
+These console logs are important in case you develop your own streaming application. In many cases, the communication failure between the browser and server becomes a major cause for the streaming application bugs.
+
+6. Troubleshooting tips
+
+When gemini-2.0-flash-exp model doesn't work: If you see any errors on the app server console with regard to gemini-2.0-flash-exp model availability, try replacing it with gemini-2.0-flash-live-001 on app/google_search_agent/agent.py at line 6.
+4. Agent definition¶
+The agent definition code agent.py in the google_search_agent folder is where the agent's logic is written:
+
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   name="google_search_agent",
+   model="gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",
+   description="Agent to answer questions using Google Search.",
+   instruction="Answer the question using the Google Search tool.",
+   tools=[google_search],
+)
+Notice how easily you integrated grounding with Google Search capabilities. The Agent class and the google_search tool handle the complex interactions with the LLM and grounding with the search API, allowing you to focus on the agent's purpose and behavior.
+
+intro_components.png
+
+The server and client architecture enables real-time, bidirectional communication between web clients and AI agents with proper session isolation and resource management.
+
+5. Server side code overview¶
+The FastAPI server provides real-time communication between web clients and the AI agent.
+
+Bidirectional communication overview¶
+Client-to-Agent Flow:¶
+Connection Establishment - Client opens SSE connection to /events/{user_id}, triggering session creation and storing request queue in active_sessions
+Message Transmission - Client sends POST to /send/{user_id} with JSON payload containing mime_type and data
+Queue Processing - Server retrieves session's live_request_queue and forwards message to agent via send_content() or send_realtime()
+Agent-to-Client Flow:¶
+Event Generation - Agent processes requests and generates events through live_events async generator
+Stream Processing - agent_to_client_sse() filters events and formats them as SSE-compatible JSON
+Real-time Delivery - Events stream to client via persistent HTTP connection with proper SSE headers
+Session Management:¶
+Per-User Isolation - Each user gets unique session stored in active_sessions dict
+Lifecycle Management - Sessions auto-cleanup on disconnect with proper resource disposal
+Concurrent Support - Multiple users can have simultaneous active sessions
+Error Handling:¶
+Session Validation - POST requests validate session existence before processing
+Stream Resilience - SSE streams handle exceptions and perform cleanup automatically
+Connection Recovery - Clients can reconnect by re-establishing SSE connection
+Agent Session Management¶
+The start_agent_session() function creates isolated AI agent sessions:
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+InMemoryRunner Setup - Creates a runner instance that manages the agent lifecycle in memory, with the app name "ADK Streaming example" and the Google Search agent.
+
+Session Creation - Uses runner.session_service.create_session() to establish a unique session per user ID, enabling multiple concurrent users.
+
+Response Modality Configuration - Sets RunConfig with either "AUDIO" or "TEXT" modality based on the is_audio parameter, determining output format.
+
+LiveRequestQueue - Creates a bidirectional communication channel that queues incoming requests and enables real-time message passing between client and agent.
+
+Live Events Stream - runner.run_live() returns an async generator that yields real-time events from the agent, including partial responses, turn completions, and interruptions.
+
+Server-Sent Events (SSE) Streaming¶
+The agent_to_client_sse() function handles real-time streaming from agent to client:
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+Event Processing Loop - Iterates through live_events async generator, processing each event as it arrives from the agent.
+
+Turn Management - Detects conversation turn completion or interruption events and sends JSON messages with turn_complete and interrupted flags to signal conversation state changes.
+
+Content Part Extraction - Extracts the first Part from event content, which contains either text or audio data.
+
+Audio Streaming - Handles PCM audio data by:
+
+Detecting audio/pcm MIME type in inline_data
+Base64 encoding raw audio bytes for JSON transmission
+Sending with mime_type and data fields
+
+Text Streaming - Processes partial text responses by sending incremental text updates as they're generated, enabling real-time typing effects.
+
+SSE Format - All data is formatted as data: {json}\n\n following SSE specification for browser EventSource API compatibility.
+
+HTTP Endpoints and Routing¶
+Root Endpoint¶
+GET / - Serves static/index.html as the main application interface using FastAPI's FileResponse.
+
+SSE Events Endpoint¶
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+GET /events/{user_id} - Establishes persistent SSE connection:
+
+Parameters - Takes user_id (int) and optional is_audio query parameter (defaults to "false")
+
+Session Initialization - Calls start_agent_session() and stores the live_request_queue in active_sessions dict using user_id as key
+
+StreamingResponse - Returns StreamingResponse with:
+
+event_generator() async function that wraps agent_to_client_sse()
+MIME type: text/event-stream
+CORS headers for cross-origin access
+Cache-control headers to prevent caching
+
+Cleanup Logic - Handles connection termination by closing the request queue and removing from active sessions, with error handling for stream interruptions.
+
+Message Sending Endpoint¶
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
+POST /send/{user_id} - Receives client messages:
+
+Session Lookup - Retrieves live_request_queue from active_sessions or returns error if session doesn't exist
+
+Message Processing - Parses JSON with mime_type and data fields:
+
+Text Messages - Creates Content with Part.from_text() and sends via send_content()
+Audio Messages - Base64 decodes PCM data and sends via send_realtime() with Blob
+
+Error Handling - Returns appropriate error responses for unsupported MIME types or missing sessions.
+
+6. Client side code overview¶
+The client-side consists of a web interface with real-time communication and audio capabilities:
+
+HTML Interface (static/index.html)¶
+
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
+Simple web interface with: - Messages Display - Scrollable div for conversation history - Text Input Form - Input field and send button for text messages - Audio Control - Button to enable audio mode and microphone access
+
+Main Application Logic (static/js/app.js)¶
+Session Management (app.js)¶
+
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let is_audio = false;
+Random Session ID - Generates unique session ID for each browser instance
+URL Construction - Builds SSE and send endpoints with session ID
+Audio Mode Flag - Tracks whether audio mode is enabled
+Server-Sent Events Connection (app.js)¶
+connectSSE() function handles real-time server communication:
+
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    ...
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+EventSource Setup - Creates SSE connection with audio mode parameter
+Connection Handlers:
+onopen - Enables send button and form submission when connected
+onmessage - Processes incoming messages from agent
+onerror - Handles disconnections with auto-reconnect after 5 seconds
+Message Processing (app.js)¶
+Handles different message types from server:
+
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+Turn Management - Detects turn_complete to reset message state
+Audio Playback - Decodes Base64 PCM data and sends to audio worklet
+Text Display - Creates new message elements and appends partial text updates for real-time typing effect
+Message Sending (app.js)¶
+sendMessage() function sends data to server:
+
+
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+HTTP POST - Sends JSON payload to /send/{session_id} endpoint
+Error Handling - Logs failed requests and network errors
+Message Format - Standardized {mime_type, data} structure
+Audio Player (static/js/audio-player.js)¶
+startAudioPlayerWorklet() function:
+
+AudioContext Setup - Creates context with 24kHz sample rate for playback
+Worklet Loading - Loads PCM player processor for audio handling
+Audio Pipeline - Connects worklet node to audio destination (speakers)
+Audio Recorder (static/js/audio-recorder.js)¶
+startAudioRecorderWorklet() function:
+
+AudioContext Setup - Creates context with 16kHz sample rate for recording
+Microphone Access - Requests user media permissions for audio input
+Audio Processing - Connects microphone to recorder worklet
+Data Conversion - Converts Float32 samples to 16-bit PCM format
+Audio Worklet Processors¶
+PCM Player Processor (static/js/pcm-player-processor.js)¶
+PCMPlayerProcessor class handles audio playback:
+
+Ring Buffer - Circular buffer for 180 seconds of 24kHz audio
+Data Ingestion - Converts Int16 to Float32 and stores in buffer
+Playback Loop - Continuously reads from buffer to output channels
+Overflow Handling - Overwrites oldest samples when buffer is full
+PCM Recorder Processor (static/js/pcm-recorder-processor.js)¶
+PCMProcessor class captures microphone input:
+
+Audio Input - Processes incoming audio frames
+Data Transfer - Copies Float32 samples and posts to main thread via message port
+Mode Switching:¶
+Audio Activation - "Start Audio" button enables microphone and reconnects SSE with audio flag
+Seamless Transition - Closes existing connection and establishes new audio-enabled session
+The client architecture enables seamless real-time communication with both text and audio modalities, using modern web APIs for professional-grade audio processing.
+
+Summary¶
+This application demonstrates a complete real-time AI agent system with the following key features:
+
+Architecture Highlights: - Real-time: Streaming responses with partial text updates and continuous audio - Robust: Comprehensive error handling and automatic recovery mechanisms - Modern: Uses latest web standards (AudioWorklet, SSE, ES6 modules)
+
+The system provides a foundation for building sophisticated AI applications that require real-time interaction, web search capabilities, and multimedia communication.
+
+Next steps for production¶
+To deploy this system in a production environment, consider implementing the following improvements:
+
+Security¶
+Authentication: Replace random session IDs with proper user authentication
+API Key Security: Use environment variables or secret management services
+HTTPS: Enforce TLS encryption for all communications
+Rate Limiting: Prevent abuse and control API costs
+Scalability¶
+Persistent Storage: Replace in-memory sessions with a persistent session
+Load Balancing: Support multiple server instances with shared session state
+Audio Optimization: Implement compression to reduce bandwidth usage
+Monitoring¶
+Error Tracking: Monitor and alert on system failures
+API Cost Monitoring: Track Google Search and Gemini usage to prevent budget overruns
+Performance Metrics: Monitor response times and audio latency
+Infrastructure¶
+Containerization: Package with Docker for consistent deployments with Cloud Run or Agent Engine
+Health Checks: Implement endpoint monitoring for uptime tracking
\ No newline at end of file
diff --git a/in_progress/streaming-test4/app/google_search_agent/__init__.py b/in_progress/streaming-test4/app/google_search_agent/__init__.py
new file mode 100644
index 000000000..63bd45e6d
--- /dev/null
+++ b/in_progress/streaming-test4/app/google_search_agent/__init__.py
@@ -0,0 +1 @@
+from . import agent
\ No newline at end of file
diff --git a/in_progress/streaming-test4/app/google_search_agent/agent.py b/in_progress/streaming-test4/app/google_search_agent/agent.py
new file mode 100644
index 000000000..150538abb
--- /dev/null
+++ b/in_progress/streaming-test4/app/google_search_agent/agent.py
@@ -0,0 +1,12 @@
+from google.adk.agents import Agent
+
+root_agent = Agent(
+    name="greeting_agent",  #google_search_agent  
+    # https://ai.google.dev/gemini-api/docs/models
+    model= "gemini-2.0-flash-live-preview-04-09",  #"gemini-2.0-flash-exp", 
+    description="Greeting agent",
+    instruction="""
+    You are an interesting and engaging conversationalist. you will ask the users name and instegate a conversation with 
+    user.
+    """,
+)
diff --git a/in_progress/streaming-test4/app/google_search_agent/agent.py_orig b/in_progress/streaming-test4/app/google_search_agent/agent.py_orig
new file mode 100644
index 000000000..015977dc3
--- /dev/null
+++ b/in_progress/streaming-test4/app/google_search_agent/agent.py_orig
@@ -0,0 +1,30 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from google.adk.agents import Agent
+from google.adk.tools import google_search  # Import the tool
+
+root_agent = Agent(
+   # A unique name for the agent.
+   name="google_search_agent",
+   # The Large Language Model (LLM) that agent will use.
+   model= "gemini-2.0-flash-exp", # if this model does not work, try below
+   #model="gemini-2.0-flash-live-001",  "gemini-2.0-flash-live-preview-04-09",
+   # A short description of the agent's purpose.
+   description="Agent to answer questions using Google Search.",
+   # Instructions to set the agent's behavior.
+   instruction="Answer the question using the Google Search tool.",
+   # Add google_search tool to perform grounding with Google search.
+   tools=[google_search],
+)
diff --git a/in_progress/streaming-test4/app/main.py b/in_progress/streaming-test4/app/main.py
new file mode 100644
index 000000000..bab2eb6a8
--- /dev/null
+++ b/in_progress/streaming-test4/app/main.py
@@ -0,0 +1,221 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import json
+import base64
+import warnings
+
+from pathlib import Path
+from dotenv import load_dotenv
+
+from google.genai.types import (
+    Part,
+    Content,
+    Blob,
+)
+
+from google.adk.runners import InMemoryRunner
+from google.adk.agents import LiveRequestQueue
+from google.adk.agents.run_config import RunConfig
+
+from fastapi import FastAPI, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse, StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+from google_search_agent.agent import root_agent
+
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+#
+# ADK Streaming
+#
+
+# Load Gemini API Key
+load_dotenv()
+
+APP_NAME = "ADK Streaming example"
+
+
+async def start_agent_session(user_id, is_audio=False):
+    """Starts an agent session"""
+
+    # Create a Runner
+    runner = InMemoryRunner(
+        app_name=APP_NAME,
+        agent=root_agent,
+    )
+
+    # Create a Session
+    session = await runner.session_service.create_session(
+        app_name=APP_NAME,
+        user_id=user_id,  # Replace with actual user ID
+    )
+
+    # Set response modality
+    modality = "AUDIO" if is_audio else "TEXT"
+    run_config = RunConfig(response_modalities=[modality])
+
+    # Create a LiveRequestQueue for this session
+    live_request_queue = LiveRequestQueue()
+
+    # Start agent session
+    live_events = runner.run_live(
+        session=session,
+        live_request_queue=live_request_queue,
+        run_config=run_config,
+    )
+    return live_events, live_request_queue
+
+
+async def agent_to_client_sse(live_events):
+    """Agent to client communication via SSE"""
+    async for event in live_events:
+        # If the turn complete or interrupted, send it
+        if event.turn_complete or event.interrupted:
+            message = {
+                "turn_complete": event.turn_complete,
+                "interrupted": event.interrupted,
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: {message}")
+            continue
+
+        # Read the Content and its first Part
+        part: Part = (
+            event.content and event.content.parts and event.content.parts[0]
+        )
+        if not part:
+            continue
+
+        # If it's audio, send Base64 encoded audio data
+        is_audio = part.inline_data and part.inline_data.mime_type.startswith("audio/pcm")
+        if is_audio:
+            audio_data = part.inline_data and part.inline_data.data
+            if audio_data:
+                message = {
+                    "mime_type": "audio/pcm",
+                    "data": base64.b64encode(audio_data).decode("ascii")
+                }
+                yield f"data: {json.dumps(message)}\n\n"
+                print(f"[AGENT TO CLIENT]: audio/pcm: {len(audio_data)} bytes.")
+                continue
+
+        # If it's text and a parial text, send it
+        if part.text and event.partial:
+            message = {
+                "mime_type": "text/plain",
+                "data": part.text
+            }
+            yield f"data: {json.dumps(message)}\n\n"
+            print(f"[AGENT TO CLIENT]: text/plain: {message}")
+
+
+#
+# FastAPI web app
+#
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+STATIC_DIR = Path("static")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+# Store active sessions
+active_sessions = {}
+
+
+@app.get("/")
+async def root():
+    """Serves the index.html"""
+    return FileResponse(os.path.join(STATIC_DIR, "index.html"))
+
+
+@app.get("/events/{user_id}")
+async def sse_endpoint(user_id: int, is_audio: str = "false"):
+    """SSE endpoint for agent to client communication"""
+
+    # Start agent session
+    user_id_str = str(user_id)
+    live_events, live_request_queue = await start_agent_session(user_id_str, is_audio == "true")
+
+    # Store the request queue for this user
+    active_sessions[user_id_str] = live_request_queue
+
+    print(f"Client #{user_id} connected via SSE, audio mode: {is_audio}")
+
+    def cleanup():
+        live_request_queue.close()
+        if user_id_str in active_sessions:
+            del active_sessions[user_id_str]
+        print(f"Client #{user_id} disconnected from SSE")
+
+    async def event_generator():
+        try:
+            async for data in agent_to_client_sse(live_events):
+                yield data
+        except Exception as e:
+            print(f"Error in SSE stream: {e}")
+        finally:
+            cleanup()
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Cache-Control"
+        }
+    )
+
+
+@app.post("/send/{user_id}")
+async def send_message_endpoint(user_id: int, request: Request):
+    """HTTP endpoint for client to agent communication"""
+
+    user_id_str = str(user_id)
+
+    # Get the live request queue for this user
+    live_request_queue = active_sessions.get(user_id_str)
+    if not live_request_queue:
+        return {"error": "Session not found"}
+
+    # Parse the message
+    message = await request.json()
+    mime_type = message["mime_type"]
+    data = message["data"]
+
+    # Send the message to the agent
+    if mime_type == "text/plain":
+        content = Content(role="user", parts=[Part.from_text(text=data)])
+        live_request_queue.send_content(content=content)
+        print(f"[CLIENT TO AGENT]: {data}")
+    elif mime_type == "audio/pcm":
+        decoded_data = base64.b64decode(data)
+        live_request_queue.send_realtime(Blob(data=decoded_data, mime_type=mime_type))
+        print(f"[CLIENT TO AGENT]: audio/pcm: {len(decoded_data)} bytes")
+    else:
+        return {"error": f"Mime type not supported: {mime_type}"}
+
+    return {"status": "sent"}
diff --git a/in_progress/streaming-test4/app/static/index.html b/in_progress/streaming-test4/app/static/index.html
new file mode 100644
index 000000000..2286c371e
--- /dev/null
+++ b/in_progress/streaming-test4/app/static/index.html
@@ -0,0 +1,23 @@
+<!doctype html>
+<html>
+  <head>
+    <title>ADK Streaming Test (Audio)</title>
+    <script src="/static/js/app.js" type="module"></script>
+  </head>
+
+  <body>
+    <h1>ADK Streaming Test</h1>
+    <div
+      id="messages"
+      style="height: 300px; overflow-y: auto; border: 1px solid black"></div>
+    <br />
+
+    <form id="messageForm">
+      <label for="message">Message:</label>
+      <input type="text" id="message" name="message" />
+      <button type="submit" id="sendButton" disabled>Send</button>
+      <button type="button" id="startAudioButton">Start Audio</button>
+    </form>
+  </body>
+
+</html>
diff --git a/in_progress/streaming-test4/app/static/js/app.js b/in_progress/streaming-test4/app/static/js/app.js
new file mode 100644
index 000000000..31f5986fb
--- /dev/null
+++ b/in_progress/streaming-test4/app/static/js/app.js
@@ -0,0 +1,218 @@
+/**
+ * app.js: JS code for the adk-streaming sample app.
+ */
+
+/**
+ * SSE (Server-Sent Events) handling
+ */
+
+// Connect the server with SSE
+const sessionId = Math.random().toString().substring(10);
+const sse_url =
+  "http://" + window.location.host + "/events/" + sessionId;
+const send_url =
+  "http://" + window.location.host + "/send/" + sessionId;
+let eventSource = null;
+let is_audio = false;
+
+// Get DOM elements
+const messageForm = document.getElementById("messageForm");
+const messageInput = document.getElementById("message");
+const messagesDiv = document.getElementById("messages");
+let currentMessageId = null;
+
+// SSE handlers
+function connectSSE() {
+  // Connect to SSE endpoint
+  eventSource = new EventSource(sse_url + "?is_audio=" + is_audio);
+
+  // Handle connection open
+  eventSource.onopen = function () {
+    // Connection opened messages
+    console.log("SSE connection opened.");
+    document.getElementById("messages").textContent = "Connection opened";
+
+    // Enable the Send button
+    document.getElementById("sendButton").disabled = false;
+    addSubmitHandler();
+  };
+
+  // Handle incoming messages
+  eventSource.onmessage = function (event) {
+    // Parse the incoming message
+    const message_from_server = JSON.parse(event.data);
+    console.log("[AGENT TO CLIENT] ", message_from_server);
+
+    // Check if the turn is complete
+    // if turn complete, add new message
+    if (
+      message_from_server.turn_complete &&
+      message_from_server.turn_complete == true
+    ) {
+      currentMessageId = null;
+      return;
+    }
+
+    // Check for interrupt message
+    if (
+      message_from_server.interrupted &&
+      message_from_server.interrupted === true
+    ) {
+      // Stop audio playback if it's playing
+      if (audioPlayerNode) {
+        audioPlayerNode.port.postMessage({ command: "endOfAudio" });
+      }
+      return;
+    }
+
+    // If it's audio, play it
+    if (message_from_server.mime_type == "audio/pcm" && audioPlayerNode) {
+      audioPlayerNode.port.postMessage(base64ToArray(message_from_server.data));
+    }
+
+    // If it's a text, print it
+    if (message_from_server.mime_type == "text/plain") {
+      // add a new message for a new turn
+      if (currentMessageId == null) {
+        currentMessageId = Math.random().toString(36).substring(7);
+        const message = document.createElement("p");
+        message.id = currentMessageId;
+        // Append the message element to the messagesDiv
+        messagesDiv.appendChild(message);
+      }
+
+      // Add message text to the existing message element
+      const message = document.getElementById(currentMessageId);
+      message.textContent += message_from_server.data;
+
+      // Scroll down to the bottom of the messagesDiv
+      messagesDiv.scrollTop = messagesDiv.scrollHeight;
+    }
+  };
+
+  // Handle connection close
+  eventSource.onerror = function (event) {
+    console.log("SSE connection error or closed.");
+    document.getElementById("sendButton").disabled = true;
+    document.getElementById("messages").textContent = "Connection closed";
+    eventSource.close();
+    setTimeout(function () {
+      console.log("Reconnecting...");
+      connectSSE();
+    }, 5000);
+  };
+}
+connectSSE();
+
+// Add submit handler to the form
+function addSubmitHandler() {
+  messageForm.onsubmit = function (e) {
+    e.preventDefault();
+    const message = messageInput.value;
+    if (message) {
+      const p = document.createElement("p");
+      p.textContent = "> " + message;
+      messagesDiv.appendChild(p);
+      messageInput.value = "";
+      sendMessage({
+        mime_type: "text/plain",
+        data: message,
+      });
+      console.log("[CLIENT TO AGENT] " + message);
+    }
+    return false;
+  };
+}
+
+// Send a message to the server via HTTP POST
+async function sendMessage(message) {
+  try {
+    const response = await fetch(send_url, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify(message)
+    });
+    
+    if (!response.ok) {
+      console.error('Failed to send message:', response.statusText);
+    }
+  } catch (error) {
+    console.error('Error sending message:', error);
+  }
+}
+
+// Decode Base64 data to Array
+function base64ToArray(base64) {
+  const binaryString = window.atob(base64);
+  const len = binaryString.length;
+  const bytes = new Uint8Array(len);
+  for (let i = 0; i < len; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
+
+/**
+ * Audio handling
+ */
+
+let audioPlayerNode;
+let audioPlayerContext;
+let audioRecorderNode;
+let audioRecorderContext;
+let micStream;
+
+// Import the audio worklets
+import { startAudioPlayerWorklet } from "./audio-player.js";
+import { startAudioRecorderWorklet } from "./audio-recorder.js";
+
+// Start audio
+function startAudio() {
+  // Start audio output
+  startAudioPlayerWorklet().then(([node, ctx]) => {
+    audioPlayerNode = node;
+    audioPlayerContext = ctx;
+  });
+  // Start audio input
+  startAudioRecorderWorklet(audioRecorderHandler).then(
+    ([node, ctx, stream]) => {
+      audioRecorderNode = node;
+      audioRecorderContext = ctx;
+      micStream = stream;
+    }
+  );
+}
+
+// Start the audio only when the user clicked the button
+// (due to the gesture requirement for the Web Audio API)
+const startAudioButton = document.getElementById("startAudioButton");
+startAudioButton.addEventListener("click", () => {
+  startAudioButton.disabled = true;
+  startAudio();
+  is_audio = true;
+  eventSource.close(); // close current connection
+  connectSSE(); // reconnect with the audio mode
+});
+
+// Audio recorder handler
+function audioRecorderHandler(pcmData) {
+  // Send the pcm data as base64
+  sendMessage({
+    mime_type: "audio/pcm",
+    data: arrayBufferToBase64(pcmData),
+  });
+  console.log("[CLIENT TO AGENT] sent %s bytes", pcmData.byteLength);
+}
+
+// Encode an array buffer with Base64
+function arrayBufferToBase64(buffer) {
+  let binary = "";
+  const bytes = new Uint8Array(buffer);
+  const len = bytes.byteLength;
+  for (let i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
diff --git a/in_progress/streaming-test4/app/static/js/audio-player.js b/in_progress/streaming-test4/app/static/js/audio-player.js
new file mode 100644
index 000000000..af890517e
--- /dev/null
+++ b/in_progress/streaming-test4/app/static/js/audio-player.js
@@ -0,0 +1,24 @@
+/**
+ * Audio Player Worklet
+ */
+
+export async function startAudioPlayerWorklet() {
+    // 1. Create an AudioContext
+    const audioContext = new AudioContext({
+        sampleRate: 24000
+    });
+    
+    
+    // 2. Load your custom processor code
+    const workletURL = new URL('./pcm-player-processor.js', import.meta.url);
+    await audioContext.audioWorklet.addModule(workletURL);
+    
+    // 3. Create an AudioWorkletNode   
+    const audioPlayerNode = new AudioWorkletNode(audioContext, 'pcm-player-processor');
+
+    // 4. Connect to the destination
+    audioPlayerNode.connect(audioContext.destination);
+
+    // The audioPlayerNode.port is how we send messages (audio data) to the processor
+    return [audioPlayerNode, audioContext];
+}
diff --git a/in_progress/streaming-test4/app/static/js/audio-recorder.js b/in_progress/streaming-test4/app/static/js/audio-recorder.js
new file mode 100644
index 000000000..82806e6dd
--- /dev/null
+++ b/in_progress/streaming-test4/app/static/js/audio-recorder.js
@@ -0,0 +1,58 @@
+/**
+ * Audio Recorder Worklet
+ */
+
+let micStream;
+
+export async function startAudioRecorderWorklet(audioRecorderHandler) {
+  // Create an AudioContext
+  const audioRecorderContext = new AudioContext({ sampleRate: 16000 });
+  console.log("AudioContext sample rate:", audioRecorderContext.sampleRate);
+
+  // Load the AudioWorklet module
+  const workletURL = new URL("./pcm-recorder-processor.js", import.meta.url);
+  await audioRecorderContext.audioWorklet.addModule(workletURL);
+
+  // Request access to the microphone
+  micStream = await navigator.mediaDevices.getUserMedia({
+    audio: { channelCount: 1 },
+  });
+  const source = audioRecorderContext.createMediaStreamSource(micStream);
+
+  // Create an AudioWorkletNode that uses the PCMProcessor
+  const audioRecorderNode = new AudioWorkletNode(
+    audioRecorderContext,
+    "pcm-recorder-processor"
+  );
+
+  // Connect the microphone source to the worklet.
+  source.connect(audioRecorderNode);
+  audioRecorderNode.port.onmessage = (event) => {
+    // Convert to 16-bit PCM
+    const pcmData = convertFloat32ToPCM(event.data);
+
+    // Send the PCM data to the handler.
+    audioRecorderHandler(pcmData);
+  };
+  return [audioRecorderNode, audioRecorderContext, micStream];
+}
+
+/**
+ * Stop the microphone.
+ */
+export function stopMicrophone(micStream) {
+  micStream.getTracks().forEach((track) => track.stop());
+  console.log("stopMicrophone(): Microphone stopped.");
+}
+
+// Convert Float32 samples to 16-bit PCM.
+function convertFloat32ToPCM(inputData) {
+  // Create an Int16Array of the same length.
+  const pcm16 = new Int16Array(inputData.length);
+  for (let i = 0; i < inputData.length; i++) {
+    // Multiply by 0x7fff (32767) to scale the float value to 16-bit PCM range.
+    pcm16[i] = inputData[i] * 0x7fff;
+  }
+  // Return the underlying ArrayBuffer.
+  return pcm16.buffer;
+}
diff --git a/in_progress/streaming-test4/app/static/js/pcm-player-processor.js b/in_progress/streaming-test4/app/static/js/pcm-player-processor.js
new file mode 100644
index 000000000..6d9dbf23b
--- /dev/null
+++ b/in_progress/streaming-test4/app/static/js/pcm-player-processor.js
@@ -0,0 +1,76 @@
+/**
+ * An audio worklet processor that stores the PCM audio data sent from the main thread
+ * to a buffer and plays it.
+ */
+class PCMPlayerProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+
+    // Init buffer
+    this.bufferSize = 24000 * 180;  // 24kHz x 180 seconds
+    this.buffer = new Float32Array(this.bufferSize);
+    this.writeIndex = 0;
+    this.readIndex = 0;
+
+    // Handle incoming messages from main thread
+    this.port.onmessage = (event) => {
+      // Reset the buffer when 'endOfAudio' message received
+      if (event.data.command === 'endOfAudio') {
+        this.readIndex = this.writeIndex; // Clear the buffer
+        console.log("endOfAudio received, clearing the buffer.");
+        return;
+      }
+
+      // Decode the base64 data to int16 array.
+      const int16Samples = new Int16Array(event.data);
+
+      // Add the audio data to the buffer
+      this._enqueue(int16Samples);
+    };
+  }
+
+  // Push incoming Int16 data into our ring buffer.
+  _enqueue(int16Samples) {
+    for (let i = 0; i < int16Samples.length; i++) {
+      // Convert 16-bit integer to float in [-1, 1]
+      const floatVal = int16Samples[i] / 32768;
+
+      // Store in ring buffer for left channel only (mono)
+      this.buffer[this.writeIndex] = floatVal;
+      this.writeIndex = (this.writeIndex + 1) % this.bufferSize;
+
+      // Overflow handling (overwrite oldest samples)
+      if (this.writeIndex === this.readIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+  }
+
+  // The system calls `process()` ~128 samples at a time (depending on the browser).
+  // We fill the output buffers from our ring buffer.
+  process(inputs, outputs, parameters) {
+
+    // Write a frame to the output
+    const output = outputs[0];
+    const framesPerBlock = output[0].length;
+    for (let frame = 0; frame < framesPerBlock; frame++) {
+
+      // Write the sample(s) into the output buffer
+      output[0][frame] = this.buffer[this.readIndex]; // left channel
+      if (output.length > 1) {
+        output[1][frame] = this.buffer[this.readIndex]; // right channel
+      }
+
+      // Move the read index forward unless underflowing
+      if (this.readIndex != this.writeIndex) {
+        this.readIndex = (this.readIndex + 1) % this.bufferSize;
+      }
+    }
+
+    // Returning true tells the system to keep the processor alive
+    return true;
+  }
+}
+
+registerProcessor('pcm-player-processor', PCMPlayerProcessor);
+
diff --git a/in_progress/streaming-test4/app/static/js/pcm-recorder-processor.js b/in_progress/streaming-test4/app/static/js/pcm-recorder-processor.js
new file mode 100644
index 000000000..b1c54bd31
--- /dev/null
+++ b/in_progress/streaming-test4/app/static/js/pcm-recorder-processor.js
@@ -0,0 +1,18 @@
+class PCMProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+  }
+
+  process(inputs, outputs, parameters) {
+    if (inputs.length > 0 && inputs[0].length > 0) {
+      // Use the first channel
+      const inputChannel = inputs[0][0];
+      // Copy the buffer to avoid issues with recycled memory
+      const inputCopy = new Float32Array(inputChannel);
+      this.port.postMessage(inputCopy);
+    }
+    return true;
+  }
+}
+
+registerProcessor("pcm-recorder-processor", PCMProcessor);
diff --git a/project-horizon-adk/.env.example b/project-horizon-adk/.env.example
new file mode 100644
index 000000000..d1dbde37e
--- /dev/null
+++ b/project-horizon-adk/.env.example
@@ -0,0 +1,36 @@
+# --- Google AI/Vertex AI Configuration ---
+# Choose ONE block (Google AI Studio or Vertex AI)
+
+# Option 1: Google AI Studio (Gemini API Key)
+GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY_HERE"
+GOOGLE_GENAI_USE_VERTEXAI="False"
+
+# Option 2: Vertex AI (using Application Default Credentials)
+# GOOGLE_GENAI_USE_VERTEXAI="True"
+# GOOGLE_CLOUD_PROJECT="your-gcp-project-id"
+# GOOGLE_CLOUD_LOCATION="your-gcp-region"
+
+# --- Service URLs ---
+# The URL where the Specialist Agent's A2A server will run
+STOCK_INFO_AGENT_A2A_SERVER_HOST=127.0.0.1
+STOCK_INFO_AGENT_A2A_SERVER_PORT=8001
+STOCK_INFO_AGENT_MODEL="gemini-2.0-flash-001"
+MOCK_STOCK_API=False
+FINNHUB_API_KEY=<YOUR_FINNHUB_API_KEY>
+
+
+# --- MCP Server Configuration ---
+STOCK_MCP_SERVER_PATH="mcp_servers/stock_mcp_server/server.py" # Relative path from specialist agent perspective
+
+# --- ADK Live Server Configuration ---
+# Host and port for the main ADK Live Server (FastAPI/WebSocket server that serves the UI).
+LIVE_SERVER_HOST=127.0.0.1
+LIVE_SERVER_PORT=8000 # MANDATORY: Port for the ADK Live Server (e.g., 8000). The UI will connect here.
+LIVE_SERVER_MODEL="gemini-2.0-flash-live-001"
+
+# Base URL for the StockInfoAgent (A2A Server). The Host Agent will append /.well-known/agent.json to this.
+# If you have multiple, comma-separate them: "http://localhost:8001,http://localhost:8002"
+SPECIALIST_AGENT_BASE_URLS="http://localhost:8001" # Points to StockInfoAgent A2A server
+
+# Optional: Logging Level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+LOG_LEVEL="INFO"
\ No newline at end of file
diff --git a/project-horizon-adk/.gitignore b/project-horizon-adk/.gitignore
new file mode 100644
index 000000000..70292a1cd
--- /dev/null
+++ b/project-horizon-adk/.gitignore
@@ -0,0 +1,180 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+_archive/
+.DS_Store
+concat_files.py
+concatenated_code.txt
+heiko-hotz-project-horizon.txt
diff --git a/project-horizon-adk/LICENSE b/project-horizon-adk/LICENSE
new file mode 100644
index 000000000..261eeb9e9
--- /dev/null
+++ b/project-horizon-adk/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
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+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
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+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
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+      Work and such Derivative Works in Source or Object form.
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+   3. Grant of Patent License. Subject to the terms and conditions of
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+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
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diff --git a/project-horizon-adk/README.md b/project-horizon-adk/README.md
new file mode 100644
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--- /dev/null
+++ b/project-horizon-adk/README.md
@@ -0,0 +1,76 @@
+# Project Horizon
+
+## Exploring the Future of Interoperable, Multimodal AI Agent Systems
+
+---
+![Project Horizon Banner](assets/project-horizon.png)
+---
+
+## Overview
+
+**Project Horizon explores the future of collaborative, multimodal AI agent systems.** It investigates how sophisticated, responsive, and interoperable agents can be built by integrating cutting-edge agent development frameworks and open communication protocols.
+
+![High-Level Architecture Diagram](assets/high-level-architecture.png)
+*(This diagram illustrates the general interaction patterns explored in Project Horizon, involving a Host Agent, Specialist Agents, and Tool Servers communicating via A2A and MCP.)*
+
+This project serves as a testbed and a collection of reference implementations for:
+
+*   Integrating diverse agent frameworks and tools like **Google's Agent Development Kit (ADK)** and the **Gemini Live API**.
+*   Implementing standardized communication patterns between independent agents using protocols like **Agent2Agent (A2A)**.
+*   Enabling agents to securely interact with external tools and data sources using protocols like the **Model Context Protocol (MCP)**.
+*   Exploring advanced agent architectures, design patterns, and evaluation techniques.
+
+**Project Horizon aims to capture and demonstrate the future of agent interactions, providing technical implementations, architectural blueprints, and practical design patterns for building the next generation of AI applications.**
+
+## Core Technologies
+
+This project showcases and explores the integration of several key technologies:
+
+*   **[Google Agent Development Kit (ADK)](https://google.github.io/adk-docs/)**: For building user-facing agents, orchestrating tasks, and managing multimodal communication.
+*   **[Gemini Live API](https://ai.google.dev/gemini-api/docs/live)**: Powers real-time, low-latency, bidirectional voice (and potentially video) interactions within ADK.
+*   **[Agent2Agent Protocol (A2A)](https://google.github.io/A2A/)**: An open standard for communication and task delegation between independent AI agents.
+    *   This repository includes examples using  a custom A2A implementation (for historical reference). An implementation with the  official **[google-a2a-python SDK](https://github.com/google/a2a-python)** is planned.
+*   **[Model Context Protocol (MCP)](https://modelcontextprotocol.io/)**: An open standard allowing agents to securely interact with dedicated "Tool Servers" that provide specific functionalities or data.
+
+## Example Applications
+
+This repository contains a growing collection of example applications, each demonstrating different aspects of multi-agent systems. Each example is self-contained within the `examples/` directory and includes its own specific setup instructions.
+
+### Available Examples:
+
+1.  **Synchronous Stock Lookup (Custom A2A/MCP Implementation)**
+    *   **Directory:** [`examples/stock_lookup_custom_a2a/`](./examples/stock_lookup_custom_a2a/)
+    *   **Description:** This is the original proof-of-concept demonstrating a real-time stock price lookup using ADK Live, a custom A2A implementation for delegation, and MCP over stdio for tool execution. It is preserved with its original dependencies for reproducibility.
+    *   **Technologies:** ADK Live, Custom A2A, Custom MCP Client, Finnhup API.
+
+*More examples demonstrating various agent architectures and protocol features will be added over time.*
+
+## Getting Started
+
+**Each example within the `examples/` directory has its own detailed `README.md` with specific setup and execution instructions.**
+
+## Future Plans / Roadmap
+
+This project establishes a foundation for exploring advanced multi-agent systems. Future iterations and new examples aim to explore:
+
+*   **A2A SDK:** Create an example with the officoal A2A Python SDK. 
+*   **Asynchronous A2A/MCP:** Implement non-blocking calls, leveraging A2A streaming or push notifications, especially for long-running tool operations within Specialist Agent -> MCP Server interactions using the official `a2a-sdk`.
+*   **Parallel A2A Delegation:** Modify Host Agents to delegate tasks to multiple Specialist Agents concurrently (e.g., fetch stock price AND company news simultaneously).
+*   **MCP over HTTP/SSE:** Explore MCP Tool Servers that use HTTP/SSE transport (instead of just stdio), requiring security considerations like authentication middleware.
+*   **Advanced State Management:** Utilize ADK's persistent SessionService options (e.g., Database, Vertex AI) and explore robust state sharing patterns between agents.
+*   **Error Handling & Resilience:** Implement more comprehensive error handling, retries, and fallback mechanisms across all communication layers in various examples.
+*   **Additional Specialist Agents & Tools:** Expand the system with more diverse agents (e.g., NewsAgent, PortfolioAgent, DocumentAnalysisAgent) and corresponding MCP Tool Servers or direct API tool integrations.
+*   **ADK Evaluation:** Integrate ADK's evaluation framework (`adk eval`) to measure performance and accuracy of different agent setups.
+*   **UI Enhancements:** Improve frontend UIs to better visualize multi-agent activity, tool calls, and potentially handle more complex inputs/outputs.
+*   **Agent Framework Interoperability:** Investigate and demonstrate patterns for integrating agents built with other frameworks (e.g., LangChain, CrewAI) into an A2A/MCP ecosystem, potentially via wrappers.
+*   **Security Considerations:** Further explore secure authentication and authorization mechanisms for A2A and MCP communication, especially for MCP over HTTP.
+
+## 📜 License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file.
+
+## 🤝 Contributing & Disclaimer
+
+This is a personal project by [Heiko Hotz](https://github.com/heiko-hotz) to explore Gemini capabilities and agent interoperability. Suggestions and feedback are welcome via Issues or Pull Requests.
+
+**This project is developed independently and does not reflect the views or efforts of Google.**
\ No newline at end of file
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diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/.env.example b/project-horizon-adk/examples/stock_lookup_custom_a2a/.env.example
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@@ -0,0 +1,36 @@
+# --- Google AI/Vertex AI Configuration ---
+# Choose ONE block (Google AI Studio or Vertex AI)
+
+# Option 1: Google AI Studio (Gemini API Key)
+GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY_HERE"
+GOOGLE_GENAI_USE_VERTEXAI="False"
+
+# Option 2: Vertex AI (using Application Default Credentials)
+# GOOGLE_GENAI_USE_VERTEXAI="True"
+# GOOGLE_CLOUD_PROJECT="your-gcp-project-id"
+# GOOGLE_CLOUD_LOCATION="your-gcp-region"
+
+# --- Service URLs ---
+# The URL where the Specialist Agent's A2A server will run
+STOCK_INFO_AGENT_A2A_SERVER_HOST=127.0.0.1
+STOCK_INFO_AGENT_A2A_SERVER_PORT=8001
+STOCK_INFO_AGENT_MODEL="gemini-2.0-flash-001"
+MOCK_STOCK_API=False
+FINNHUB_API_KEY=<YOUR_FINNHUB_API_KEY>
+
+
+# --- MCP Server Configuration ---
+STOCK_MCP_SERVER_PATH="mcp_servers/stock_mcp_server/server.py" # Relative path from specialist agent perspective
+
+# --- ADK Live Server Configuration ---
+# Host and port for the main ADK Live Server (FastAPI/WebSocket server that serves the UI).
+LIVE_SERVER_HOST=127.0.0.1
+LIVE_SERVER_PORT=8000 # MANDATORY: Port for the ADK Live Server (e.g., 8000). The UI will connect here.
+LIVE_SERVER_MODEL="gemini-2.0-flash-live-001"
+
+# Base URL for the StockInfoAgent (A2A Server). The Host Agent will append /.well-known/agent.json to this.
+# If you have multiple, comma-separate them: "http://localhost:8001,http://localhost:8002"
+SPECIALIST_AGENT_BASE_URLS="http://localhost:8001" # Points to StockInfoAgent A2A server
+
+# Optional: Logging Level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
+LOG_LEVEL="INFO"
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/GETTING_STARTED.md b/project-horizon-adk/examples/stock_lookup_custom_a2a/GETTING_STARTED.md
new file mode 100644
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+# Getting Started
+
+This guide provides detailed instructions on how to set up, run, and test the various components of this specific Project Horizon example: the Synchronous Stock Lookup.
+
+## 1. Running and Testing the Stock MCP Server
+
+The Stock MCP Server (`StockToolServer`) is responsible for fetching stock price information using the `finnhub` library (previously used `yfinance`). It communicates using the Model Context Protocol (MCP) over standard input/output (stdio).
+
+### Prerequisites
+
+*   Ensure you have followed the main project [Installation steps](../README.md#overall-setup-for-this-example) from the example's main README.md located in this folder, especially installing dependencies from `requirements.txt`. This will make the `mcp` command-line tools available.
+*   Activate your Python virtual environment:
+    ```bash
+    source .venv/bin/activate  # Linux/macOS
+    # .venv\Scripts\activate  # Windows
+    ```
+*   **Finnhub API Key (Optional but Recommended):** To get real stock data, you'll need a Finnhub API key. If not provided, the server will use mock data.
+    * Register for a free API key at [Finnhub.io](https://finnhub.io/)
+    * Add the key to your `.env` file (located in this `examples/stock_lookup_custom_a2a/` directory): `FINNHUB_API_KEY=your_api_key_here`
+
+### A. Running the Stock MCP Server Independently
+
+While the Specialist Agent normally starts this server as a subprocess, you can run it directly for testing or development.
+
+1.  **Navigate to the example directory:**
+    ```bash
+    *(This command assumes you are running from this example's directory: examples/stock_lookup_custom_a2a/)*
+    ```
+2.  **Run the server script:**
+    ```bash
+    python mcp_servers/stock_mcp_server/server.py
+    ```
+    If successful, the server will start and wait for MCP requests on its standard input. You won't see any immediate output until it receives a request. To stop it, you'd typically press `Ctrl+C` if you ran it directly in a terminal, but for testing with `mcp inspector`, the inspector will manage its lifecycle.
+
+### B. Testing the Stock MCP Server with `mcp inspector`
+
+The `mcp dev` command is the recommended way to test your Python-based MCP server. It starts your server script and concurrently launches the `@modelcontextprotocol/inspector`, a web-based UI for interacting with MCP servers.
+
+**Additional Prerequisites for `mcp dev` / `@modelcontextprotocol/inspector`:**
+*   **Node.js and npm/npx:** The `@modelcontextprotocol/inspector` is an npm package. `mcp dev` will typically handle its download via `npx` if it's not already available. Ensure you have Node.js installed, which includes npm (Node Package Manager) and npx (Node Package Execute).
+
+1.  **Open a terminal** in this example's directory (`examples/stock_lookup_custom_a2a/`), with your Python virtual environment activated.
+
+2.  **Run `mcp dev`:**
+    Execute the following command, pointing to your MCP server script:
+    ```bash
+    mcp dev mcp_servers/stock_mcp_server/server.py
+    ```
+    *   **First-time use:** `mcp dev` (via `npx`) might ask for permission to download and install `@modelcontextprotocol/inspector` if it's the first time or if there's an update. For example:
+        ```
+        Need to install the following packages:
+        @modelcontextprotocol/inspector@0.12.0
+        Ok to proceed? (y)
+        ```
+        Type `y` and press Enter.
+    *   Once started, you will see output indicating your Python server is running, a proxy server is operational, and the URL for the inspector. For example:
+        ```
+        INFO - MCP_SERVER - Initializing FastMCP server...
+        ⚙️ Proxy server listening on port 6277
+        🔍 MCP Inspector is up and running at http://127.0.0.1:6274 🚀
+        ```
+        *(Note: Port numbers and exact logging may vary)*
+
+    *(Advanced Note: The `@modelcontextprotocol/inspector` can also be invoked directly using `npx @modelcontextprotocol/inspector python mcp_servers/stock_mcp_server/server.py`. However, for this project, using `mcp dev` is simpler as it manages both the server and inspector.)*
+
+3.  **Using the MCP Inspector Web UI:**
+    *   Open the Inspector URL provided in your terminal (e.g., `http://127.0.0.1:6274`). The `mcp dev` command should automatically connect the Inspector to your running `StockToolServer`.
+    *   **Initial Connection:**
+        *   Ideally, you'll be taken directly to a connected interface. However, if you were to encounter an initial screen asking you to manually configure the connection (similar to the image below), this would typically be for manual setups. With `mcp dev`, this screen (`../../assets/mcp-inspector-1.png`) should be bypassed.
+
+            *Initial MCP Inspector Connection Screen (manual setup):*
+            [![MCP Inspector Initial Connection Screen](../../assets/mcp-inspector-1.png)](../../assets/mcp-inspector-1.png)
+
+    *   **Navigating the Connected Inspector:**
+        *   Once connected, you'll see an interface with a connection pane on the left (showing server status, logs, and history) and main interaction tabs in the center.
+        *   The **Resources Tab** (shown below, `../../assets/mcp-inspector-2.png`) lists data resources from the server. For the `StockToolServer`, which mainly provides tools, this tab might not show much specific content but illustrates the general layout once connected. The "History" section at the bottom will show initialization calls.
+
+            *MCP Inspector - Resources Tab (example of a connected view):*
+            [![MCP Inspector Resources Tab](../../assets/mcp-inspector-2.png)](../../assets/mcp-inspector-2.png)
+
+        *   **Testing with the Tools Tab:**
+            *   The primary tab for testing the `StockToolServer` is the **Tools Tab**.
+            *   Select it, and you should see the `get_current_stock_price` tool listed.
+            *   Click on `get_current_stock_price`. You can then input a `ticker_symbol` (e.g., "AAPL") and click "Run Tool", as illustrated below (`../../assets/mcp-inspector-3.png`).
+            *   The results will appear in the "Tool Result" section, showing the JSON response from the server.
+
+            *MCP Inspector - Tools Tab (calling get_current_stock_price and viewing results):*
+            [![MCP Inspector Tool Call and Result](../../assets/mcp-inspector-3.png)](../../assets/mcp-inspector-3.png)
+
+    *   **Further Details:**
+        *   The MCP Inspector has many other features for in-depth debugging and interaction with MCP servers, including detailed views for prompts, server notifications, and more.
+        *   For a comprehensive understanding of all its capabilities, please refer to the official **[MCP Inspector Documentation](https://modelcontextprotocol.io/docs/tools/inspector)**.
+
+4.  **Goal of Testing:**
+    Verify that the `get_current_stock_price` tool functions correctly. Call it with a valid stock ticker and check if the response contains the expected data structure (price, currency, symbol). Test with a mock symbol if `MOCK_STOCK_API` is enabled to see the mock data.
+
+5.  **Stopping the Server and Inspector:**
+    *   To stop the `StockToolServer` and the `mcp dev` proxy/inspector, go back to your terminal where you ran the `mcp dev` command and press `Ctrl+C`.
+
+This confirms that your `StockToolServer` is working correctly and can respond to MCP requests via the `mcp dev` environment. The next steps in this guide will cover running the other components that interact with this server. 
+
+## 2. Running and Testing the Specialist Agent (StockInfoAgent A2A Server)
+
+The `StockInfoAgent` is a specialist agent responsible for handling stock information requests. It acts as an Agent-to-Agent (A2A) server, receiving tasks from the main Host Agent. Internally, it uses its own ADK agent instance, which in turn utilizes the `StockToolServer` (via MCP) to fetch actual stock data. This specialist agent launches and manages the `StockToolServer` as a subprocess.
+
+### Prerequisites
+
+*   **Virtual Environment:** Ensure your Python virtual environment (`.venv`) is activated:
+    ```bash
+    source .venv/bin/activate  # Linux/macOS
+    # .venv\Scripts\activate  # Windows
+    ```
+*   **`.env` Configuration:** Your `.env` file in this example's directory (`examples/stock_lookup_custom_a2a/`) must be correctly configured with the following key variables for the `StockInfoAgent`:
+    *   `STOCK_INFO_AGENT_A2A_SERVER_HOST`: The host address the A2A server will bind to (e.g., `127.0.0.1`).
+    *   `STOCK_INFO_AGENT_A2A_SERVER_PORT`: The port the A2A server will listen on (e.g., `8001`).
+    *   `STOCK_INFO_AGENT_MODEL`: The Gemini model to be used by the ADK agent within the specialist (e.g., `gemini-1.0-pro`).
+    *   `STOCK_MCP_SERVER_PATH`: The relative path to the Stock MCP Server script (e.g., `mcp_servers/stock_mcp_server/server.py` relative to this example's directory), which this agent will run as a subprocess.
+    *   `FINNHUB_API_KEY`: (Optional but recommended) Your Finnhub API key for real stock data. If not provided, mock data will be used.
+    *   `GOOGLE_GENAI_USE_VERTEXAI`, and either `GOOGLE_API_KEY` (if `False`) or `GOOGLE_CLOUD_PROJECT`/`GOOGLE_CLOUD_LOCATION` (if `True`): For authenticating with the Gemini API for the internal ADK agent.
+    *   *(Refer to `.env.example` for the full list and descriptions.)*
+*   **MCP Server Not Running Separately:** You do not need to run the `StockToolServer` (from Section 1) independently. The `StockInfoAgent` will start it automatically as a subprocess.
+
+### A. Running the Specialist Agent
+
+1.  **Ensure you are in this example's directory** (`examples/stock_lookup_custom_a2a/`).
+2.  **Run the agent script:**
+    ```bash
+    python -m specialist_agents.stock_info_agent
+    ```
+3.  **Expected Output:**
+    You should see log messages indicating the A2A server is starting, followed by confirmation that it's listening on the configured host and port. It will also typically log the initialization of its internal ADK agent and the launching of the `StockToolServer` subprocess.
+    Key messages to look for (details and paths may vary):
+    ```log
+    INFO:__main__:Starting StockInfoAgent A2A Server...
+    INFO:__main__:  Host: 127.0.0.1
+    INFO:__main__:  Port: 8001
+    INFO:__main__:  MCP Server Script Path: mcp_servers/stock_mcp_server/server.py
+    INFO:specialist_agents.stock_info_agent.task_manager:StockInfoTaskManager initialized (ADK Runner Mode). Will use MCP server at: /path/to/your/project-horizon/examples/stock_lookup_custom_a2a/mcp_servers/stock_mcp_server/server.py
+    INFO:uvicorn:Uvicorn running on http://127.0.0.1:8001 (Press CTRL+C to quit)
+    ```
+
+### B. Testing the Specialist Agent
+
+There are two main ways to test if the `StockInfoAgent` A2A server is running and correctly configured:
+1.  Fetching its Agent Card via a web browser.
+2.  Using the provided Python test client script for a functional test.
+
+#### B.1. Fetching the Agent Card via Web Browser
+
+The Agent Card provides metadata about the agent and its capabilities. It's located at a standard `.well-known` path.
+
+1.  Ensure the `StockInfoAgent` (from section 2.A) is running in a separate terminal.
+2.  **Open your web browser**.
+3.  **Navigate to the Agent Card URL:**
+    Use the host and port configured in your `.env` file for `STOCK_INFO_AGENT_A2A_SERVER_HOST` and `STOCK_INFO_AGENT_A2A_SERVER_PORT`.
+    The standard path is `/.well-known/agent.json`.
+    For example, if your agent is running on `http://127.0.0.1:8001`, the URL will be:
+    ```
+    http://127.0.0.1:8001/.well-known/agent.json
+    ```
+4.  **Expected Output:**
+    Your browser should display a JSON response containing the agent's card. The exact content will depend on its configuration, but it will look something like this:
+    ```json
+    {
+      "name": "StockInfoAgent",
+      "description": "Provides current stock price information using the yfinance library via MCP.",
+      "url": "http://127.0.0.1:8001",
+      "provider": {
+        "organization": "ProjectHorizon"
+      },
+      "version": "1.0.0",
+      "capabilities": {
+        "streaming": false,
+        "pushNotifications": false,
+        "stateTransitionHistory": false
+      },
+      "defaultInputModes": [
+        "text/plain"
+      ],
+      "defaultOutputModes": [
+        "application/json"
+      ],
+      "skills": [
+        {
+          "id": "get_stock_price_skill",
+          "name": "Get Stock Price",
+          "description": "Retrieves the current price and currency for a given stock ticker symbol.",
+          "tags": [
+            "finance",
+            "stocks",
+            "price lookup"
+          ],
+          "examples": [
+            "What is the price of GOOGL?",
+            "Stock price for MSFT"
+          ],
+          "outputModes": [
+            "application/json"
+          ]
+        }
+      ]
+    }
+    ```
+    *   The presence of this JSON response in your browser indicates that the `StockInfoAgent` A2A server is running and accessible.
+    *   This card is what the Host Agent will fetch during its own startup to discover and learn how to communicate with this specialist agent.
+
+#### B.2. Using the A2A Test Client Script (`tests/test_a2a_stock_client.py`)
+
+For a more functional test that simulates sending a task to the `StockInfoAgent`, you can use the `test_a2a_stock_client.py` script. This script acts as a dedicated test harness, performing an end-to-end check of the `StockInfoAgent`'s ability to receive a task, process it (which involves invoking its internal tool via MCP), and return a structured result.
+
+**What the script does:**
+
+1.  **Initialization:** 
+    *   Takes a stock ticker symbol (e.g., `GOOGL`) as a mandatory command-line argument.
+    *   Optionally accepts `--url` for the `StockInfoAgent` (defaulting to `http://127.0.0.1:8001`) and a `--session` ID (a new one is generated if not provided).
+    *   It ensures this example's directory or necessary parent directories are in Python's `sys.path` to correctly import its local `common_impl` module.
+2.  **A2A Client Setup:** 
+    *   Creates an instance of `A2AClient` from this example's `common_impl.client.client`, configured to target the `StockInfoAgent`'s URL.
+3.  **Task Construction:**
+    *   Generates a unique `task_id`.
+    *   Constructs an A2A `TaskSendParams` object. This is the standard way to define a task in A2A. The payload includes:
+        *   The `task_id` and `sessionId`.
+        *   A `Message` object with the `role` set to "user" and the provided stock symbol embedded within a `TextPart`. This simulates how the Host Agent would phrase the query to the specialist.
+4.  **Sending the Task:**
+    *   Sends the `TaskSendParams` (as a JSON payload) via an HTTP POST request to the `StockInfoAgent`'s `/tasks/send` A2A endpoint.
+5.  **Processing the Response:**
+    *   Asynchronously awaits the `SendTaskResponse` from the `StockInfoAgent`.
+    *   Checks if the response indicates an error at the A2A level.
+    *   If no immediate error, it inspects the `result` field of the response, which contains the final `Task` object from the specialist.
+6.  **Verifying Task Outcome and Artifacts:**
+    *   Logs the final `state` of the task (e.g., `COMPLETED`, `FAILED`).
+    *   If the task `COMPLETED` successfully, it iterates through any `artifacts` attached to the task result. The `StockInfoAgent` is expected to return an artifact (e.g., named `stock_data` or similar) containing a `DataPart` with the actual stock price, currency, and symbol as a JSON object.
+    *   The script then logs the content of these artifacts, allowing you to verify the data.
+7.  **Error Handling:**
+    *   Includes `try-except` blocks to gracefully handle potential issues like `ConnectionRefusedError` (if the agent isn't running), `A2AClientHTTPError` (for other HTTP issues), `A2AClientJSONError` (if the server's response is not valid JSON), and other unexpected exceptions.
+
+**Why this script is useful:**
+
+*   **Beyond a simple ping:** Unlike just fetching the Agent Card, this script tests the full request-processing pipeline of the `StockInfoAgent`.
+*   **End-to-end validation:** It verifies that the agent can: 
+    *   Correctly receive and parse an A2A task.
+    *   Trigger its internal ADK agent logic.
+    *   Successfully manage and communicate with its `StockToolServer` MCP subprocess.
+    *   Receive the data back from the MCP server.
+    *   Package the final result into the correct A2A `Artifact` format and send it back.
+*   **Independent testing:** It allows you to confirm the `StockInfoAgent` is fully operational before attempting to integrate it with the main Host Agent and the UI, simplifying troubleshooting.
+
+**How to run it:**
+
+1.  Ensure the `StockInfoAgent` (from section 2.A) is running in a separate terminal.
+2.  **Open a new terminal** in this example's directory (`examples/stock_lookup_custom_a2a/`), with your Python virtual environment activated:
+    ```bash
+    source .venv/bin/activate  # Linux/macOS
+    # .venv\Scripts\activate  # Windows
+    ```
+3.  **Run the test script:**
+    You need to provide a stock symbol as an argument. You can also specify the agent's URL and a session ID if needed (though defaults are provided).
+    ```bash
+    python tests/test_a2a_stock_client.py <STOCK_SYMBOL>
+    ```
+    For example, to test with the symbol "GOOGL":
+    ```bash
+    python tests/test_a2a_stock_client.py GOOGL
+    ```
+    To specify a different URL (if your agent isn't running on `http://127.0.0.1:8001`):
+    ```bash
+    python tests/test_a2a_stock_client.py GOOGL --url http://<your_agent_host>:<your_agent_port>
+    ```
+4.  **Expected Output:**
+    The script will log its actions, including the generated task ID, session ID, and the full response from the A2A server. If successful, you should see logs indicating the task completed and the stock data artifact:
+    ```
+    INFO:     A2A_TEST_CLIENT - No session ID provided, generated one: test_session_xxxxxxxx
+    INFO:     A2A_TEST_CLIENT - Creating A2A task yyyyyyyyyyyyyyyy for session test_session_xxxxxxxx with symbol 'GOOGL'
+    INFO:     A2A_TEST_CLIENT - Sending task to A2A server at http://127.0.0.1:8001...
+    INFO:     A2A_TEST_CLIENT - Received response from A2A server.
+    INFO:     A2A_TEST_CLIENT - Task yyyyyyyyyyyyyyyy final state: COMPLETED
+    INFO:     A2A_TEST_CLIENT - Task completed successfully.
+    INFO:     A2A_TEST_CLIENT - Artifacts received:
+    INFO:     A2A_TEST_CLIENT - - Artifact Name: stock_data
+    INFO:     A2A_TEST_CLIENT -   DataPart Content: {
+      "price": 2735.70, // Example price
+      "currency": "USD",
+      "symbol": "GOOGL"
+    }
+    ```
+    If the agent is not running or there's an issue, the script will output error messages (e.g., connection refused, HTTP error, task failed).
+
+    **Note on Mock Data:** If you haven't provided a Finnhub API key in your `.env` file, the tool will return mock data (price: 123.45). This is useful for testing without an API key, but not for production use.
+
+4.  **Stopping the Specialist Agent:**
+    *   Go back to the terminal where the `StockInfoAgent` is running and press `Ctrl+C`.
+    *   This will also terminate the `StockToolServer` subprocess that it launched.
+
+If you can successfully retrieve the Agent Card, your `StockInfoAgent` is likely set up correctly and ready to receive tasks. The next step in a full system test would be to run the Host Agent, which will interact with this Specialist Agent.
+
+## 3. Running the ADK Live Server (Host Agent) and Performing an End-to-End Test
+
+This final section explains how to run the main ADK Live Server, which hosts the "Host Agent". This agent orchestrates the user interaction (including voice via the Gemini Live API) and delegates tasks to specialist agents like the `StockInfoAgent` using A2A communication. This section will guide you through an end-to-end test of the entire system.
+
+### Prerequisites
+
+1.  **Specialist Agent MUST Be Running:**
+    *   Before starting the ADK Live Server (Host Agent), ensure that the `StockInfoAgent` (Specialist Agent from Section 2.A) is already running in its own terminal. The Host Agent needs to discover and communicate with it.
+2.  **Virtual Environment:**
+    *   Ensure your Python virtual environment (`.venv`) is activated in the terminal where you will run the Live Server:
+        ```bash
+        source .venv/bin/activate  # Linux/macOS
+        # .venv\Scripts\activate  # Windows
+        ```
+3.  **`.env` Configuration:**
+    *   Your `.env` file in this example's directory (`examples/stock_lookup_custom_a2a/`) must be correctly configured. Key variables for the ADK Live Server / Host Agent include:
+        *   `LIVE_SERVER_HOST`: The host for the ADK Live Server (e.g., `127.0.0.1`).
+        *   `LIVE_SERVER_PORT`: The port for the ADK Live Server (e.g., `8000`).
+        *   `LIVE_SERVER_MODEL`: The Gemini model compatible with the Live API for the Host Agent (e.g., `gemini-1.5-flash-latest` or a specific Live-enabled version).
+        *   `SPECIALIST_AGENT_BASE_URLS`: **Crucial.** A comma-separated list of base URLs for all specialist A2A agents that the Host Agent should discover. For the `StockInfoAgent`, if it's running on `http://127.0.0.1:8001`, this list should include that URL (e.g., `SPECIALIST_AGENT_BASE_URLS=http://127.0.0.1:8001`).
+        *   `GOOGLE_GENAI_USE_VERTEXAI`, and corresponding `GOOGLE_API_KEY` or Google Cloud project details: For the Host Agent's Gemini API access.
+        *   *(Refer to `.env.example` for the full list and descriptions.)*
+
+### A. Running the ADK Live Server (Host Agent)
+
+1.  **Ensure you are in this example's directory** (`examples/stock_lookup_custom_a2a/`).
+2.  **Run the Live Server script:**
+    ```bash
+    python -m app.live_server
+    ```
+3.  **Expected Terminal Output:**
+    You should see a series of log messages indicating:
+    *   FastAPI application startup.
+    *   Initialization of the ADK system (`initialize_adk_system()`).
+    *   The Host Agent (`HostAgent`) being created.
+    *   **Specialist Agent Discovery:** Crucially, logs showing the Host Agent attempting to fetch Agent Cards from the URLs specified in `SPECIALIST_AGENT_BASE_URLS`. For the `StockInfoAgent`, you should see messages indicating it successfully fetched and processed its card (e.g., `INFO:host_agent.tools:Discovered specialist agent: StockInfoAgent_A2A from http://127.0.0.1:8001/.well-known/agent.json`).
+    *   The dynamic system prompt for the Host Agent being populated with information about discovered tools/specialists.
+    *   Uvicorn server starting and listening on the configured `LIVE_SERVER_HOST` and `LIVE_SERVER_PORT`.
+    Example (key messages, details may vary):
+    ```log
+    INFO:     Started server process [xxxxx]
+    INFO:     Waiting for application startup.
+    INFO:host_agent.agent:--- Loaded Environment Variables (HostAgent) ---
+    INFO:app.live_server:Initializing ADK system...
+    INFO:host_agent.agent:Creating HostAgent...
+    INFO:host_agent.tools:Attempting to discover specialist agents from: ['http://127.0.0.1:8001']
+    INFO:host_agent.tools:Fetching agent card from http://127.0.0.1:8001/.well-known/agent.json
+    INFO:host_agent.tools:Discovered specialist agent: StockInfoAgent (StockInfoAgent_A2A) from http://127.0.0.1:8001/.well-known/agent.json
+    INFO:host_agent.tools:Successfully discovered 1 specialist agents: ['StockInfoAgent']
+    INFO:host_agent.agent:HostAgent created with dynamic system instructions including 1 discovered specialists.
+    INFO:app.live_server:ADK system initialized successfully.
+    INFO:     Application startup complete.
+    INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
+    ```
+    If you see errors related to specialist agent discovery, double-check that the `StockInfoAgent` is running and that `SPECIALIST_AGENT_BASE_URLS` in your `.env` file is correct.
+
+### B. Performing an End-to-End Test via the UI
+
+1.  **Access the Web UI:**
+    Open your web browser and navigate to the address of your ADK Live Server (e.g., `http://127.0.0.1:8000`, based on your `LIVE_SERVER_HOST` and `LIVE_SERVER_PORT` settings).
+2.  **Interact with the Host Agent:**
+    *   Click the "Connect" button on the webpage to establish a WebSocket connection with the ADK Live Server.
+    *   Click the microphone icon. Your browser may ask for permission to use your microphone; allow it.
+    *   Speak your request clearly, for example: "What is the current price of Microsoft?" or "Stock price for AAPL".
+    *   Click the microphone icon again to stop recording and send the audio to the server.
+3.  **Observe the Result:**
+    *   **Audio Response:** You should hear the Host Agent respond with the stock price information in a spoken voice.
+    *   **Terminal Logs:** Check the terminals where both the `app.live_server` (Host Agent) and `specialist_agents.stock_info_agent` (Specialist Agent) are running.
+        *   **Host Agent Logs:** You should see activity related to receiving your audio, transcribing it, the LLM deciding to use the `delegate_task_to_specialist` tool, the A2A request being sent to the `StockInfoAgent`, and the A2A response being received.
+        *   **Specialist Agent Logs:** You should see it receiving an A2A task, logs from its internal ADK agent processing the request (which includes launching/communicating with its `StockToolServer` MCP subprocess), and sending the A2A task response back.
+    This confirms the entire end-to-end flow: UI -> ADK Live Server (Host Agent) -> A2A -> Specialist Agent -> MCP -> `yfinance` -> MCP -> Specialist Agent -> A2A -> Host Agent -> UI (audio).
+
+### C. Stopping the System
+
+1.  **Stop the ADK Live Server (Host Agent):**
+    *   Go to the terminal where `app.live_server` is running and press `Ctrl+C`.
+2.  **Stop the Specialist Agent:**
+    *   If you are finished testing, go to the terminal where `specialist_agents.stock_info_agent` is running and press `Ctrl+C`. This will also stop the `StockToolServer` subprocess it was managing.
+3.  **Stop the MCP Inspector (if running):**
+    *   If you still have `mcp dev` (from Section 1) running in a terminal, press `Ctrl+C` there as well.
+
+Congratulations! If you've reached this point and successfully performed an end-to-end test, you have the core Project Horizon system up and running. 
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/README.md b/project-horizon-adk/examples/stock_lookup_custom_a2a/README.md
new file mode 100644
index 000000000..e3be88d52
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/README.md
@@ -0,0 +1,129 @@
+# Example: Synchronous Stock Price Lookup (Custom A2A/MCP)
+
+**This example demonstrates a real-time stock price lookup using Google's Agent Development Kit (ADK) with the Gemini Live API, a custom A2A (Agent2Agent) protocol implementation for delegation, and a custom MCP (Model Context Protocol) client for tool execution over stdio.**
+
+**Note:** This example uses a custom implementation of A2A and MCP client/server components and has its dependencies pinned in its local `requirements.txt` file for reproducibility. Newer examples in this repository will utilize the official `a2a-sdk`.
+
+---
+![Project Horizon Banner](../../assets/project-horizon.png) 
+<!-- Path to asset is now relative to the example's README location -->
+---
+
+## Overview of this Example
+
+This proof-of-concept demonstrates the powerful synergy between ADK (leveraging the Gemini Live API), a custom A2A protocol layer, and a custom MCP layer.
+
+![High-Level Architecture](../../assets/high-level-architecture.png) 
+<!-- Adjust path if this image is specific or general -->
+
+This specific example focuses on:
+
+*   Integrating ADK for real-time, interactive user experiences (voice, text).
+*   A bespoke implementation for standardized communication between a "Host Agent" and a "Specialist Agent" (A2A).
+*   A bespoke implementation for enabling a Specialist Agent to securely interact with an external tool (an MCP Server providing stock data).
+
+## Scenario: Real-Time Stock Price Lookup (v0.1.1 - Synchronous)
+
+The flow demonstrated is a synchronous, end-to-end request for a stock price.
+
+**User Interaction:**
+The user interacts with the Host Agent via a simple web UI, primarily using voice input. They ask for the current price of a stock (e.g., *"What is the price of Microsoft?"*).
+
+**Execution Flow:**
+
+0.  **Server Startup & Initialization (FastAPI & ADK):**
+    *   When `app/live_server.py` (within this example directory) is started, the FastAPI application initializes.
+    *   `startup_event` calls `initialize_adk_system()`.
+        *   This initializes the `HostAgent` and discovers the `StockInfoAgent` using its Agent Card (fetched via HTTP).
+        *   The global ADK `Runner` instance is initialized with this configured `HostAgent`.
+    *   The FastAPI server then accepts WebSocket connections.
+
+1.  **Input (ADK Live):** Frontend captures audio, streams to `app/live_server.py`.
+2.  **Host Agent Processing (ADK):** `HostAgent` (Gemini Live model) processes input, identifies intent (stock lookup) and ticker (e.g., "MSFT").
+3.  **Tool Invocation (ADK -> Custom A2A Client):** `HostAgent` uses `delegate_task_to_specialist` tool with specialist name "StockInfoAgent" and "MSFT".
+4.  **Delegation (Custom A2A Request):** The tool (in `host_agent/tools.py`) acts as an A2A client, looks up "StockInfoAgent", gets its A2A endpoint from the discovered Agent Card, and sends an A2A task via HTTP POST to the `StockInfoAgent`.
+5.  **Task Reception (Custom A2A Server):** `StockInfoAgent` (`specialist_agents/stock_info_agent/__main__.py`), as an A2A Server, receives and parses the task.
+6.  **ADK Agent Execution (within A2A Server):** `StockInfoTaskManager` instantiates its ADK `stock_info_agent`. This involves launching the `StockToolServer` (`mcp_servers/stock_mcp_server/server.py`) as a subprocess (managed by `MCPToolset` from ADK).
+7.  **MCP Tool Call (via ADK Agent & Toolset):** The `stock_info_agent` uses its `get_current_stock_price` tool (loaded via MCP). The `MCPToolset` sends an MCP `tools/call` request to the `StockToolServer` subprocess via stdio.
+8.  **Tool Execution (MCP Server):** `StockToolServer` (FastMCP) receives the request, uses `yfinance` for stock data.
+9.  **MCP Response (stdio):** `StockToolServer` sends the result back via stdout.
+10. **ADK Agent Receives Tool Result:** `MCPToolset` gets the response. `StockInfoTaskManager` extracts the data. The `StockToolServer` subprocess is terminated.
+11. **Task Completion (Custom A2A Response):** `StockInfoTaskManager` formats the data into an A2A Artifact and sends it as the HTTP response to the Host Agent.
+12. **Tool Result (Custom A2A Client -> ADK):** `delegate_task_to_specialist` tool in `HostAgent` gets the A2A response.
+13. **Final Response Generation (ADK):** `HostAgent` formulates a natural language response.
+14. **Audio Output (ADK Live):** ADK synthesizes text to audio.
+15. **Streaming Output (ADK Live -> UI):** Audio streams to the UI.
+
+![Flow Diagram specific to this example](../../assets/flow.png)
+<!-- Ensure this path is correct, or if flow.png is general, it's fine. -->
+
+**This flow validates the core integration points between ADK Live, the custom A2A layer (with dynamic agent card discovery), and the custom MCP layer (over stdio) in a synchronous workflow.**
+
+## Getting Started with THIS Example
+
+### Prerequisites
+
+*   Python (>= 3.9, due to MCP library requirements for this example).
+*   `pip` (Python package installer).
+*   Git.
+*   Access to Google Cloud / Google AI Studio (for Gemini API Key/Credentials).
+*   An API key for a Gemini model supporting the Live API (e.g., `gemini-2.0-flash-live-001`).
+*   Familiarity with `asyncio` and FastAPI.
+
+### Setup Steps
+
+1.  **Navigate to this Example Directory:**
+    If you are at the root of the `project-horizon` repository, navigate here:
+    ```bash
+    cd examples/stock_lookup_custom_a2a
+    ```
+
+2.  **Set up Virtual Environment:**
+    It's highly recommended to use a virtual environment for this example.
+    ```bash
+    python -m venv .venv
+    source .venv/bin/activate  # Linux/macOS
+    # .venv\Scripts\activate  # Windows
+    ```
+
+3.  **Install Dependencies for this Example:**
+    This example has its own `requirements.txt` with pinned versions.
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4.  **Configure Environment Variables (`.env`):**
+    *   In this directory (`examples/sync-stock-lookup-custom-a2a/`), copy `.env.example` to a new file named `.env`:
+        ```bash
+        cp .env.example .env
+        ```
+    *   Open the `.env` file and populate it with your specific configuration values (API keys, ports, etc.). Refer to the comments within `.env.example`.
+        *Key variables include `GOOGLE_API_KEY`, `LIVE_SERVER_PORT`, `STOCK_INFO_AGENT_A2A_SERVER_PORT`, `STOCK_MCP_SERVER_PATH`, `MOCK_STOCK_API`.*
+
+### Running the Demo
+
+Execute the components in **separate terminals** from **this example's root directory** (`project-horizon/examples/sync-stock-lookup-custom-a2a/`), ensuring your virtual environment is active in each.
+
+1.  **Terminal 1: Start Specialist Agent (StockInfoAgent A2A Server)**
+    Ensure the `STOCK_MCP_SERVER_PATH` in your `.env` file is correctly set (e.g., `mcp_servers/stock_mcp_server/server.py` relative to this example's root, or an absolute path).
+    ```bash
+    python -m specialist_agents.stock_info_agent
+    ```
+    *Verify it starts listening on the port defined in `.env` (e.g., 8001).*
+
+2.  **Terminal 2: Start ADK Live Server (Host Agent)**
+    ```bash
+    python -m app.live_server
+    ```
+    *Verify it starts, discovers the specialist agent, and listens on its port (e.g., 8000 as per `.env`).*
+
+3.  **Access UI:**
+    Open your web browser to `http://<LIVE_SERVER_HOST>:<LIVE_SERVER_PORT>` (e.g., `http://127.0.0.1:8000`, using values from your `.env`).
+
+4.  **Interact:**
+    *   Click "Connect".
+    *   Click the Mic button, speak a stock request (e.g., "What is the price of Apple?").
+    *   Click the Mic button again to stop recording.
+    *   Listen for the audio response.
+    *   Check the logs in both terminals for A2A and MCP interactions.
+    *   Click "Stop" in the UI when finished.
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/live_server.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/live_server.py
new file mode 100644
index 000000000..10e4785ae
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/live_server.py
@@ -0,0 +1,356 @@
+# app/live_server.py
+import asyncio
+import json
+import logging
+import os
+import uuid
+import base64 # Needed for decoding/encoding audio
+from pathlib import Path
+import sys # To modify path for imports if needed
+from contextlib import suppress # For ignoring CancelledError during cleanup
+
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from fastapi.staticfiles import StaticFiles
+from starlette.responses import FileResponse # Use Starlette directly for FileResponse
+
+# --- ADK Imports ---
+from google.adk.runners import Runner
+from google.adk.agents.run_config import RunConfig
+from google.adk.sessions import InMemorySessionService, Session # Using in-memory for simplicity
+from google.adk.agents.live_request_queue import LiveRequestQueue
+from google.adk.events import Event, EventActions # Import EventActions for state delta
+# Use alias for genai types to avoid conflicts if any
+from google.genai import types as genai_types
+
+from typing import Any, Dict, Optional
+
+
+# --- Configuration ---
+from dotenv import load_dotenv
+# Load .env file from the parent directory (project root)
+dotenv_path = os.path.join(os.path.dirname(__file__), '..', '.env')
+load_dotenv(dotenv_path=dotenv_path, override=True)
+
+# Setup logging
+logging.basicConfig(level=os.getenv('LOG_LEVEL', 'INFO').upper(),
+                   format='%(asctime)s - %(name)s - %(levelname)s - LIVE_SERVER - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Import the Host Agent CREATION FUNCTION ---
+try:
+    project_root = Path(__file__).parent.parent
+    if str(project_root) not in sys.path:
+        sys.path.insert(0, str(project_root))
+    from host_agent.agent import create_host_agent
+    logger.info("Successfully imported host_agent creation function.")
+except ImportError as e:
+     logger.critical(f"Could not import host_agent.agent: {e}. Check sys.path and ensure host_agent/agent.py exists.", exc_info=True)
+     create_host_agent = None # type: ignore
+
+if not create_host_agent:
+    logger.critical("Host agent creation function failed to load. Exiting.")
+    sys.exit(1)
+
+APP_NAME = "ProjectHorizonLive"
+STATIC_DIR = Path(__file__).parent / "static"
+
+# --- ADK Setup ---
+session_service = InMemorySessionService()
+runner: Optional[Runner] = None # Initialize as None
+
+# --- FastAPI App ---
+app = FastAPI(title="Project Horizon Live Server") # Create app instance early
+
+async def initialize_adk_system():
+    """Initializes the ADK system, including specialist agent discovery and HostAgent creation."""
+    global runner
+
+    if not create_host_agent:
+        logger.critical("create_host_agent function not available. Cannot initialize ADK system.")
+        # This should ideally prevent server from starting, but with FastAPI events,
+        # we'll log critical and the app might fail to handle requests properly.
+        return
+
+    temp_host_agent = await create_host_agent()
+    if not temp_host_agent:
+        logger.critical("Failed to create HostAgent after discovery.")
+        return
+    
+    runner = Runner(
+        app_name=APP_NAME,
+        agent=temp_host_agent,
+        session_service=session_service,
+    )
+    logger.info(f"ADK Runner initialized with dynamically configured agent: {temp_host_agent.name}")
+
+# --- FastAPI Startup Event ---
+@app.on_event("startup")
+async def startup_event():
+    logger.info("FastAPI server starting up...")
+    await initialize_adk_system()
+    if not runner:
+        logger.critical("ADK Runner failed to initialize during startup. Server might not function correctly.")
+    else:
+        logger.info("ADK System initialized successfully within FastAPI startup.")
+
+
+# --- WebSocket Endpoint ---
+@app.websocket("/ws/{session_id}")
+async def websocket_endpoint(websocket: WebSocket, session_id: str):
+    """Handles WebSocket connection for live agent interaction."""
+    await websocket.accept()
+    logger.info(f"WebSocket client connected for session: {session_id}")
+    user_id = f"live_user_{session_id}"
+
+    live_events: Optional[asyncio.StreamReader] = None
+    live_request_queue: Optional[LiveRequestQueue] = None
+    adk_run_task: Optional[asyncio.Task] = None
+    client_listener_task: Optional[asyncio.Task] = None
+    adk_session: Optional[Session] = None
+
+    if not runner:
+        logger.error(f"Runner not initialized for session {session_id}. Aborting WebSocket connection.")
+        await websocket.close(code=1011, reason="Server not ready (Runner missing)")
+        return
+
+    try:
+        adk_session = session_service.get_session(
+            app_name=APP_NAME, user_id=user_id, session_id=session_id
+        )
+        if not adk_session:
+            adk_session = session_service.create_session(
+                app_name=APP_NAME, user_id=user_id, session_id=session_id, state={}
+            )
+            logger.info(f"Created new ADK session: {session_id}")
+        else:
+             logger.info(f"Resumed existing ADK session: {session_id}")
+
+        live_request_queue = LiveRequestQueue()
+        run_config = RunConfig(response_modalities=["AUDIO"])
+        live_events = runner.run_live( # type: ignore
+            session=adk_session,
+            live_request_queue=live_request_queue,
+            run_config=run_config,
+        )
+        logger.info(f"ADK run_live started for session {session_id}")
+
+        async def adk_to_client():
+            nonlocal adk_session
+            logger.debug(f"[{session_id}] ADK -> Client task started.")
+            audio_chunk_counter = 0
+            try:
+                async for event in live_events: # type: ignore
+                    logger.debug(f"[{session_id}] Raw ADK Event Received: {event}")
+                    message_to_send = None
+                    event_processed = False
+
+                    if hasattr(event, 'interrupted') and event.interrupted:
+                         logger.info(f"[{session_id}] Received interruption signal from ADK.")
+                         await websocket.send_json({"type": "interrupted"})
+                         logger.info(f"[{session_id}] Sent 'interrupted' signal to client.")
+
+                    if event.content and event.content.parts:
+                         part = event.content.parts[0]
+                         if part.inline_data and part.inline_data.mime_type.startswith("audio/"):
+                              audio_bytes = part.inline_data.data
+                              audio_b64 = base64.b64encode(audio_bytes).decode('utf-8')
+                              message_to_send = {"type": "audio", "data": audio_b64}
+                              audio_chunk_counter += 1
+                              if audio_chunk_counter == 1:
+                                  logger.info(f"[{session_id}] Receiving audio stream from ADK...")
+                              logger.debug(f"[{session_id}] Sending audio chunk #{audio_chunk_counter} ({len(audio_bytes)} bytes) to client.")
+                              await websocket.send_json(message_to_send)
+                              event_processed = True
+
+                    if event.turn_complete:
+                        if audio_chunk_counter > 0:
+                            logger.info(f"[{session_id}] Finished sending {audio_chunk_counter} audio chunks.")
+                        audio_chunk_counter = 0
+                        await websocket.send_json({"type": "turn_complete"})
+                        logger.info(f"[{session_id}] Sent turn_complete signal to client.")
+                        event_processed = True
+
+                    if not event_processed and not event.actions:
+                        is_tool_event = bool(event.get_function_calls() or event.get_function_responses())
+                        if not is_tool_event:
+                             logger.debug(f"[{session_id}] Skipping event: Author={event.author}, Partial={event.partial}, Content Type={type(event.content.parts[0]).__name__ if event.content and event.content.parts else 'None'}")
+                        else:
+                             if event.get_function_calls():
+                                 logger.debug(f"[{session_id}] Processing event: Tool Call Requested by {event.author}")
+                             elif event.get_function_responses():
+                                 logger.debug(f"[{session_id}] Processing event: Tool Response from {event.author}")
+
+                    if event.actions and (event.actions.state_delta or event.actions.artifact_delta):
+                        logger.debug(f"[{session_id}] Appending event with actions: {event.actions}")
+                        if adk_session:
+                            session_service.append_event(adk_session, event)
+                            adk_session = session_service.get_session(app_name=APP_NAME, user_id=user_id, session_id=session_id) # type: ignore
+                            logger.debug(f"[{session_id}] Session state possibly updated by event actions.")
+                        else:
+                            logger.warning(f"[{session_id}] adk_session is None, cannot append event actions.")
+            except WebSocketDisconnect:
+                 logger.info(f"[{session_id}] WebSocket disconnected during ADK event processing.")
+            except asyncio.CancelledError:
+                 logger.info(f"[{session_id}] ADK -> Client task cancelled.")
+            except Exception as e:
+                 logger.error(f"[{session_id}] Error in ADK -> Client task: {e}", exc_info=True)
+            finally:
+                 logger.debug(f"[{session_id}] ADK -> Client task finished.")
+
+        async def client_to_adk():
+            nonlocal adk_session
+            logger.debug(f"[{session_id}] Client -> ADK task started.")
+            try:
+                while True:
+                    data = await websocket.receive_json()
+                    message_type = data.get("type")
+                    logger.debug(f"[{session_id}] Received '{message_type}' from client.")
+
+                    if message_type == "audio":
+                        audio_b64 = data.get("data", "")
+                        if audio_b64:
+                            audio_bytes = base64.b64decode(audio_b64)
+                            if audio_bytes:
+                                audio_blob = genai_types.Blob(mime_type='audio/pcm', data=audio_bytes)
+                                if live_request_queue: live_request_queue.send_realtime(blob=audio_blob)
+                            else:
+                                logger.warning(f"[{session_id}] Received empty audio data from client.")
+                    elif message_type == "text":
+                        text_data = data.get("data", "")
+                        if text_data:
+                            logger.info(f"[{session_id}] Sending text '{text_data}' to ADK.")
+                            content = genai_types.Content(role='user', parts=[genai_types.Part(text=text_data)])
+                            if live_request_queue: live_request_queue.send_content(content=content)
+                    elif message_type == "end_of_turn":
+                        logger.info(f"[{session_id}] Client indicated end of turn.")
+                        logger.debug(f"[{session_id}] End of turn signal received, letting Gemini API infer turn end.")
+                    elif message_type == "toggle_mock":
+                         mock_value = data.get("value", False)
+                         logger.info(f"[{session_id}] Setting mock_a2a_calls state to: {mock_value}")
+                         state_update_event = Event(
+                             author="ui_control",
+                             invocation_id = f"ui_mock_toggle_{uuid.uuid4().hex[:8]}",
+                             actions=EventActions(
+                                 state_delta={'mock_a2a_calls': mock_value}
+                             )
+                         )
+                         if adk_session:
+                             session_service.append_event(adk_session, state_update_event)
+                             adk_session = session_service.get_session(app_name=APP_NAME, user_id=user_id, session_id=session_id) # type: ignore
+                             logger.info(f"[{session_id}] State 'mock_a2a_calls' updated via event to: {adk_session.state.get('mock_a2a_calls')}") # type: ignore
+                         else:
+                             logger.warning(f"[{session_id}] adk_session is None, cannot update mock_a2a_calls state.")
+                    else:
+                         logger.warning(f"[{session_id}] Received unknown message type from client: {message_type}")
+
+            except WebSocketDisconnect:
+                logger.info(f"[{session_id}] Client WebSocket disconnected (detected in listener).")
+                if live_request_queue: live_request_queue.close()
+            except asyncio.CancelledError:
+                 logger.info(f"[{session_id}] Client -> ADK task cancelled.")
+            except Exception as e_inner:
+                logger.error(f"[{session_id}] Error in client listener task: {e_inner}", exc_info=True)
+                if live_request_queue: live_request_queue.close()
+            finally:
+                logger.debug(f"[{session_id}] Client -> ADK task finished.")
+
+        logger.info(f"[{session_id}] Starting ADK <-> WebSocket bridge tasks.")
+        adk_run_task = asyncio.create_task(adk_to_client())
+        client_listener_task = asyncio.create_task(client_to_adk())
+        done, pending = await asyncio.wait(
+            [adk_run_task, client_listener_task],
+            return_when=asyncio.FIRST_COMPLETED,
+        )
+        logger.info(f"[{session_id}] One of the bridge tasks completed ({len(done)} done, {len(pending)} pending).")
+
+        for task in pending:
+            if not task.done():
+                logger.debug(f"[{session_id}] Cancelling pending bridge task...")
+                task.cancel()
+                with suppress(asyncio.CancelledError): await task
+
+    except WebSocketDisconnect:
+        logger.info(f"WebSocket client disconnected gracefully for session: {session_id}")
+    except asyncio.CancelledError:
+         logger.info(f"WebSocket endpoint task cancelled for session: {session_id}")
+    except Exception as e_outer:
+        logger.error(f"Unhandled error in WebSocket endpoint for session {session_id}: {e_outer}", exc_info=True)
+        with suppress(Exception):
+            await websocket.close(code=1011, reason=f"Internal Server Error: {type(e_outer).__name__}")
+    finally:
+        logger.info(f"Performing final cleanup for session: {session_id}")
+        if live_request_queue:
+             logger.debug(f"[{session_id}] Closing live request queue in final cleanup.")
+             try:
+                 live_request_queue.close()
+             except Exception as q_close_err:
+                 logger.warning(f"[{session_id}] Error closing LiveRequestQueue: {q_close_err}")
+        if adk_run_task and not adk_run_task.done(): adk_run_task.cancel()
+        if client_listener_task and not client_listener_task.done(): client_listener_task.cancel()
+        if adk_session:
+            try:
+                session_service.delete_session(app_name=APP_NAME, user_id=user_id, session_id=session_id)
+                logger.info(f"Session removed from service: {session_id}")
+            except Exception as del_err:
+                 logger.warning(f"[{session_id}] Error deleting session: {del_err}")
+        logger.info(f"WebSocket connection fully closed for session: {session_id}")
+
+
+# --- Static File Serving ---
+if not STATIC_DIR.is_dir():
+    logger.error(f"Static directory not found at {STATIC_DIR}. UI will not be served.")
+else:
+    app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+    logger.info(f"Serving static files from {STATIC_DIR}")
+
+    @app.get("/")
+    async def read_index():
+        index_path = STATIC_DIR / "index.html"
+        if not index_path.is_file():
+            logger.error("index.html not found in static directory.")
+            return {"error": "index.html not found"}, 404
+        logger.debug("Serving index.html")
+        return FileResponse(str(index_path))
+
+# --- Server Startup (for running directly) ---
+# The `main_async_startup` is now only needed if you run this file directly.
+# Uvicorn will handle app creation and startup events when run as `uvicorn app.live_server:app`
+if __name__ == "__main__":
+    project_root_path = Path(__file__).parent.parent
+    if str(project_root_path) not in sys.path:
+        sys.path.insert(0, str(project_root_path))
+        logger.debug(f"Added project root to sys.path for direct execution: {project_root_path}")
+
+    LIVE_SERVER_MODEL_ID = os.getenv("LIVE_SERVER_MODEL")
+    if not LIVE_SERVER_MODEL_ID:
+        logger.warning("LIVE_SERVER_MODEL env var not set. Defaulting to 'gemini-2.0-flash-exp'.")
+        LIVE_SERVER_MODEL_ID = "gemini-2.0-flash-exp"
+
+    LIVE_SERVER_HOST = os.getenv("LIVE_SERVER_HOST")
+    if LIVE_SERVER_HOST is None:
+        logger.critical("CRITICAL: LIVE_SERVER_HOST env var not set.")
+        sys.exit(1)
+
+    raw_port = os.getenv("LIVE_SERVER_PORT")
+    if raw_port is None:
+        logger.critical("LIVE_SERVER_PORT env var not set.")
+        sys.exit(1)
+    
+    try:
+        LIVE_SERVER_PORT = int(raw_port)
+    except ValueError:
+        logger.critical(f"Invalid LIVE_SERVER_PORT: '{raw_port}'. Must be integer.")
+        sys.exit(1)
+
+    # When running directly, we need to manually trigger the startup event logic
+    # (or Uvicorn does it if 'app' object is passed)
+    # Uvicorn will call startup events registered on the app object.
+    # No need to call initialize_adk_system() directly here if Uvicorn is used.
+    logger.info(f"Starting Project Horizon Live Server (direct run) on http://{LIVE_SERVER_HOST}:{LIVE_SERVER_PORT}")
+    try:
+        import uvicorn
+        uvicorn.run(app, host=LIVE_SERVER_HOST, port=LIVE_SERVER_PORT, log_level="info")
+    except ImportError:
+         logger.critical("Uvicorn not installed. Run 'pip install uvicorn[standard]'.")
+    except Exception as startup_error:
+         logger.critical(f"Failed to start Uvicorn server: {startup_error}", exc_info=True)
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/adk-websocket-api.js b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/adk-websocket-api.js
new file mode 100644
index 000000000..f30434dba
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/adk-websocket-api.js
@@ -0,0 +1,369 @@
+// app/static/adk-websocket-api.js
+
+// Ensure EventEmitter3 is loaded (e.g., via CDN in index.html)
+const EventEmitter = window.EventEmitter3;
+
+// --- Assume these classes are available globally or via imports ---
+// --- You MUST copy the corresponding .js files from Pastra   ---
+// --- into ./audio/ and ./utils/                           ---
+import { AudioRecorder } from './audio/audio-recorder.js';
+import { AudioStreamer } from './audio/audio-streamer.js';
+import { base64ToArrayBuffer } from './utils/utils.js';
+// ---------------------------------------------------------------
+
+class ADKWebSocketAPI extends EventEmitter {
+    constructor() {
+        super(); // Initialize EventEmitter
+        this.ws = null;
+        this.audioContext = null; // Keep initialization lazy but ensure resumed
+        this.audioRecorder = new AudioRecorder();
+        this.audioStreamer = null;
+        this.isRecording = false;
+        this.isMuted = false; // Start unmuted
+        this.isConnected = false;
+        this.sessionID = null;
+        this.statusIndicator = document.getElementById('statusIndicator');
+        this.chatOutput = document.getElementById('chatOutput');
+        this.connectButton = document.getElementById('connectButton');
+        this.stopButton = document.getElementById('stopButton');
+        this.micButton = document.getElementById('micButton');
+        this.connectContainer = document.getElementById('connectButtonContainer');
+        this.mediaContainer = document.getElementById('mediaButtonsContainer');
+
+        this._bindUIEvents();
+        console.log("ADKWebSocketAPI initialized."); // Log constructor finish
+    }
+
+    _updateStatusIndicator(status) { // 'offline', 'connecting', 'online', 'error'
+        this.statusIndicator.className = `status-indicator ${status}`;
+        this.statusIndicator.title = status.charAt(0).toUpperCase() + status.slice(1);
+    }
+
+    _logToUI(message, type = 'info') { // type: 'user', 'agent', 'info', 'error'
+        const p = document.createElement('p');
+        p.textContent = message;
+        p.className = `${type}-message`; // Apply class based on type
+        this.chatOutput.appendChild(p);
+        // Auto-scroll to bottom
+        this.chatOutput.scrollTop = this.chatOutput.scrollHeight;
+    }
+
+    // *** CHANGE 1: Make initializeAudio async ***
+    async initializeAudio() {
+        try {
+            if (!this.audioContext) {
+                console.log("Creating AudioContext...");
+                this.audioContext = new (window.AudioContext || window.webkitAudioContext)({
+                    sampleRate: 24000 // Matches streamer and likely ADK output
+                });
+                console.log("AudioContext created, state:", this.audioContext.state);
+            }
+            // Resume context on user interaction (required by browsers)
+            if (this.audioContext.state === 'suspended') {
+                console.log("Attempting to resume suspended AudioContext...");
+                await this.audioContext.resume();
+                console.log("AudioContext resumed, new state:", this.audioContext.state);
+            }
+            // Add log right before creating streamer
+            if (!this.audioStreamer && this.audioContext) {
+                 console.log(`Creating AudioStreamer with context state: ${this.audioContext.state}`);
+                 this.audioStreamer = new AudioStreamer(this.audioContext);
+                 this.audioStreamer.onComplete = () => console.log("AudioStreamer: Playback complete.");
+                 console.log("AudioStreamer created.");
+            }
+            console.log("Audio initialization/resume complete.");
+            return true; // Indicate success
+        } catch (error) {
+             console.error("Failed to initialize audio:", error);
+             this._logToUI(`Error initializing audio: ${error.message}`, "error");
+             return false; // Indicate failure
+        }
+    }
+
+    // *** CHANGE 2: Modify connect to call initializeAudio ***
+    async connect() { // Make connect async
+        this.sessionID = uuid.v4();
+        const wsProtocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
+        // Use the port the current page is served from
+        const backendPort = window.location.port || (wsProtocol === "wss:" ? "443" : "80"); 
+        const wsEndpoint = `${wsProtocol}//${window.location.hostname}:${backendPort}/ws/${this.sessionID}`;
+
+        console.log('Attempting to connect WebSocket to:', wsEndpoint);
+        this._logToUI("Connecting to agent...", "info");
+        this._updateStatusIndicator("connecting");
+
+        return new Promise((resolve, reject) => {
+            if (this.ws && this.ws.readyState === WebSocket.OPEN) {
+                console.warn("WebSocket already open.");
+                resolve();
+                return;
+            }
+
+            try {
+                 this.ws = new WebSocket(wsEndpoint);
+            } catch (error) {
+                 console.error("WebSocket creation failed:", error);
+                 this._logToUI(`WebSocket Error: ${error.message}`, "error");
+                 this._updateStatusIndicator("error");
+                 reject(error);
+                 return;
+            }
+
+
+            this.ws.onopen = async () => { // Make onopen async
+                console.log('WebSocket connection established.');
+                this.isConnected = true;
+
+                console.log("Attempting to initialize audio system on connect...");
+                const audioInitialized = await this.initializeAudio();
+                console.log(`Audio initialization result: ${audioInitialized}`);
+                console.log(`AudioContext state after init: ${this.audioContext?.state}`); // Log state again
+
+                if (audioInitialized) {
+                    this._updateStatusIndicator("online"); // Set status *after* successful audio init
+                    this._logToUI("Connected! Audio ready. Press Mic to talk.", "info");
+                    // Update UI: Hide Connect, Show Controls
+                    this.connectContainer.classList.add('hidden');
+                    this.mediaContainer.classList.remove('hidden');
+                    this.micButton.disabled = false; // Enable mic button
+                    resolve(); // Resolve the promise after audio is ready
+                } else {
+                    this._updateStatusIndicator("error"); // Show error if audio failed
+                     this._logToUI("Connected, but failed to initialize audio playback.", "error");
+                     // Still show connected, but warn user playback might fail
+                     this.connectContainer.classList.add('hidden');
+                     this.mediaContainer.classList.remove('hidden');
+                     this.micButton.disabled = false; // Mic might still work for input
+                     // Reject or resolve based on whether audio playback is critical
+                     reject(new Error("Audio initialization failed")); // Reject if audio must work
+                }
+            };
+
+            this.ws.onmessage = async (event) => { // Keep async
+                try {
+                    const message = JSON.parse(event.data);
+                    console.log(`WebSocket Message Received: Type=${message.type}, ContextState=${this.audioContext?.state}`); // Log type and state
+
+                    if (message.type === 'audio') {
+                        const canPlay = !!this.audioStreamer && this.audioContext?.state === 'running';
+                        console.log(`Audio message received. Can play? ${canPlay}`); // Log the check result
+
+                        if (canPlay) {
+                            const audioBytes = base64ToArrayBuffer(message.data);
+                            console.log(`Queuing ${audioBytes.byteLength} audio bytes for playback.`);
+                            this.audioStreamer.addPCM16(new Uint8Array(audioBytes));
+                        } else {
+                             console.warn("Audio streamer not ready or context not running. Cannot play audio chunk.", { streamer: !!this.audioStreamer, contextState: this.audioContext?.state });
+                             // Try resuming again *just in case*
+                             if(this.audioContext && this.audioContext.state === 'suspended') {
+                                 console.log("Attempting to resume suspended context during message handling...");
+                                 await this.audioContext.resume();
+                                 console.log(`Context state after resume attempt: ${this.audioContext.state}`);
+                                 // If resume succeeded, maybe try adding the chunk again? Careful about race conditions.
+                                 if (this.audioContext.state === 'running' && this.audioStreamer) {
+                                     console.log("Context resumed, retrying addPCM16 for the missed chunk.");
+                                     const audioBytes = base64ToArrayBuffer(message.data);
+                                     this.audioStreamer.addPCM16(new Uint8Array(audioBytes));
+                                 }
+                             }
+                        }
+                    } else if (message.type === 'text') {
+                        console.log("Received text message:", message.data);
+                        this._logToUI(message.data, "agent");
+                    } else if (message.type === 'interrupted') {
+                        console.log("Received interruption signal from server.");
+                        if (this.audioStreamer) {
+                            console.log("Stopping audio streamer due to interruption.");
+                            this.audioStreamer.stop();
+                        }
+                        this._logToUI("Agent response interrupted.", "info");
+                    } else if (message.type === 'turn_complete') {
+                         console.log("Received turn_complete message.");
+                         if (this.audioStreamer) this.audioStreamer.complete();
+                    } else if (message.type === 'error') {
+                        console.error("Received error message:", message.data);
+                        this._logToUI(`Server Error: ${message.data}`, "error");
+                    }
+
+                } catch (error) {
+                    console.error('Error processing WebSocket message:', error);
+                    this._logToUI("Error processing server message.", "error");
+                }
+            };
+
+            this.ws.onerror = (error) => {
+                console.error('WebSocket Error:', error);
+                this.isConnected = false;
+                this._updateStatusIndicator("error");
+                // Try to log the specific event if possible, might give clues
+                let errorMsg = "Connection error.";
+                // Check if it's a proper ErrorEvent (browser standard)
+                if (error instanceof ErrorEvent && error.message) {
+                    errorMsg = `Connection error: ${error.message}`;
+                } else if (typeof error === 'string') {
+                     errorMsg = `Connection error: ${error}`;
+                }
+                 console.error("Detailed WebSocket error event:", error); // Log the raw event
+                this._logToUI(errorMsg, "error");
+                this._resetUI();
+                reject(error instanceof Error ? error : new Error(errorMsg)); // Reject promise on error
+            };
+
+
+            this.ws.onclose = (event) => {
+                console.log('WebSocket connection closed:', event.code, event.reason);
+                this.isConnected = false;
+                this._updateStatusIndicator("offline");
+                this._logToUI("Connection closed.", "info");
+                this._resetUI(); // Reset UI on close
+                // Optionally trigger automatic reconnection here
+                // If the promise hasn't resolved/rejected yet (e.g., closed during initial connection)
+                // reject(new Error(`WebSocket closed prematurely: Code ${event.code}`));
+            };
+        });
+    }
+
+    _sendMessage(message) {
+        if (this.ws && this.ws.readyState === WebSocket.OPEN) {
+            // console.debug("Sending WebSocket Message:", message.type); // Keep logging minimal
+            this.ws.send(JSON.stringify(message));
+        } else {
+            console.error('WebSocket is not open. Cannot send message.');
+            this._logToUI("Connection lost.", "error");
+            this._resetUI();
+        }
+    }
+
+    sendAudioChunk(base64Audio) {
+        if (!this.isMuted) { // Only send if not muted
+             this._sendMessage({ type: 'audio', data: base64Audio });
+        }
+    }
+
+    sendTextMessage(text) {
+        this._logToUI(text, "user"); // Log user text to chat
+        this._sendMessage({ type: 'text', data: text });
+        this.endUserTurn(); // End turn immediately after sending text
+    }
+
+    endUserTurn() {
+        console.log("Signaling end of user turn.");
+        this._sendMessage({ type: 'end_of_turn' });
+    }
+
+    // *** CHANGE 4: Make _startRecording async ***
+    async _startRecording() { // Make async
+        if (this.isRecording) return;
+        try {
+            // Ensure audio context is ready before starting recorder
+            // Although initializeAudio is called on connect, call it again here
+            // to be absolutely sure and handle cases where it might have been suspended.
+            const audioReady = await this.initializeAudio();
+            if (!audioReady) {
+                 throw new Error("Audio context could not be initialized/resumed.");
+            }
+
+            await this.audioRecorder.start();
+            this.isRecording = true;
+            this.isMuted = false; // Start unmuted
+            console.log("Audio recording started.");
+            this._logToUI("🎤 Recording... (Click Mic again to stop)", "info");
+            this.micButton.classList.add('active');
+            this.micButton.title = "Stop Recording";
+            this.micButton.querySelector('.material-symbols-outlined').textContent = 'stop_circle'; // Change icon
+
+            // Send audio chunks
+            this.audioRecorder.on('data', (base64Data) => {
+                if (this.isConnected && !this.isMuted) { // Check connection and mute state
+                    this.sendAudioChunk(base64Data);
+                }
+            });
+        } catch (error) {
+            console.error('Error starting recording:', error);
+            this._logToUI(`Error starting microphone: ${error.message}`, "error");
+            // Reset button state if recording failed to start
+            this.isRecording = false;
+            this.micButton.classList.remove('active');
+            this.micButton.title = "Start Recording";
+            this.micButton.querySelector('.material-symbols-outlined').textContent = 'mic';
+        }
+    }
+
+    _stopRecording() {
+        if (!this.isRecording) return;
+        this.audioRecorder.stop();
+        this.isRecording = false;
+        this.isMuted = false; // Reset mute state
+        console.log("Audio recording stopped.");
+        this._logToUI("Recording stopped.", "info");
+        this.micButton.classList.remove('active');
+        this.micButton.classList.remove('muted'); // Ensure mute style is off
+        this.micButton.title = "Start Recording";
+        this.micButton.querySelector('.material-symbols-outlined').textContent = 'mic'; // Reset icon
+        this.endUserTurn(); // Signal end of input turn
+        if (this.audioStreamer) this.audioStreamer.stop(); // Stop playback if any was happening
+    }
+
+    // No toggleMute needed for simplified UI, just start/stop
+
+    disconnect() {
+        if (this.ws) {
+            console.log("Disconnecting WebSocket.");
+            this._stopRecording(); // Ensure recording stops if active
+            if (this.audioStreamer) this.audioStreamer.stop();
+            this.ws.close(); // Triggers onclose handler
+        }
+        this._resetUI(); // Update UI immediately
+    }
+
+    _resetUI() {
+        this.isConnected = false;
+        this.isRecording = false;
+        this.isMuted = false;
+        this.connectContainer.classList.remove('hidden');
+        this.mediaContainer.classList.add('hidden');
+        this.micButton.disabled = true;
+        this.micButton.classList.remove('active', 'muted');
+        this.micButton.querySelector('.material-symbols-outlined').textContent = 'mic';
+        this.micButton.title = "Start/Stop Recording";
+         // Don't clear chat on disconnect/error, maybe add separator?
+        // this.chatOutput.innerHTML = '<p class="info-message">Disconnected. Click Connect to restart.</p>';
+    }
+
+    // *** CHANGE 5: Make button handler async ***
+    _bindUIEvents() {
+        this.connectButton.onclick = async () => { // Make async
+            this.connectButton.disabled = true;
+            this._logToUI("Connecting...", "info");
+            try {
+                await this.connect(); // Await the connect promise
+                 // Success is handled within connect's onopen
+            } catch (error) {
+                // Error handling moved inside connect's onerror/onclose/reject
+                 console.error("Connect button click failed:", error);
+                 // LogUI and status indicator are handled by connect error paths
+                this.connectButton.disabled = false; // Re-enable on failure
+                 this._updateStatusIndicator("error"); // Ensure error state shown
+                 // Optional: Log a more specific message if connect rejected
+                 this._logToUI(`Connection Failed: ${error.message || 'Unknown error'}`, "error");
+            }
+        };
+
+        this.stopButton.onclick = () => {
+            this.disconnect();
+        };
+
+        this.micButton.onclick = async () => { // Make async because _startRecording is async
+            if (!this.isConnected) return;
+            if (this.isRecording) {
+                this._stopRecording();
+            } else {
+                await this._startRecording(); // Await start recording
+            }
+        };
+    }
+}
+
+// Initialize the API and make it globally accessible (or use modules)
+window.adkApi = new ADKWebSocketAPI();
+console.log("adkApi created."); // Log instance creation
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-recorder.js b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-recorder.js
new file mode 100644
index 000000000..72b9c77a0
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-recorder.js
@@ -0,0 +1,116 @@
+/**
+ * Copyright 2025 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import { audioContext } from "../utils/utils.js";
+import { createWorkletFromSrc, registeredWorklets } from "./audioworklet-registry.js";
+import AudioRecordingWorklet from "./audio-recording-worklet.js";
+
+function arrayBufferToBase64(buffer) {
+  var binary = "";
+  var bytes = new Uint8Array(buffer);
+  var len = bytes.byteLength;
+  for (var i = 0; i < len; i++) {
+    binary += String.fromCharCode(bytes[i]);
+  }
+  return window.btoa(binary);
+}
+
+export class AudioRecorder extends EventEmitter3 {
+  constructor() {
+    super();
+    this.sampleRate = 16000;
+    this.stream = undefined;
+    this.audioContext = undefined;
+    this.source = undefined;
+    this.recording = false;
+    this.recordingWorklet = undefined;
+    this.starting = null;
+    this.isMuted = false;
+  }
+
+  async start() {
+    if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
+      throw new Error("Could not request user media");
+    }
+
+    this.starting = new Promise(async (resolve, reject) => {
+      this.stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+      this.audioContext = await audioContext({ sampleRate: this.sampleRate });
+      this.source = this.audioContext.createMediaStreamSource(this.stream);
+
+      const workletName = "audio-recorder-worklet";
+      const workletSrc = AudioRecordingWorklet;
+
+      if (!registeredWorklets.has(this.audioContext)) {
+        registeredWorklets.set(this.audioContext, {});
+      }
+
+      const registry = registeredWorklets.get(this.audioContext);
+      if (!registry[workletName]) {
+        const src = createWorkletFromSrc(workletName, workletSrc);
+        await this.audioContext.audioWorklet.addModule(src);
+        registry[workletName] = {
+          node: new AudioWorkletNode(this.audioContext, workletName),
+          handlers: []
+        };
+      }
+
+      this.recordingWorklet = registry[workletName].node;
+
+      this.recordingWorklet.port.onmessage = async (ev) => {
+        const arrayBuffer = ev.data.data.int16arrayBuffer;
+
+        if (arrayBuffer) {
+          const arrayBufferString = arrayBufferToBase64(arrayBuffer);
+          this.emit("data", arrayBufferString);
+        }
+      };
+      this.source.connect(this.recordingWorklet);
+
+      this.recording = true;
+      resolve();
+      this.starting = null;
+    });
+  }
+
+  stop() {
+    const handleStop = () => {
+      this.source?.disconnect();
+      this.stream?.getTracks().forEach((track) => track.stop());
+      this.stream = undefined;
+      this.recordingWorklet = undefined;
+    };
+    if (this.starting) {
+      this.starting.then(handleStop);
+      return;
+    }
+    handleStop();
+  }
+
+  mute() {
+    if (this.source && this.recordingWorklet && !this.isMuted) {
+      this.source.disconnect(this.recordingWorklet);
+      this.isMuted = true;
+    }
+  }
+
+  unmute() {
+    if (this.source && this.recordingWorklet && this.isMuted) {
+      this.source.connect(this.recordingWorklet);
+      this.isMuted = false;
+    }
+  }
+}
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-recording-worklet.js b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-recording-worklet.js
new file mode 100644
index 000000000..e079c24c5
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-recording-worklet.js
@@ -0,0 +1,69 @@
+/**
+ * Copyright 2025 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+const workletCode = `
+class AudioProcessingWorklet extends AudioWorkletProcessor {
+  // send and clear buffer every 2048 samples, 
+  // which at 16khz is about 8 times a second
+  buffer = new Int16Array(2048);
+
+  // current write index
+  bufferWriteIndex = 0;
+
+  constructor() {
+    super();
+  }
+
+  process(inputs) {
+    if (inputs[0].length) {
+      const channel0 = inputs[0][0];
+      this.processChunk(channel0);
+    }
+    return true;
+  }
+
+  sendAndClearBuffer() {
+    this.port.postMessage({
+      event: "chunk",
+      data: {
+        int16arrayBuffer: this.buffer.slice(0, this.bufferWriteIndex).buffer,
+      },
+    });
+    this.bufferWriteIndex = 0;
+  }
+
+  processChunk(float32Array) {
+    const l = float32Array.length;
+    
+    for (let i = 0; i < l; i++) {
+      // convert float32 -1 to 1 to int16 -32768 to 32767
+      const int16Value = float32Array[i] * 32768;
+      this.buffer[this.bufferWriteIndex++] = int16Value;
+      if(this.bufferWriteIndex >= this.buffer.length) {
+        this.sendAndClearBuffer();
+      }
+    }
+
+    if(this.bufferWriteIndex >= this.buffer.length) {
+      this.sendAndClearBuffer();
+    }
+  }
+}
+
+registerProcessor('audio-recorder-worklet', AudioProcessingWorklet);
+`;
+
+export default workletCode;
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-streamer.js b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-streamer.js
new file mode 100644
index 000000000..47609abcf
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audio-streamer.js
@@ -0,0 +1,184 @@
+/**
+ * Copyright 2025 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+export class AudioStreamer {
+    constructor(audioContext) {
+      this.context = audioContext;
+      this.sampleRate = 24000; // Output sample rate as per API spec
+      this.audioQueue = [];
+      this.isPlaying = false;
+      this.currentSource = null;
+      this.gainNode = this.context.createGain();
+      this.gainNode.connect(this.context.destination);
+      this.addPCM16 = this.addPCM16.bind(this);
+      this.onComplete = () => {};
+      this.playbackTimeout = null;
+      this.lastPlaybackTime = 0;
+    }
+
+    addPCM16(chunk) {
+      // Convert incoming PCM16 data to float32
+      const float32Array = new Float32Array(chunk.length / 2);
+      const dataView = new DataView(chunk.buffer);
+
+      for (let i = 0; i < chunk.length / 2; i++) {
+        try {
+          const int16 = dataView.getInt16(i * 2, true);
+          float32Array[i] = int16 / 32768;
+        } catch (e) {
+          console.error(e);
+        }
+      }
+
+      // Create and fill audio buffer
+      const audioBuffer = this.context.createBuffer(1, float32Array.length, this.sampleRate);
+      audioBuffer.getChannelData(0).set(float32Array);
+
+      // Add to queue and start playing if needed
+      this.audioQueue.push(audioBuffer);
+      
+      if (!this.isPlaying) {
+        this.isPlaying = true;
+        this.lastPlaybackTime = this.context.currentTime;
+        this.playNextBuffer();
+      }
+
+      // Ensure playback continues if it was interrupted
+      this.checkPlaybackStatus();
+    }
+
+    checkPlaybackStatus() {
+      // Clear any existing timeout
+      if (this.playbackTimeout) {
+        clearTimeout(this.playbackTimeout);
+      }
+
+      // Set a new timeout to check playback status
+      this.playbackTimeout = setTimeout(() => {
+        const now = this.context.currentTime;
+        const timeSinceLastPlayback = now - this.lastPlaybackTime;
+
+        // If more than 1 second has passed since last playback and we have buffers to play
+        if (timeSinceLastPlayback > 1 && this.audioQueue.length > 0 && this.isPlaying) {
+          console.log('Playback appears to have stalled, restarting...');
+          this.playNextBuffer();
+        }
+
+        // Continue checking if we're still playing
+        if (this.isPlaying) {
+          this.checkPlaybackStatus();
+        }
+      }, 1000);
+    }
+
+    playNextBuffer() {
+      if (this.audioQueue.length === 0) {
+        this.isPlaying = false;
+        return;
+      }
+
+      // Update last playback time
+      this.lastPlaybackTime = this.context.currentTime;
+
+      try {
+        const audioBuffer = this.audioQueue.shift();
+        const source = this.context.createBufferSource();
+        source.buffer = audioBuffer;
+        source.connect(this.gainNode);
+
+        // Store current source for potential stopping
+        if (this.currentSource) {
+          try {
+            this.currentSource.disconnect();
+          } catch (e) {
+            // Ignore disconnection errors
+          }
+        }
+        this.currentSource = source;
+
+        // When this buffer ends, play the next one
+        source.onended = () => {
+          this.lastPlaybackTime = this.context.currentTime;
+          if (this.audioQueue.length > 0) {
+            // Small delay to ensure smooth transition
+            setTimeout(() => this.playNextBuffer(), 0);
+          } else {
+            this.isPlaying = false;
+            this.onComplete();
+          }
+        };
+
+        // Start playing immediately
+        source.start(0);
+      } catch (error) {
+        console.error('Error during playback:', error);
+        // Try to recover by playing next buffer
+        if (this.audioQueue.length > 0) {
+          setTimeout(() => this.playNextBuffer(), 100);
+        } else {
+          this.isPlaying = false;
+        }
+      }
+    }
+
+    stop() {
+      this.isPlaying = false;
+      if (this.playbackTimeout) {
+        clearTimeout(this.playbackTimeout);
+        this.playbackTimeout = null;
+      }
+      if (this.currentSource) {
+        try {
+          this.currentSource.stop();
+          this.currentSource.disconnect();
+        } catch (e) {
+          // Ignore if already stopped
+        }
+      }
+      this.audioQueue = [];
+      this.gainNode.gain.linearRampToValueAtTime(0, this.context.currentTime + 0.1);
+
+      setTimeout(() => {
+        this.gainNode.disconnect();
+        this.gainNode = this.context.createGain();
+        this.gainNode.connect(this.context.destination);
+      }, 200);
+    }
+
+    async resume() {
+      if (this.context.state === 'suspended') {
+        await this.context.resume();
+      }
+      this.lastPlaybackTime = this.context.currentTime;
+      this.gainNode.gain.setValueAtTime(1, this.context.currentTime);
+      if (this.audioQueue.length > 0 && !this.isPlaying) {
+        this.isPlaying = true;
+        this.playNextBuffer();
+      }
+    }
+
+    complete() {
+      if (this.audioQueue.length > 0) {
+        // Let the remaining buffers play out
+        return;
+      }
+      if (this.playbackTimeout) {
+        clearTimeout(this.playbackTimeout);
+        this.playbackTimeout = null;
+      }
+      this.onComplete();
+    }
+  }
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audioworklet-registry.js b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audioworklet-registry.js
new file mode 100644
index 000000000..66ef29252
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/audio/audioworklet-registry.js
@@ -0,0 +1,29 @@
+/**
+ * Copyright 2025 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * A registry to map attached worklets by their audio-context
+ * any module using `audioContext.audioWorklet.addModule(` should register the worklet here
+ */
+export const registeredWorklets = new Map();
+
+export function createWorkletFromSrc(name, workletSrc) {
+  const blob = new Blob(
+    [`${workletSrc}`],
+    { type: 'application/javascript' }
+  );
+  return URL.createObjectURL(blob);
+}
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/index.html b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/index.html
new file mode 100644
index 000000000..ac2e7ff0e
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/index.html
@@ -0,0 +1,47 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <title>Project Horizon - Live Agent</title>
+  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, viewport-fit=cover">
+  <meta name="theme-color" content="#1a1a1a">
+  <link rel="stylesheet" href="/static/styles.css">
+  <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200" />
+  <!-- <link rel="icon" type="image/x-icon" href="/static/favicon.ico"> -->
+
+  <!-- *** ADD THIS SCRIPT TAG FOR UUID LIBRARY *** -->
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/uuid/8.3.2/uuid.min.js" integrity="sha512-UNM1njAgOFUa74Z0bADwAq8gbTcqZC8Ej4xPSzpnh0l6KMevwvkBvbldF9uR++qKeJ+MOZHRjV1HZjoRvjDfNQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
+  <!-- ****************************************** -->
+
+</head>
+<body>
+  <div class="header-section">
+    <h1>Project Horizon</h1>
+    <span id="statusIndicator" class="status-indicator offline" title="Disconnected"></span>
+  </div>
+
+  <div id="chatOutput" class="chat-output">
+     <p class="info-message">Click Connect (▶) to start the agent session.</p>
+  </div>
+
+  <div class="controls">
+    <div id="connectButtonContainer" class="centered-button-container">
+      <button id="connectButton" class="action-button connect-button" title="Connect to Agent">
+        <span class="material-symbols-outlined">play_arrow</span>
+      </button>
+    </div>
+    <div id="mediaButtonsContainer" class="media-buttons-container hidden">
+      <button id="stopButton" class="action-button stop-button" title="Disconnect Session">
+        <span class="material-symbols-outlined">stop</span>
+      </button>
+      <button id="micButton" class="action-button mic-button" disabled title="Start/Stop Recording">
+        <span class="material-symbols-outlined">mic</span>
+      </button>
+    </div>
+  </div>
+
+  <!-- Load JS Libraries -->
+  <script src="https://cdn.jsdelivr.net/npm/eventemitter3@5.0.1/dist/eventemitter3.umd.min.js"></script>
+  <!-- Your main application logic (ensure UUID script is loaded before this) -->
+  <script type="module" src="/static/adk-websocket-api.js"></script>
+</body>
+</html>
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/styles.css b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/styles.css
new file mode 100644
index 000000000..6927f8230
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/styles.css
@@ -0,0 +1,181 @@
+/* app/static/styles.css - Simplified Styles */
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+    display: flex;
+    flex-direction: column;
+    height: 100vh; /* Full viewport height */
+    background-color: #1e1e1e; /* Dark background */
+    color: #e0e0e0; /* Light text */
+    overflow: hidden; /* Prevent body scroll */
+}
+
+.header-section {
+    background-color: #333;
+    color: white;
+    padding: 10px 20px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    border-bottom: 1px solid #444;
+    flex-shrink: 0; /* Prevent header from shrinking */
+}
+
+.header-section h1 {
+    margin: 0;
+    font-size: 1.4em;
+    font-weight: 500;
+}
+
+.status-indicator {
+    display: inline-block;
+    width: 12px;
+    height: 12px;
+    border-radius: 50%;
+    background-color: #dc3545; /* Offline: Red */
+    margin-left: 10px;
+    transition: background-color 0.3s ease;
+}
+.status-indicator.online { background-color: #28a745; } /* Online: Green */
+.status-indicator.error { background-color: #ffc107; } /* Error: Yellow/Orange */
+
+.chat-output {
+    flex-grow: 1; /* Takes up remaining vertical space */
+    overflow-y: auto; /* Enable scrolling for chat messages */
+    padding: 15px;
+    background-color: #2a2a2a; /* Slightly lighter dark */
+    display: flex;
+    flex-direction: column;
+}
+
+.chat-output p {
+    margin-bottom: 10px;
+    padding: 10px 15px;
+    border-radius: 18px;
+    max-width: 75%;
+    word-wrap: break-word;
+    line-height: 1.4;
+}
+
+.user-message {
+    background-color: #007bff; /* Blue for user */
+    color: white;
+    align-self: flex-end;
+    margin-left: 25%;
+}
+
+.agent-message {
+    background-color: #444; /* Dark grey for agent */
+    color: #e0e0e0;
+    align-self: flex-start;
+    margin-right: 25%;
+}
+.info-message {
+    font-style: italic;
+    color: #aaa;
+    text-align: center;
+    background-color: transparent;
+    align-self: center;
+}
+.error-message {
+    color: #ff8a80; /* Light red for errors */
+    font-weight: bold;
+    text-align: center;
+    background-color: rgba(255, 0, 0, 0.1);
+    border: 1px solid #f44336;
+    align-self: center;
+    max-width: 90%;
+}
+
+.controls {
+    padding: 15px;
+    background-color: #333;
+    border-top: 1px solid #444;
+    flex-shrink: 0; /* Prevent controls from shrinking */
+}
+
+.centered-button-container, .media-buttons-container {
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    gap: 20px; /* Spacing between buttons */
+    height: 70px; /* Fixed height for control area */
+}
+
+.action-button {
+    width: 56px;
+    height: 56px;
+    border-radius: 50%;
+    border: none;
+    background-color: #555; /* Neutral dark grey */
+    color: #e0e0e0;
+    cursor: pointer;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: background-color 0.2s ease, transform 0.1s ease;
+}
+.action-button:disabled {
+    background-color: #444;
+    color: #777;
+    cursor: not-allowed;
+}
+.action-button:not(:disabled):hover {
+    background-color: #666;
+}
+.action-button:not(:disabled):active {
+    transform: scale(0.92);
+    background-color: #777;
+}
+
+/* Specific Button Styles */
+.connect-button { background-color: #28a745; color: white; } /* Green for connect */
+.connect-button:not(:disabled):hover { background-color: #218838; }
+.stop-button { background-color: #dc3545; color: white; } /* Red for stop */
+.stop-button:not(:disabled):hover { background-color: #c82333; }
+.mic-button.active { background-color: #dc3545; color: white; } /* Red when recording */
+.mic-button.muted { background-color: #6c757d; } /* Grey when muted */
+
+.hidden { display: none !important; } /* Utility to hide elements */
+
+/* Mock Toggle Styles */
+.mock-toggle-container {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    color: #ccc; /* Lighter text for toggle */
+    background-color: rgba(255, 255, 255, 0.1); /* Slight background */
+    padding: 5px 10px;
+    border-radius: 15px;
+    cursor: pointer; /* Make the whole area clickable */
+}
+.mock-toggle-container input[type="checkbox"] {
+    cursor: pointer;
+}
+.mock-toggle-container label {
+    cursor: pointer;
+    user-select: none; /* Prevent text selection on label click */
+}
+
+/* Material Symbols */
+.material-symbols-outlined {
+  font-variation-settings:
+  'FILL' 0,
+  'wght' 400,
+  'GRAD' 0,
+  'opsz' 24
+}
+
+/* Responsive Adjustments (Optional) */
+@media (max-width: 600px) {
+    .header-section h1 { font-size: 1.2em; }
+    .chat-output p { max-width: 90%; font-size: 0.95em; }
+    .action-button { width: 50px; height: 50px; }
+    .media-buttons-container { gap: 15px; }
+    .mock-toggle-container label { font-size: 0.9em; }
+}
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/utils/utils.js b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/utils/utils.js
new file mode 100644
index 000000000..eaeda55dc
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/app/static/utils/utils.js
@@ -0,0 +1,35 @@
+/**
+ * Copyright 2025 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+export async function audioContext({ sampleRate }) {
+  const context = new (window.AudioContext || window.webkitAudioContext)({ sampleRate });
+  await context.resume();
+  return context;
+}
+
+/**
+ * Convert a base64 string to an ArrayBuffer
+ * @param {string} base64 The base64 string to convert
+ * @returns {ArrayBuffer} The converted ArrayBuffer
+ */
+export function base64ToArrayBuffer(base64) {
+  const binaryString = atob(base64);
+  const bytes = new Uint8Array(binaryString.length);
+  for (let i = 0; i < binaryString.length; i++) {
+    bytes[i] = binaryString.charCodeAt(i);
+  }
+  return bytes.buffer;
+}
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/__init__.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/__init__.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/__init__.py
new file mode 100644
index 000000000..5aa0e0d6f
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/__init__.py
@@ -0,0 +1,4 @@
+from .client import A2AClient
+from .card_resolver import A2ACardResolver
+
+__all__ = ["A2AClient", "A2ACardResolver"]
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/card_resolver.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/card_resolver.py
new file mode 100644
index 000000000..f9ba17db8
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/card_resolver.py
@@ -0,0 +1,21 @@
+import httpx
+from common_impl.types import (
+    AgentCard,
+    A2AClientJSONError,
+)
+import json
+
+
+class A2ACardResolver:
+    def __init__(self, base_url, agent_card_path="/.well-known/agent.json"):
+        self.base_url = base_url.rstrip("/")
+        self.agent_card_path = agent_card_path.lstrip("/")
+
+    def get_agent_card(self) -> AgentCard:
+        with httpx.Client() as client:
+            response = client.get(self.base_url + "/" + self.agent_card_path)
+            response.raise_for_status()
+            try:
+                return AgentCard(**response.json())
+            except json.JSONDecodeError as e:
+                raise A2AClientJSONError(str(e)) from e
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/client.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/client.py
new file mode 100644
index 000000000..688afb3d8
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/client/client.py
@@ -0,0 +1,88 @@
+import httpx
+from httpx_sse import connect_sse
+from typing import Any, AsyncIterable
+from common_impl.types import (
+    AgentCard,
+    GetTaskRequest,
+    SendTaskRequest,
+    SendTaskResponse,
+    JSONRPCRequest,
+    GetTaskResponse,
+    CancelTaskResponse,
+    CancelTaskRequest,
+    SetTaskPushNotificationRequest,
+    SetTaskPushNotificationResponse,
+    GetTaskPushNotificationRequest,
+    GetTaskPushNotificationResponse,
+    A2AClientHTTPError,
+    A2AClientJSONError,
+    SendTaskStreamingRequest,
+    SendTaskStreamingResponse,
+)
+import json
+
+
+class A2AClient:
+    def __init__(self, agent_card: AgentCard = None, url: str = None):
+        if agent_card:
+            self.url = agent_card.url
+        elif url:
+            self.url = url
+        else:
+            raise ValueError("Must provide either agent_card or url")
+
+    async def send_task(self, payload: dict[str, Any]) -> SendTaskResponse:
+        # self.url += "/a2a"
+        print(f"Sending task to {self.url}")
+        request = SendTaskRequest(params=payload)
+        return SendTaskResponse(**await self._send_request(request))
+
+    async def send_task_streaming(
+        self, payload: dict[str, Any]
+    ) -> AsyncIterable[SendTaskStreamingResponse]:
+        request = SendTaskStreamingRequest(params=payload)
+        with httpx.Client(timeout=None) as client:
+            with connect_sse(
+                client, "POST", self.url, json=request.model_dump()
+            ) as event_source:
+                try:
+                    for sse in event_source.iter_sse():
+                        yield SendTaskStreamingResponse(**json.loads(sse.data))
+                except json.JSONDecodeError as e:
+                    raise A2AClientJSONError(str(e)) from e
+                except httpx.RequestError as e:
+                    raise A2AClientHTTPError(400, str(e)) from e
+
+    async def _send_request(self, request: JSONRPCRequest) -> dict[str, Any]:
+        async with httpx.AsyncClient() as client:
+            try:
+                # Image generation could take time, adding timeout
+                response = await client.post(
+                    self.url, json=request.model_dump(), timeout=30
+                )
+                response.raise_for_status()
+                return response.json()
+            except httpx.HTTPStatusError as e:
+                raise A2AClientHTTPError(e.response.status_code, str(e)) from e
+            except json.JSONDecodeError as e:
+                raise A2AClientJSONError(str(e)) from e
+
+    async def get_task(self, payload: dict[str, Any]) -> GetTaskResponse:
+        request = GetTaskRequest(params=payload)
+        return GetTaskResponse(**await self._send_request(request))
+
+    async def cancel_task(self, payload: dict[str, Any]) -> CancelTaskResponse:
+        request = CancelTaskRequest(params=payload)
+        return CancelTaskResponse(**await self._send_request(request))
+
+    async def set_task_callback(
+        self, payload: dict[str, Any]
+    ) -> SetTaskPushNotificationResponse:
+        request = SetTaskPushNotificationRequest(params=payload)
+        return SetTaskPushNotificationResponse(**await self._send_request(request))
+
+    async def get_task_callback(
+        self, payload: dict[str, Any]
+    ) -> GetTaskPushNotificationResponse:
+        request = GetTaskPushNotificationRequest(params=payload)
+        return GetTaskPushNotificationResponse(**await self._send_request(request))
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/__init__.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/__init__.py
new file mode 100644
index 000000000..10f5fa467
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/__init__.py
@@ -0,0 +1,4 @@
+from .server import A2AServer
+from .task_manager import TaskManager, InMemoryTaskManager
+
+__all__ = ["A2AServer", "TaskManager", "InMemoryTaskManager"]
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/server.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/server.py
new file mode 100644
index 000000000..89ec74cad
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/server.py
@@ -0,0 +1,120 @@
+from starlette.applications import Starlette
+from starlette.responses import JSONResponse
+from sse_starlette.sse import EventSourceResponse
+from starlette.requests import Request
+from common_impl.types import (
+    A2ARequest,
+    JSONRPCResponse,
+    InvalidRequestError,
+    JSONParseError,
+    GetTaskRequest,
+    CancelTaskRequest,
+    SendTaskRequest,
+    SetTaskPushNotificationRequest,
+    GetTaskPushNotificationRequest,
+    InternalError,
+    AgentCard,
+    TaskResubscriptionRequest,
+    SendTaskStreamingRequest,
+)
+from pydantic import ValidationError
+import json
+from typing import AsyncIterable, Any
+from common_impl.server.task_manager import TaskManager
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class A2AServer:
+    def __init__(
+        self,
+        host="0.0.0.0",
+        port=5000,
+        endpoint="/",
+        agent_card: AgentCard = None,
+        task_manager: TaskManager = None,
+    ):
+        self.host = host
+        self.port = port
+        self.endpoint = endpoint
+        self.task_manager = task_manager
+        self.agent_card = agent_card
+        self.app = Starlette()
+        self.app.add_route(self.endpoint, self._process_request, methods=["POST"])
+        self.app.add_route(
+            "/.well-known/agent.json", self._get_agent_card, methods=["GET"]
+        )
+
+    def start(self):
+        if self.agent_card is None:
+            raise ValueError("agent_card is not defined")
+
+        if self.task_manager is None:
+            raise ValueError("request_handler is not defined")
+
+        import uvicorn
+
+        uvicorn.run(self.app, host=self.host, port=self.port)
+
+    def _get_agent_card(self, request: Request) -> JSONResponse:
+        return JSONResponse(self.agent_card.model_dump(exclude_none=True))
+
+    async def _process_request(self, request: Request):
+        try:
+            body = await request.json()
+            json_rpc_request = A2ARequest.validate_python(body)
+
+            if isinstance(json_rpc_request, GetTaskRequest):
+                result = await self.task_manager.on_get_task(json_rpc_request)
+            elif isinstance(json_rpc_request, SendTaskRequest):
+                result = await self.task_manager.on_send_task(json_rpc_request)
+            elif isinstance(json_rpc_request, SendTaskStreamingRequest):
+                result = await self.task_manager.on_send_task_subscribe(
+                    json_rpc_request
+                )
+            elif isinstance(json_rpc_request, CancelTaskRequest):
+                result = await self.task_manager.on_cancel_task(json_rpc_request)
+            elif isinstance(json_rpc_request, SetTaskPushNotificationRequest):
+                result = await self.task_manager.on_set_task_push_notification(json_rpc_request)
+            elif isinstance(json_rpc_request, GetTaskPushNotificationRequest):
+                result = await self.task_manager.on_get_task_push_notification(json_rpc_request)
+            elif isinstance(json_rpc_request, TaskResubscriptionRequest):
+                result = await self.task_manager.on_resubscribe_to_task(
+                    json_rpc_request
+                )
+            else:
+                logger.warning(f"Unexpected request type: {type(json_rpc_request)}")
+                raise ValueError(f"Unexpected request type: {type(request)}")
+
+            return self._create_response(result)
+
+        except Exception as e:
+            return self._handle_exception(e)
+
+    def _handle_exception(self, e: Exception) -> JSONResponse:
+        if isinstance(e, json.decoder.JSONDecodeError):
+            json_rpc_error = JSONParseError()
+        elif isinstance(e, ValidationError):
+            json_rpc_error = InvalidRequestError(data=json.loads(e.json()))
+        else:
+            logger.error(f"Unhandled exception: {e}")
+            json_rpc_error = InternalError()
+
+        response = JSONRPCResponse(id=None, error=json_rpc_error)
+        return JSONResponse(response.model_dump(exclude_none=True), status_code=400)
+
+    def _create_response(self, result: Any) -> JSONResponse | EventSourceResponse:
+        if isinstance(result, AsyncIterable):
+
+            async def event_generator(result) -> AsyncIterable[dict[str, str]]:
+                async for item in result:
+                    yield {"data": item.model_dump_json(exclude_none=True)}
+
+            return EventSourceResponse(event_generator(result))
+        elif isinstance(result, JSONRPCResponse):
+            return JSONResponse(result.model_dump(exclude_none=True))
+        else:
+            logger.error(f"Unexpected result type: {type(result)}")
+            raise ValueError(f"Unexpected result type: {type(result)}")
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/task_manager.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/task_manager.py
new file mode 100644
index 000000000..804fcf920
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/task_manager.py
@@ -0,0 +1,277 @@
+from abc import ABC, abstractmethod
+from typing import Union, AsyncIterable, List
+from common_impl.types import Task
+from common_impl.types import (
+    JSONRPCResponse,
+    TaskIdParams,
+    TaskQueryParams,
+    GetTaskRequest,
+    TaskNotFoundError,
+    SendTaskRequest,
+    CancelTaskRequest,
+    TaskNotCancelableError,
+    SetTaskPushNotificationRequest,
+    GetTaskPushNotificationRequest,
+    GetTaskResponse,
+    CancelTaskResponse,
+    SendTaskResponse,
+    SetTaskPushNotificationResponse,
+    GetTaskPushNotificationResponse,
+    PushNotificationNotSupportedError,
+    TaskSendParams,
+    TaskStatus,
+    TaskState,
+    TaskResubscriptionRequest,
+    SendTaskStreamingRequest,
+    SendTaskStreamingResponse,
+    Artifact,
+    PushNotificationConfig,
+    TaskStatusUpdateEvent,
+    JSONRPCError,
+    TaskPushNotificationConfig,
+    InternalError,
+)
+from common_impl.server.utils import new_not_implemented_error
+import asyncio
+import logging
+
+logger = logging.getLogger(__name__)
+
+class TaskManager(ABC):
+    @abstractmethod
+    async def on_get_task(self, request: GetTaskRequest) -> GetTaskResponse:
+        pass
+
+    @abstractmethod
+    async def on_cancel_task(self, request: CancelTaskRequest) -> CancelTaskResponse:
+        pass
+
+    @abstractmethod
+    async def on_send_task(self, request: SendTaskRequest) -> SendTaskResponse:
+        pass
+
+    @abstractmethod
+    async def on_send_task_subscribe(
+        self, request: SendTaskStreamingRequest
+    ) -> Union[AsyncIterable[SendTaskStreamingResponse], JSONRPCResponse]:
+        pass
+
+    @abstractmethod
+    async def on_set_task_push_notification(
+        self, request: SetTaskPushNotificationRequest
+    ) -> SetTaskPushNotificationResponse:
+        pass
+
+    @abstractmethod
+    async def on_get_task_push_notification(
+        self, request: GetTaskPushNotificationRequest
+    ) -> GetTaskPushNotificationResponse:
+        pass
+
+    @abstractmethod
+    async def on_resubscribe_to_task(
+        self, request: TaskResubscriptionRequest
+    ) -> Union[AsyncIterable[SendTaskResponse], JSONRPCResponse]:
+        pass
+
+
+class InMemoryTaskManager(TaskManager):
+    def __init__(self):
+        self.tasks: dict[str, Task] = {}
+        self.push_notification_infos: dict[str, PushNotificationConfig] = {}
+        self.lock = asyncio.Lock()
+        self.task_sse_subscribers: dict[str, List[asyncio.Queue]] = {}
+        self.subscriber_lock = asyncio.Lock()
+
+    async def on_get_task(self, request: GetTaskRequest) -> GetTaskResponse:
+        logger.info(f"Getting task {request.params.id}")
+        task_query_params: TaskQueryParams = request.params
+
+        async with self.lock:
+            task = self.tasks.get(task_query_params.id)
+            if task is None:
+                return GetTaskResponse(id=request.id, error=TaskNotFoundError())
+
+            task_result = self.append_task_history(
+                task, task_query_params.historyLength
+            )
+
+        return GetTaskResponse(id=request.id, result=task_result)
+
+    async def on_cancel_task(self, request: CancelTaskRequest) -> CancelTaskResponse:
+        logger.info(f"Cancelling task {request.params.id}")
+        task_id_params: TaskIdParams = request.params
+
+        async with self.lock:
+            task = self.tasks.get(task_id_params.id)
+            if task is None:
+                return CancelTaskResponse(id=request.id, error=TaskNotFoundError())
+
+        return CancelTaskResponse(id=request.id, error=TaskNotCancelableError())
+
+    @abstractmethod
+    async def on_send_task(self, request: SendTaskRequest) -> SendTaskResponse:
+        pass
+
+    @abstractmethod
+    async def on_send_task_subscribe(
+        self, request: SendTaskStreamingRequest
+    ) -> Union[AsyncIterable[SendTaskStreamingResponse], JSONRPCResponse]:
+        pass
+
+    async def set_push_notification_info(self, task_id: str, notification_config: PushNotificationConfig):
+        async with self.lock:
+            task = self.tasks.get(task_id)
+            if task is None:
+                raise ValueError(f"Task not found for {task_id}")
+
+            self.push_notification_infos[task_id] = notification_config
+
+        return
+    
+    async def get_push_notification_info(self, task_id: str) -> PushNotificationConfig:
+        async with self.lock:
+            task = self.tasks.get(task_id)
+            if task is None:
+                raise ValueError(f"Task not found for {task_id}")
+
+            return self.push_notification_infos[task_id]
+            
+        return
+    
+    async def has_push_notification_info(self, task_id: str) -> bool:
+        async with self.lock:
+            return task_id in self.push_notification_infos
+            
+
+    async def on_set_task_push_notification(
+        self, request: SetTaskPushNotificationRequest
+    ) -> SetTaskPushNotificationResponse:
+        logger.info(f"Setting task push notification {request.params.id}")
+        task_notification_params: TaskPushNotificationConfig = request.params
+
+        try:
+            await self.set_push_notification_info(task_notification_params.id, task_notification_params.pushNotificationConfig)
+        except Exception as e:
+            logger.error(f"Error while setting push notification info: {e}")
+            return JSONRPCResponse(
+                id=request.id,
+                error=InternalError(
+                    message="An error occurred while setting push notification info"
+                ),
+            )
+            
+        return SetTaskPushNotificationResponse(id=request.id, result=task_notification_params)
+
+    async def on_get_task_push_notification(
+        self, request: GetTaskPushNotificationRequest
+    ) -> GetTaskPushNotificationResponse:
+        logger.info(f"Getting task push notification {request.params.id}")
+        task_params: TaskIdParams = request.params
+
+        try:
+            notification_info = await self.get_push_notification_info(task_params.id)
+        except Exception as e:
+            logger.error(f"Error while getting push notification info: {e}")
+            return GetTaskPushNotificationResponse(
+                id=request.id,
+                error=InternalError(
+                    message="An error occurred while getting push notification info"
+                ),
+            )
+        
+        return GetTaskPushNotificationResponse(id=request.id, result=TaskPushNotificationConfig(id=task_params.id, pushNotificationConfig=notification_info))
+
+    async def upsert_task(self, task_send_params: TaskSendParams) -> Task:
+        logger.info(f"Upserting task {task_send_params.id}")
+        async with self.lock:
+            task = self.tasks.get(task_send_params.id)
+            if task is None:
+                task = Task(
+                    id=task_send_params.id,
+                    sessionId = task_send_params.sessionId,
+                    messages=[task_send_params.message],
+                    status=TaskStatus(state=TaskState.SUBMITTED),
+                    history=[task_send_params.message],
+                )
+                self.tasks[task_send_params.id] = task
+            else:
+                task.history.append(task_send_params.message)
+
+            return task
+
+    async def on_resubscribe_to_task(
+        self, request: TaskResubscriptionRequest
+    ) -> Union[AsyncIterable[SendTaskStreamingResponse], JSONRPCResponse]:
+        return new_not_implemented_error(request.id)
+
+    async def update_store(
+        self, task_id: str, status: TaskStatus, artifacts: list[Artifact]
+    ) -> Task:
+        async with self.lock:
+            try:
+                task = self.tasks[task_id]
+            except KeyError:
+                logger.error(f"Task {task_id} not found for updating the task")
+                raise ValueError(f"Task {task_id} not found")
+
+            task.status = status
+
+            if status.message is not None:
+                task.history.append(status.message)
+
+            if artifacts is not None:
+                if task.artifacts is None:
+                    task.artifacts = []
+                task.artifacts.extend(artifacts)
+
+            return task
+
+    def append_task_history(self, task: Task, historyLength: int | None):
+        new_task = task.model_copy()
+        if historyLength is not None and historyLength > 0:
+            new_task.history = new_task.history[-historyLength:]
+        else:
+            new_task.history = []
+
+        return new_task        
+
+    async def setup_sse_consumer(self, task_id: str, is_resubscribe: bool = False):
+        async with self.subscriber_lock:
+            if task_id not in self.task_sse_subscribers:
+                if is_resubscribe:
+                    raise ValueError("Task not found for resubscription")
+                else:
+                    self.task_sse_subscribers[task_id] = []
+
+            sse_event_queue = asyncio.Queue(maxsize=0) # <=0 is unlimited
+            self.task_sse_subscribers[task_id].append(sse_event_queue)
+            return sse_event_queue
+
+    async def enqueue_events_for_sse(self, task_id, task_update_event):
+        async with self.subscriber_lock:
+            if task_id not in self.task_sse_subscribers:
+                return
+
+            current_subscribers = self.task_sse_subscribers[task_id]
+            for subscriber in current_subscribers:
+                await subscriber.put(task_update_event)
+
+    async def dequeue_events_for_sse(
+        self, request_id, task_id, sse_event_queue: asyncio.Queue
+    ) -> AsyncIterable[SendTaskStreamingResponse] | JSONRPCResponse:
+        try:
+            while True:                
+                event = await sse_event_queue.get()
+                if isinstance(event, JSONRPCError):
+                    yield SendTaskStreamingResponse(id=request_id, error=event)
+                    break
+                                                
+                yield SendTaskStreamingResponse(id=request_id, result=event)
+                if isinstance(event, TaskStatusUpdateEvent) and event.final:
+                    break
+        finally:
+            async with self.subscriber_lock:
+                if task_id in self.task_sse_subscribers:
+                    self.task_sse_subscribers[task_id].remove(sse_event_queue)
+
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/utils.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/utils.py
new file mode 100644
index 000000000..654b87ed0
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/server/utils.py
@@ -0,0 +1,28 @@
+from common_impl.types import (
+    JSONRPCResponse,
+    ContentTypeNotSupportedError,
+    UnsupportedOperationError,
+)
+from typing import List
+
+
+def are_modalities_compatible(
+    server_output_modes: List[str], client_output_modes: List[str]
+):
+    """Modalities are compatible if they are both non-empty
+    and there is at least one common element."""
+    if client_output_modes is None or len(client_output_modes) == 0:
+        return True
+
+    if server_output_modes is None or len(server_output_modes) == 0:
+        return True
+
+    return any(x in server_output_modes for x in client_output_modes)
+
+
+def new_incompatible_types_error(request_id):
+    return JSONRPCResponse(id=request_id, error=ContentTypeNotSupportedError())
+
+
+def new_not_implemented_error(request_id):
+    return JSONRPCResponse(id=request_id, error=UnsupportedOperationError())
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/types.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/types.py
new file mode 100644
index 000000000..585fec3b7
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/types.py
@@ -0,0 +1,365 @@
+from typing import Union, Any
+from pydantic import BaseModel, Field, TypeAdapter
+from typing import Literal, List, Annotated, Optional
+from datetime import datetime
+from pydantic import model_validator, ConfigDict, field_serializer
+from uuid import uuid4
+from enum import Enum
+from typing_extensions import Self
+
+
+class TaskState(str, Enum):
+    SUBMITTED = "submitted"
+    WORKING = "working"
+    INPUT_REQUIRED = "input-required"
+    COMPLETED = "completed"
+    CANCELED = "canceled"
+    FAILED = "failed"
+    UNKNOWN = "unknown"
+
+
+class TextPart(BaseModel):
+    type: Literal["text"] = "text"
+    text: str
+    metadata: dict[str, Any] | None = None
+
+
+class FileContent(BaseModel):
+    name: str | None = None
+    mimeType: str | None = None
+    bytes: str | None = None
+    uri: str | None = None
+
+    @model_validator(mode="after")
+    def check_content(self) -> Self:
+        if not (self.bytes or self.uri):
+            raise ValueError("Either 'bytes' or 'uri' must be present in the file data")
+        if self.bytes and self.uri:
+            raise ValueError(
+                "Only one of 'bytes' or 'uri' can be present in the file data"
+            )
+        return self
+
+
+class FilePart(BaseModel):
+    type: Literal["file"] = "file"
+    file: FileContent
+    metadata: dict[str, Any] | None = None
+
+
+class DataPart(BaseModel):
+    type: Literal["data"] = "data"
+    data: dict[str, Any]
+    metadata: dict[str, Any] | None = None
+
+
+Part = Annotated[Union[TextPart, FilePart, DataPart], Field(discriminator="type")]
+
+
+class Message(BaseModel):
+    role: Literal["user", "agent"]
+    parts: List[Part]
+    metadata: dict[str, Any] | None = None
+
+
+class TaskStatus(BaseModel):
+    state: TaskState
+    message: Message | None = None
+    timestamp: datetime = Field(default_factory=datetime.now)
+
+    @field_serializer("timestamp")
+    def serialize_dt(self, dt: datetime, _info):
+        return dt.isoformat()
+
+
+class Artifact(BaseModel):
+    name: str | None = None
+    description: str | None = None
+    parts: List[Part]
+    metadata: dict[str, Any] | None = None
+    index: int = 0
+    append: bool | None = None
+    lastChunk: bool | None = None
+
+
+class Task(BaseModel):
+    id: str
+    sessionId: str | None = None
+    status: TaskStatus
+    artifacts: List[Artifact] | None = None
+    history: List[Message] | None = None
+    metadata: dict[str, Any] | None = None
+
+
+class TaskStatusUpdateEvent(BaseModel):
+    id: str
+    status: TaskStatus
+    final: bool = False
+    metadata: dict[str, Any] | None = None
+
+
+class TaskArtifactUpdateEvent(BaseModel):
+    id: str
+    artifact: Artifact    
+    metadata: dict[str, Any] | None = None
+
+
+class AuthenticationInfo(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    schemes: List[str]
+    credentials: str | None = None
+
+
+class PushNotificationConfig(BaseModel):
+    url: str
+    token: str | None = None
+    authentication: AuthenticationInfo | None = None
+
+
+class TaskIdParams(BaseModel):
+    id: str
+    metadata: dict[str, Any] | None = None
+
+
+class TaskQueryParams(TaskIdParams):
+    historyLength: int | None = None
+
+
+class TaskSendParams(BaseModel):
+    id: str
+    sessionId: str = Field(default_factory=lambda: uuid4().hex)
+    message: Message
+    acceptedOutputModes: Optional[List[str]] = None
+    pushNotification: PushNotificationConfig | None = None
+    historyLength: int | None = None
+    metadata: dict[str, Any] | None = None
+
+
+class TaskPushNotificationConfig(BaseModel):
+    id: str
+    pushNotificationConfig: PushNotificationConfig
+
+
+## RPC Messages
+
+
+class JSONRPCMessage(BaseModel):
+    jsonrpc: Literal["2.0"] = "2.0"
+    id: int | str | None = Field(default_factory=lambda: uuid4().hex)
+
+
+class JSONRPCRequest(JSONRPCMessage):
+    method: str
+    params: dict[str, Any] | None = None
+
+
+class JSONRPCError(BaseModel):
+    code: int
+    message: str
+    data: Any | None = None
+
+
+class JSONRPCResponse(JSONRPCMessage):
+    result: Any | None = None
+    error: JSONRPCError | None = None
+
+
+class SendTaskRequest(JSONRPCRequest):
+    method: Literal["tasks/send"] = "tasks/send"
+    params: TaskSendParams
+
+
+class SendTaskResponse(JSONRPCResponse):
+    result: Task | None = None
+
+
+class SendTaskStreamingRequest(JSONRPCRequest):
+    method: Literal["tasks/sendSubscribe"] = "tasks/sendSubscribe"
+    params: TaskSendParams
+
+
+class SendTaskStreamingResponse(JSONRPCResponse):
+    result: TaskStatusUpdateEvent | TaskArtifactUpdateEvent | None = None
+
+
+class GetTaskRequest(JSONRPCRequest):
+    method: Literal["tasks/get"] = "tasks/get"
+    params: TaskQueryParams
+
+
+class GetTaskResponse(JSONRPCResponse):
+    result: Task | None = None
+
+
+class CancelTaskRequest(JSONRPCRequest):
+    method: Literal["tasks/cancel",] = "tasks/cancel"
+    params: TaskIdParams
+
+
+class CancelTaskResponse(JSONRPCResponse):
+    result: Task | None = None
+
+
+class SetTaskPushNotificationRequest(JSONRPCRequest):
+    method: Literal["tasks/pushNotification/set",] = "tasks/pushNotification/set"
+    params: TaskPushNotificationConfig
+
+
+class SetTaskPushNotificationResponse(JSONRPCResponse):
+    result: TaskPushNotificationConfig | None = None
+
+
+class GetTaskPushNotificationRequest(JSONRPCRequest):
+    method: Literal["tasks/pushNotification/get",] = "tasks/pushNotification/get"
+    params: TaskIdParams
+
+
+class GetTaskPushNotificationResponse(JSONRPCResponse):
+    result: TaskPushNotificationConfig | None = None
+
+
+class TaskResubscriptionRequest(JSONRPCRequest):
+    method: Literal["tasks/resubscribe",] = "tasks/resubscribe"
+    params: TaskIdParams
+
+
+A2ARequest = TypeAdapter(
+    Annotated[
+        Union[
+            SendTaskRequest,
+            GetTaskRequest,
+            CancelTaskRequest,
+            SetTaskPushNotificationRequest,
+            GetTaskPushNotificationRequest,
+            TaskResubscriptionRequest,
+            SendTaskStreamingRequest,
+        ],
+        Field(discriminator="method"),
+    ]
+)
+
+## Error types
+
+
+class JSONParseError(JSONRPCError):
+    code: int = -32700
+    message: str = "Invalid JSON payload"
+    data: Any | None = None
+
+
+class InvalidRequestError(JSONRPCError):
+    code: int = -32600
+    message: str = "Request payload validation error"
+    data: Any | None = None
+
+
+class MethodNotFoundError(JSONRPCError):
+    code: int = -32601
+    message: str = "Method not found"
+    data: None = None
+
+
+class InvalidParamsError(JSONRPCError):
+    code: int = -32602
+    message: str = "Invalid parameters"
+    data: Any | None = None
+
+
+class InternalError(JSONRPCError):
+    code: int = -32603
+    message: str = "Internal error"
+    data: Any | None = None
+
+
+class TaskNotFoundError(JSONRPCError):
+    code: int = -32001
+    message: str = "Task not found"
+    data: None = None
+
+
+class TaskNotCancelableError(JSONRPCError):
+    code: int = -32002
+    message: str = "Task cannot be canceled"
+    data: None = None
+
+
+class PushNotificationNotSupportedError(JSONRPCError):
+    code: int = -32003
+    message: str = "Push Notification is not supported"
+    data: None = None
+
+
+class UnsupportedOperationError(JSONRPCError):
+    code: int = -32004
+    message: str = "This operation is not supported"
+    data: None = None
+
+
+class ContentTypeNotSupportedError(JSONRPCError):
+    code: int = -32005
+    message: str = "Incompatible content types"
+    data: None = None
+
+
+class AgentProvider(BaseModel):
+    organization: str
+    url: str | None = None
+
+
+class AgentCapabilities(BaseModel):
+    streaming: bool = False
+    pushNotifications: bool = False
+    stateTransitionHistory: bool = False
+
+
+class AgentAuthentication(BaseModel):
+    schemes: List[str]
+    credentials: str | None = None
+
+
+class AgentSkill(BaseModel):
+    id: str
+    name: str
+    description: str | None = None
+    tags: List[str] | None = None
+    examples: List[str] | None = None
+    inputModes: List[str] | None = None
+    outputModes: List[str] | None = None
+
+
+class AgentCard(BaseModel):
+    name: str
+    description: str | None = None
+    url: str
+    provider: AgentProvider | None = None
+    version: str
+    documentationUrl: str | None = None
+    capabilities: AgentCapabilities
+    authentication: AgentAuthentication | None = None
+    defaultInputModes: List[str] = ["text"]
+    defaultOutputModes: List[str] = ["text"]
+    skills: List[AgentSkill]
+
+
+class A2AClientError(Exception):
+    pass
+
+
+class A2AClientHTTPError(A2AClientError):
+    def __init__(self, status_code: int, message: str):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(f"HTTP Error {status_code}: {message}")
+
+
+class A2AClientJSONError(A2AClientError):
+    def __init__(self, message: str):
+        self.message = message
+        super().__init__(f"JSON Error: {message}")
+
+
+class MissingAPIKeyError(Exception):
+    """Exception for missing API key."""
+
+    pass
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/utils/in_memory_cache.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/utils/in_memory_cache.py
new file mode 100644
index 000000000..06a29fe57
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/utils/in_memory_cache.py
@@ -0,0 +1,109 @@
+"""In Memory Cache utility."""
+
+import threading
+import time
+from typing import Any, Dict, Optional
+
+
+class InMemoryCache:
+    """A thread-safe Singleton class to manage cache data.
+
+    Ensures only one instance of the cache exists across the application.
+    """
+
+    _instance: Optional["InMemoryCache"] = None
+    _lock: threading.Lock = threading.Lock()
+    _initialized: bool = False
+
+    def __new__(cls):
+        """Override __new__ to control instance creation (Singleton pattern).
+
+        Uses a lock to ensure thread safety during the first instantiation.
+
+        Returns:
+            The singleton instance of InMemoryCache.
+        """
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        """Initialize the cache storage.
+
+        Uses a flag (_initialized) to ensure this logic runs only on the very first
+        creation of the singleton instance.
+        """
+        if not self._initialized:
+            with self._lock:
+                if not self._initialized:
+                    # print("Initializing SessionCache storage")
+                    self._cache_data: Dict[str, Dict[str, Any]] = {}
+                    self._ttl: Dict[str, float] = {}
+                    self._data_lock: threading.Lock = threading.Lock()
+                    self._initialized = True
+
+    def set(self, key: str, value: Any, ttl: Optional[int] = None) -> None:
+        """Set a key-value pair.
+
+        Args:
+            key: The key for the data.
+            value: The data to store.
+            ttl: Time to live in seconds. If None, data will not expire.
+        """
+        with self._data_lock:
+            self._cache_data[key] = value
+
+            if ttl is not None:
+                self._ttl[key] = time.time() + ttl
+            else:
+                if key in self._ttl:
+                    del self._ttl[key]
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """Get the value associated with a key.
+
+        Args:
+            key: The key for the data within the session.
+            default: The value to return if the session or key is not found.
+
+        Returns:
+            The cached value, or the default value if not found.
+        """
+        with self._data_lock:
+            if key in self._ttl and time.time() > self._ttl[key]:
+                del self._cache_data[key]
+                del self._ttl[key]
+                return default
+            return self._cache_data.get(key, default)
+
+    def delete(self, key: str) -> None:
+        """Delete a specific key-value pair from a cache.
+
+        Args:
+            key: The key to delete.
+
+        Returns:
+            True if the key was found and deleted, False otherwise.
+        """
+
+        with self._data_lock:
+            if key in self._cache_data:
+                del self._cache_data[key]
+                if key in self._ttl:
+                    del self._ttl[key]
+                return True
+            return False
+
+    def clear(self) -> bool:
+        """Remove all data.
+
+        Returns:
+            True if the data was cleared, False otherwise.
+        """
+        with self._data_lock:
+            self._cache_data.clear()
+            self._ttl.clear()
+            return True
+        return False
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/utils/push_notification_auth.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/utils/push_notification_auth.py
new file mode 100644
index 000000000..cc74ade0d
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/common_impl/utils/push_notification_auth.py
@@ -0,0 +1,135 @@
+from jwcrypto import jwk
+import uuid
+from starlette.responses import JSONResponse
+from starlette.requests import Request
+from typing import Any
+
+import jwt
+import time
+import json
+import hashlib
+import httpx
+import logging
+
+from jwt import PyJWK, PyJWKClient
+
+logger = logging.getLogger(__name__)
+AUTH_HEADER_PREFIX = 'Bearer '
+
+class PushNotificationAuth:
+    def _calculate_request_body_sha256(self, data: dict[str, Any]):
+        """Calculates the SHA256 hash of a request body.
+
+        This logic needs to be same for both the agent who signs the payload and the client verifier.
+        """
+        body_str = json.dumps(
+            data,
+            ensure_ascii=False,
+            allow_nan=False,
+            indent=None,
+            separators=(",", ":"),
+        )
+        return hashlib.sha256(body_str.encode()).hexdigest()
+
+class PushNotificationSenderAuth(PushNotificationAuth):
+    def __init__(self):
+        self.public_keys = []
+        self.private_key_jwk: PyJWK = None
+
+    @staticmethod
+    async def verify_push_notification_url(url: str) -> bool:
+        async with httpx.AsyncClient(timeout=10) as client:
+            try:
+                validation_token = str(uuid.uuid4())
+                response = await client.get(
+                    url,
+                    params={"validationToken": validation_token}
+                )
+                response.raise_for_status()
+                is_verified = response.text == validation_token
+
+                logger.info(f"Verified push-notification URL: {url} => {is_verified}")            
+                return is_verified                
+            except Exception as e:
+                logger.warning(f"Error during sending push-notification for URL {url}: {e}")
+
+        return False
+
+    def generate_jwk(self):
+        key = jwk.JWK.generate(kty='RSA', size=2048, kid=str(uuid.uuid4()), use="sig")
+        self.public_keys.append(key.export_public(as_dict=True))
+        self.private_key_jwk = PyJWK.from_json(key.export_private())
+    
+    def handle_jwks_endpoint(self, _request: Request):
+        """Allow clients to fetch public keys.
+        """
+        return JSONResponse({
+            "keys": self.public_keys
+        })
+    
+    def _generate_jwt(self, data: dict[str, Any]):
+        """JWT is generated by signing both the request payload SHA digest and time of token generation.
+
+        Payload is signed with private key and it ensures the integrity of payload for client.
+        Including iat prevents from replay attack.
+        """
+        
+        iat = int(time.time())
+
+        return jwt.encode(
+            {"iat": iat, "request_body_sha256": self._calculate_request_body_sha256(data)},
+            key=self.private_key_jwk,
+            headers={"kid": self.private_key_jwk.key_id},
+            algorithm="RS256"
+        )
+
+    async def send_push_notification(self, url: str, data: dict[str, Any]):
+        jwt_token = self._generate_jwt(data)
+        headers = {'Authorization': f"Bearer {jwt_token}"}
+        async with httpx.AsyncClient(timeout=10) as client: 
+            try:
+                response = await client.post(
+                    url,
+                    json=data,
+                    headers=headers
+                )
+                response.raise_for_status()
+                logger.info(f"Push-notification sent for URL: {url}")                            
+            except Exception as e:
+                logger.warning(f"Error during sending push-notification for URL {url}: {e}")
+
+class PushNotificationReceiverAuth(PushNotificationAuth):
+    def __init__(self):
+        self.public_keys_jwks = []
+        self.jwks_client = None
+
+    async def load_jwks(self, jwks_url: str):
+        self.jwks_client = PyJWKClient(jwks_url)
+    
+    async def verify_push_notification(self, request: Request) -> bool:
+        auth_header = request.headers.get("Authorization")
+        if not auth_header or not auth_header.startswith(AUTH_HEADER_PREFIX):
+            print("Invalid authorization header")
+            return False
+        
+        token = auth_header[len(AUTH_HEADER_PREFIX):]
+        signing_key = self.jwks_client.get_signing_key_from_jwt(token)
+
+        decode_token = jwt.decode(
+            token,
+            signing_key,
+            options={"require": ["iat", "request_body_sha256"]},
+            algorithms=["RS256"],
+        )
+
+        actual_body_sha256 = self._calculate_request_body_sha256(await request.json())
+        if actual_body_sha256 != decode_token["request_body_sha256"]:
+            # Payload signature does not match the digest in signed token.
+            raise ValueError("Invalid request body")
+        
+        if time.time() - decode_token["iat"] > 60 * 5:
+            # Do not allow push-notifications older than 5 minutes.
+            # This is to prevent replay attack.
+            raise ValueError("Token is expired")
+        
+        return True
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/__init__.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/agent.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/agent.py
new file mode 100644
index 000000000..f9d7852bc
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/agent.py
@@ -0,0 +1,92 @@
+import logging
+import os
+import asyncio # For running the discovery at startup
+
+from google.adk.agents import Agent
+from google.adk.agents.callback_context import CallbackContext
+from google.adk.tools import ToolContext # For callback type hints
+
+from typing import Dict, Any, List, Optional # Added List, Optional
+
+from dotenv import load_dotenv
+
+# Environment Loading (as before)
+dotenv_path = os.path.join(os.path.dirname(__file__), '..', '.env')
+load_dotenv(dotenv_path=dotenv_path, override=True)
+
+# Import the new tool and discovery cache
+try:
+    from .tools import delegate_tool, DISCOVERED_SPECIALIST_AGENTS, initialize_specialist_agents_discovery
+except ImportError as e:
+    logging.critical(f"Failed to import tools for HostAgent: {e}")
+    delegate_tool = None
+    DISCOVERED_SPECIALIST_AGENTS = {}
+    initialize_specialist_agents_discovery = None # Define it to avoid NameError later
+
+logger = logging.getLogger(__name__)
+MODEL_ID_LIVE = os.getenv("LIVE_SERVER_MODEL", "gemini-2.0-flash-live-001")
+
+# --- Dynamic Instruction Builder ---
+def get_host_agent_instruction() -> str:
+    base_instruction = (
+        "You are a friendly financial assistant interacting via voice and text. "
+        "Your primary function is to help users with financial queries.\n"
+        "You have access to a set of specialist agents. "
+        "Based on the user's request, determine if a specialist agent is suitable. "
+        "If so, use the 'delegate_task_to_specialist' tool. Provide the specialist's name and the user's query (e.g., just the stock symbol for stock lookups).\n"
+        "If the user asks for a stock price (e.g., 'price of GOOGL', 'how is MSFT doing?'), "
+        "delegate to the specialist agent whose description mentions stock prices or financial data. The 'query_payload' for this tool should be the stock ticker symbol.\n"
+        "After the specialist responds:\n"
+        "  - If successful, relay the information clearly (e.g., 'Microsoft (MSFT) is currently trading at $150.25 USD.').\n"
+        "  - If there's an error, inform the user politely (e.g., 'Sorry, I couldn't retrieve that information right now.').\n"
+        "Handle other conversational turns naturally.\n\n"
+        "Available Specialist Agents:\n"
+    )
+    if not DISCOVERED_SPECIALIST_AGENTS:
+        base_instruction += "- None discovered. You must handle all queries yourself if possible, or state you cannot fulfill the request.\n"
+    else:
+        for name, card in DISCOVERED_SPECIALIST_AGENTS.items():
+            description = card.description or "No description provided."
+            # Try to get a skill description if top-level description is generic
+            if "Provides current stock price information" in description and card.skills: # Heuristic
+                skill_desc = next((s.description for s in card.skills if "stock" in s.name.lower() or (s.description and "stock" in s.description.lower())), None)
+                if skill_desc : description = skill_desc
+
+            base_instruction += f"- Name: '{name}', Description: '{description}'\n"
+    
+    logger.debug(f"Generated HostAgent Instruction:\n{base_instruction}")
+    return base_instruction
+
+# --- Agent Definition ---
+host_agent: Optional[Agent] = None
+
+async def create_host_agent() -> Optional[Agent]:
+    """
+    Asynchronously initializes specialist agents and creates the HostAgent.
+    Should be called once at application startup.
+    """
+    global host_agent # Allow modification of the global host_agent
+    if initialize_specialist_agents_discovery:
+        await initialize_specialist_agents_discovery()
+    else:
+        logger.error("initialize_specialist_agents_discovery function not available. Cannot discover specialists.")
+        return None
+
+    if not delegate_tool:
+        logger.critical("Host Agent could not be created because 'delegate_tool' failed to load.")
+        return None
+
+    current_instruction = get_host_agent_instruction()
+    host_agent = Agent(
+        name="HostAgentLiveDynamic",
+        model=MODEL_ID_LIVE,
+        description="User-facing agent that dynamically delegates to discovered specialist agents.",
+        instruction=current_instruction,
+        tools=[delegate_tool], # Use the new dynamic delegation tool
+    )
+    logger.info(f"ADK Host Agent '{host_agent.name}' created with model '{MODEL_ID_LIVE}'.")
+    return host_agent
+
+# Note: The actual creation of the host_agent instance will happen
+# in the live_server.py by calling create_host_agent().
+# This ensures discovery happens in an async context.
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/tools.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/tools.py
new file mode 100644
index 000000000..a7045c8e6
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/host_agent/tools.py
@@ -0,0 +1,190 @@
+import os
+import uuid
+import logging
+import json
+from typing import Dict, Any, List, Optional # Added List, Optional
+import httpx # For fetching agent card
+
+# ADK Imports
+from google.adk.tools import ToolContext, FunctionTool
+
+# A2A Imports
+try:
+    from common_impl.client import A2AClient, A2ACardResolver # Added A2ACardResolver
+    from common_impl.types import (
+        TaskSendParams, Message, TextPart, TaskState, DataPart,
+        A2AClientHTTPError, A2AClientJSONError, AgentCard # Added AgentCard
+    )
+except ImportError:
+    raise ImportError("Could not import A2A common library...")
+
+logger = logging.getLogger(__name__)
+
+# --- Specialist Agent Configuration ---
+# Instead of full URL, we'll use base URLs to discover Agent Cards
+# This could be a list if you have multiple specialists
+SPECIALIST_AGENT_BASE_URLS_STR = os.getenv("SPECIALIST_AGENT_BASE_URLS", "http://localhost:8001") # Default to stock agent
+SPECIALIST_AGENT_BASE_URLS = [url.strip() for url in SPECIALIST_AGENT_BASE_URLS_STR.split(',') if url.strip()]
+
+# Cache for discovered agent cards, keyed by agent name (or another unique ID from the card)
+DISCOVERED_SPECIALIST_AGENTS: Dict[str, AgentCard] = {}
+
+async def initialize_specialist_agents_discovery():
+    """
+    Fetches Agent Cards from configured specialist base URLs and populates the cache.
+    This should be called once at HostAgent startup.
+    """
+    if not SPECIALIST_AGENT_BASE_URLS:
+        logger.warning("No specialist agent base URLs configured. HostAgent cannot delegate via A2A.")
+        return
+
+    logger.info(f"Discovering specialist agents from base URLs: {SPECIALIST_AGENT_BASE_URLS}")
+    async with httpx.AsyncClient() as client:
+        for base_url in SPECIALIST_AGENT_BASE_URLS:
+            card_url = f"{base_url.rstrip('/')}/.well-known/agent.json"
+            try:
+                logger.info(f"Fetching Agent Card from: {card_url}")
+                # In a real A2A client, you'd use A2ACardResolver,
+                # but for simplicity in this tool, direct httpx get.
+                # resolver = A2ACardResolver(base_url=base_url) # This is synchronous, needs async wrapper or use httpx
+                
+                response = await client.get(card_url, timeout=10.0)
+                response.raise_for_status()
+                card_data = response.json()
+                agent_card = AgentCard(**card_data)
+
+                if not agent_card.name:
+                    logger.error(f"Agent Card from {card_url} is missing a name. Skipping.")
+                    continue
+                if not agent_card.url: # This is the A2A endpoint URL
+                    logger.error(f"Agent Card for '{agent_card.name}' from {card_url} is missing the A2A 'url'. Skipping.")
+                    continue
+
+                DISCOVERED_SPECIALIST_AGENTS[agent_card.name] = agent_card
+                logger.info(f"Discovered Specialist Agent: '{agent_card.name}' - A2A URL: {agent_card.url}")
+                logger.debug(f"  Description: {agent_card.description}")
+                logger.debug(f"  Skills: {[s.name for s in agent_card.skills]}")
+
+            except httpx.HTTPStatusError as e:
+                logger.error(f"Failed to fetch Agent Card from {card_url}: HTTP {e.response.status_code}")
+            except (httpx.RequestError, json.JSONDecodeError, ValueError) as e: # ValueError for Pydantic validation
+                logger.error(f"Error processing Agent Card from {card_url}: {e}", exc_info=True)
+    
+    if not DISCOVERED_SPECIALIST_AGENTS:
+        logger.warning("No specialist agents were successfully discovered.")
+
+
+async def delegate_task_to_specialist(
+    specialist_agent_name: str,
+    query_payload: str, # Could be a JSON string for more complex inputs
+    tool_context: ToolContext
+) -> Dict[str, Any]:
+    """
+    Dynamically delegates a task to a discovered specialist agent using A2A.
+
+    Args:
+        specialist_agent_name: The name of the specialist agent (must match a discovered AgentCard.name).
+        query_payload: The core query or data to send to the specialist (e.g., stock symbol, or JSON string of params).
+        tool_context: The ADK ToolContext.
+
+    Returns:
+        A dictionary containing the specialist's processed result or an error.
+    """
+    logger.info(f"ADK Tool: 'delegate_task_to_specialist' for '{specialist_agent_name}' with query: '{query_payload[:100]}...'")
+
+    if specialist_agent_name not in DISCOVERED_SPECIALIST_AGENTS:
+        err_msg = f"Specialist agent '{specialist_agent_name}' not found or not discovered."
+        logger.error(f"ADK Tool: {err_msg}")
+        return {"status": "error", "message": err_msg}
+
+    agent_card = DISCOVERED_SPECIALIST_AGENTS[specialist_agent_name]
+    specialist_a2a_url = agent_card.url # Get A2A endpoint from the card
+
+    a2a_session_id = tool_context._invocation_context.session.id
+    a2a_task_id = uuid.uuid4().hex
+
+    # For simplicity, assume query_payload is text for now.
+    # For more complex interactions, query_payload could be a JSON string
+    # that gets parsed into multiple A2A MessageParts.
+    try:
+        # Attempt to parse if it's JSON, otherwise treat as text
+        parts_data = json.loads(query_payload)
+        if isinstance(parts_data, dict): # Expecting a dict for DataPart
+            a2a_parts = [DataPart(data=parts_data)]
+        else: # Fallback to text if not a dict
+            a2a_parts = [TextPart(text=str(query_payload))]
+    except json.JSONDecodeError:
+        a2a_parts = [TextPart(text=query_payload)]
+
+
+    a2a_client = A2AClient(url=specialist_a2a_url) # Use URL from card
+    task_params = TaskSendParams(
+        id=a2a_task_id,
+        sessionId=a2a_session_id,
+        message=Message(
+            role="user",
+            parts=a2a_parts
+        )
+    )
+
+    logger.info(f"ADK Tool: Sending A2A task to '{specialist_agent_name}' at {specialist_a2a_url}...")
+    logger.debug(f"ADK Tool: A2A TaskSendParams: {task_params.model_dump_json(indent=2)}")
+
+    try:
+        a2a_response = await a2a_client.send_task(task_params.model_dump())
+
+        if a2a_response.error:
+            error_msg = f"A2A protocol error from '{specialist_agent_name}': {a2a_response.error.message} (Code: {a2a_response.error.code})"
+            logger.error(f"ADK Tool: {error_msg}")
+            return {"status": "error", "message": error_msg, "specialist_name": specialist_agent_name}
+
+        if a2a_response.result:
+            task_result = a2a_response.result
+            logger.debug(f"ADK Tool: A2A Task Result from '{specialist_agent_name}', State: {task_result.status.state}")
+
+            if task_result.status.state == TaskState.COMPLETED and task_result.artifacts:
+                # Generic artifact extraction - you might want to make this more specific
+                # if different specialists return different artifact structures.
+                # For now, just return the first DataPart found.
+                for artifact in task_result.artifacts:
+                     if artifact.parts:
+                         for part in artifact.parts:
+                             if isinstance(part, DataPart):
+                                 data = part.data
+                                 logger.info(f"ADK Tool: Successfully retrieved data from '{specialist_agent_name}': {data}")
+                                 return {"status": "success", "data": data, "specialist_name": specialist_agent_name}
+                logger.warning(f"ADK Tool: Task from '{specialist_agent_name}' completed but no suitable DataPart artifact found.")
+                return {"status": "error", "message": "Received no parsable data artifact from specialist.", "specialist_name": specialist_agent_name}
+
+            elif task_result.status.state == TaskState.FAILED and task_result.artifacts:
+                 for artifact in task_result.artifacts:
+                     if artifact.name == "error_details" and artifact.parts:
+                          for part in artifact.parts:
+                              if isinstance(part, DataPart) and "error" in part.data:
+                                  error_msg = part.data["error"]
+                                  logger.error(f"ADK Tool: Specialist '{specialist_agent_name}' task failed: {error_msg}")
+                                  return {"status": "error", "message": f"Specialist Error: {error_msg}", "specialist_name": specialist_agent_name}
+                 logger.error(f"ADK Tool: Specialist '{specialist_agent_name}' task failed, but couldn't parse error artifact.")
+                 return {"status": "error", "message": "Specialist reported failure with unclear details.", "specialist_name": specialist_agent_name}
+            else:
+                 logger.error(f"ADK Tool: Specialist '{specialist_agent_name}' task ended in unexpected state: {task_result.status.state}")
+                 return {"status": "error", "message": f"Specialist agent '{specialist_agent_name}' ended in state: {task_result.status.state}", "specialist_name": specialist_agent_name}
+        else:
+            logger.error(f"ADK Tool: Received empty successful response from A2A server '{specialist_agent_name}'.")
+            return {"status": "error", "message": "Empty response from specialist agent.", "specialist_name": specialist_agent_name}
+
+    except A2AClientHTTPError as http_err:
+         logger.error(f"ADK Tool: HTTP Error calling specialist agent '{specialist_agent_name}': {http_err.status_code} - {http_err.message}", exc_info=True)
+         return {"status": "error", "message": f"Network error with '{specialist_agent_name}': {http_err.status_code}", "specialist_name": specialist_agent_name}
+    except Exception as e:
+        logger.error(f"ADK Tool: Unexpected error during A2A call to '{specialist_agent_name}': {e}", exc_info=True)
+        return {"status": "error", "message": f"An unexpected error occurred with '{specialist_agent_name}': {str(e)}", "specialist_name": specialist_agent_name}
+
+
+# Create the ADK FunctionTool
+delegate_tool = FunctionTool(
+    func=delegate_task_to_specialist,
+    # Name for the LLM to call
+)
+
+logger.info("✅ ADK Tool for dynamic A2A delegation defined.")
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/mcp_servers/stock_mcp_server/server.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/mcp_servers/stock_mcp_server/server.py
new file mode 100644
index 000000000..5c1d4d260
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/mcp_servers/stock_mcp_server/server.py
@@ -0,0 +1,120 @@
+import logging
+import asyncio
+from mcp.server.fastmcp import FastMCP
+import finnhub
+import sys
+import json
+import os
+from dotenv import load_dotenv
+
+# Determine the absolute path to the .env file in the project root
+# __file__ is the path to the current script (server.py)
+# os.path.dirname(__file__) is the directory of server.py
+# os.path.join(..., "..", "..") goes up two directories to the project root
+project_root_env = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".env"))
+load_dotenv(project_root_env, override=True) # Load variables from .env file
+
+# --- Configuration ---
+# Basic logging setup
+logging.basicConfig(level=logging.DEBUG, # <--- Change level to DEBUG for more detail
+                    stream=sys.stderr,
+                    format='%(asctime)s - %(name)s - %(levelname)s - MCP_SERVER - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Finnhub Client Initialization ---
+FINNHUB_API_KEY = os.getenv("FINNHUB_API_KEY")
+if not FINNHUB_API_KEY:
+    logger.warning("FINNHUB_API_KEY environment variable not found. Stock price queries will use mock data.")
+    
+finnhub_client = finnhub.Client(api_key=FINNHUB_API_KEY or "")
+if FINNHUB_API_KEY:
+    logger.info(f"Finnhub client initialized with API key: {FINNHUB_API_KEY[:3]}...{FINNHUB_API_KEY[-3:] if len(FINNHUB_API_KEY) > 6 else ''}")
+else:
+    logger.info("Finnhub client initialized without API key")
+
+# --- FastMCP Server Initialization ---
+# Creates a customizable MCP server named "Stock Price Server"
+logger.info("Initializing FastMCP server...")
+mcp = FastMCP("Stock Price Server") # Name used during initialization
+
+# --- Tool Functions (using decorators) ---
+
+@mcp.tool()
+async def get_current_stock_price(symbol: str) -> dict:
+    """
+    Retrieve the current stock price for the given ticker symbol.
+    Returns a dictionary containing the price and currency, or an error message.
+    """
+    # Log the incoming arguments (part of the MCP request's params)
+    logger.debug(f"Tool 'get_current_stock_price' called with args: {{'symbol': '{symbol}'}}")
+
+    # Check MOCK_STOCK_API environment variable
+    mock_api_enabled = os.getenv("MOCK_STOCK_API", "False").lower() == "true"
+
+    if mock_api_enabled:
+        logger.info("MOCK_STOCK_API is enabled. Returning mock data.")
+        result = {
+            "symbol": symbol.upper(),
+            "price": 123.45,
+            "currency": "USD",
+            "note": "Mock data (MOCK_STOCK_API enabled)"
+        }
+        logger.debug(f"Tool 'get_current_stock_price' returning result: {json.dumps(result, indent=2)}")
+        return result
+
+    # If MOCK_STOCK_API is not true, proceed with actual API call
+    try:
+        # Use Finnhub API to get stock quote
+        quote = finnhub_client.quote(symbol.upper())
+        
+        # Check if we have a valid quote
+        if quote and 'c' in quote and quote['c'] > 0:
+            price = quote['c']  # Current price
+            result = {
+                "symbol": symbol.upper(),
+                "price": round(price, 2),
+                "currency": "USD",  # Finnhub uses USD by default
+                "previous_close": quote.get('pc', None)  # Previous close price
+            }
+            logger.info(f"Tool: Successfully found price {result['price']} USD for {symbol} using Finnhub.")
+        else:
+            # If no valid price in quote, use previous close if available
+            if quote and 'pc' in quote and quote['pc'] > 0:
+                price = quote['pc']  # Previous close
+                result = {
+                    "symbol": symbol.upper(),
+                    "price": round(price, 2),
+                    "currency": "USD",
+                    "note": "Using previous close price"
+                }
+                logger.info(f"Tool: Using previous close price {result['price']} USD for {symbol}.")
+            else:
+                # No valid data from Finnhub
+                logger.warning(f"Tool: No current price data found for symbol {symbol}. Returning mock data.")
+                result = {
+                    "symbol": symbol.upper(),
+                    "price": 123.45,
+                    "currency": "USD",
+                    "note": "Mock data: Price not found via Finnhub"
+                }
+
+    except Exception as e:
+        logger.error(f"Tool: Error fetching price for {symbol} from Finnhub: {e}. Returning mock data.", exc_info=False)
+        result = {
+            "symbol": symbol.upper(),
+            "price": 123.45,
+            "currency": "USD",
+            "note": f"Mock data: Finnhub API error ({type(e).__name__})"
+        }
+
+    # Log the result dictionary (which will be the MCP response's result field)
+    logger.debug(f"Tool 'get_current_stock_price' returning result: {json.dumps(result, indent=2)}")
+    return result
+
+
+# --- Main Execution ---
+if __name__ == "__main__":
+    # --- To run the MCP server (original behavior) ---
+    logger.info("Starting FastMCP Stock Price Server...")
+    mcp.run() # defaults to stdio transport
+    logger.info("FastMCP Stock Price Server stopped.")
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/requirements.txt b/project-horizon-adk/examples/stock_lookup_custom_a2a/requirements.txt
new file mode 100644
index 000000000..2fb6de98f
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/requirements.txt
@@ -0,0 +1,21 @@
+# Google Libraries
+google-adk==0.3.0
+google-genai==1.12.1
+
+# Core application & web serving
+python-dotenv==1.1.0
+fastapi==0.115.12
+uvicorn[standard]==0.34.2 # Includes websockets, http-tools
+starlette==0.46.2
+sse-starlette==2.3.3
+httpx==0.28.1
+httpx-sse==0.4.0
+pydantic==2.11.3
+
+# MCP & Tools
+mcp[cli] @ git+https://github.com/modelcontextprotocol/python-sdk.git@b4c7db6a50a5c88bae1db5c1f7fba44d16eebc6e
+finnhub-python==2.4.23
+
+# Testing
+pytest==8.3.5
+pytest-asyncio==0.26.0
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/__init__.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/__main__.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/__main__.py
new file mode 100644
index 000000000..2dae31b14
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/__main__.py
@@ -0,0 +1,128 @@
+import os
+import logging
+import click
+from dotenv import load_dotenv
+
+# A2A Imports from common library
+from common_impl.server import A2AServer
+from common_impl.types import AgentCard, AgentCapabilities, AgentSkill
+
+# Local Imports
+from .task_manager import StockInfoTaskManager
+
+# --- Configuration ---
+# Load .env file from the project root
+dotenv_path = os.path.join(os.path.dirname(__file__), '..', '..', '.env')
+load_dotenv(dotenv_path=dotenv_path, override=True)
+
+logging.basicConfig(level=os.getenv('LOG_LEVEL', 'INFO').upper(),
+                    format='%(asctime)s - %(name)s - %(levelname)s - A2A_SERVER - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Environment Variable Defaults for Click Options ---
+# These are read once at module load time after .env is processed.
+# Click options will use these values for defaults and to determine 'required' status.
+ENV_STOCK_INFO_AGENT_A2A_SERVER_HOST = os.getenv("STOCK_INFO_AGENT_A2A_SERVER_HOST")
+ENV_STOCK_INFO_AGENT_A2A_SERVER_PORT = os.getenv("STOCK_INFO_AGENT_A2A_SERVER_PORT")
+ENV_STOCK_MCP_SERVER_PATH = os.getenv("STOCK_MCP_SERVER_PATH")
+
+# --- Main Function ---
+@click.command()
+@click.option("--host",
+              default=ENV_STOCK_INFO_AGENT_A2A_SERVER_HOST,
+              help="Host to bind the A2A server to. Reads from STOCK_INFO_AGENT_A2A_SERVER_HOST environment variable.",
+              required=ENV_STOCK_INFO_AGENT_A2A_SERVER_HOST is None,
+              show_default=True)
+@click.option("--port",
+              default=ENV_STOCK_INFO_AGENT_A2A_SERVER_PORT,
+              type=int,
+              help="Port to bind the A2A server to. Reads from STOCK_INFO_AGENT_A2A_SERVER_PORT environment variable.",
+              required=ENV_STOCK_INFO_AGENT_A2A_SERVER_PORT is None,
+              show_default=True)
+@click.option("--mcp-server-path",
+              default=ENV_STOCK_MCP_SERVER_PATH,
+              help="Absolute path to the stock_mcp_server.py script. Reads from STOCK_MCP_SERVER_PATH environment variable.",
+              required=ENV_STOCK_MCP_SERVER_PATH is None,
+              show_default=True # Added for consistency, Click will show the default if available.
+             )
+
+
+def main(host: str, port: int, mcp_server_path: str):
+    """Starts the StockInfoAgent A2A Server."""
+    logger.info("Starting StockInfoAgent A2A Server...")
+    logger.info(f"  Host: {host}")
+    logger.info(f"  Port: {port}")
+    logger.info(f"  MCP Server Script Path: {mcp_server_path}")
+
+    # --- Convert MCP Server Path to Absolute if Necessary ---
+    if not os.path.isabs(mcp_server_path):
+        logger.info(f"MCP server path '{mcp_server_path}' is relative. Converting to absolute path.")
+        # Assume the relative path is from the project root or current working directory.
+        # For consistency, let's resolve it relative to the project root.
+        # The .env file is at project_root/.env, and this script is at project_root/specialist_agents/stock_info_agent/__main__.py
+        # So, to get to the project root from this script's directory: os.path.dirname(__file__)/../../
+        project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+        mcp_server_path = os.path.join(project_root, mcp_server_path)
+        logger.info(f"Resolved MCP server path to: {mcp_server_path}")
+    
+    # --- Verify MCP Server Path ---
+    if not os.path.exists(mcp_server_path):
+        logger.error(f"Error: MCP server script not found at: '{mcp_server_path}'")
+        click.echo(f"Error: MCP server script not found at: '{mcp_server_path}'", err=True)
+        return
+
+    try:
+        # --- Define Agent Card Programmatically ---
+        capabilities = AgentCapabilities(streaming=False) # Synchronous agent
+        skill = AgentSkill(
+            id="get_stock_price_skill",
+            name="Get Stock Price",
+            description="Retrieves the current price and currency for a given stock ticker symbol.",
+            tags=["finance", "stocks", "price lookup"],
+            examples=["What is the price of GOOGL?", "Stock price for MSFT"],
+            outputModes=["application/json"] # Skill specific output
+        )
+        agent_card = AgentCard(
+            name="StockInfoAgent",
+            description="Provides current stock price information using the yfinance library via MCP.",
+            url=f"http://{host}:{port}", # Define the A2A endpoint path
+            provider={"organization": "ProjectHorizon"},
+            version="1.0.0",
+            defaultInputModes=["text/plain"],
+            defaultOutputModes=["application/json"],
+            capabilities=capabilities,
+            skills=[skill],
+            authentication=None # No auth for this example
+        )
+        logger.info(f"Agent Card created for: {agent_card.name}")
+
+        # --- Initialize Task Manager & A2A Server ---
+        task_manager = StockInfoTaskManager(mcp_server_script_path=mcp_server_path)
+        logger.debug("StockInfoTaskManager initialized.")
+
+        # The A2AServer from common likely takes care of setting up ASGI routes
+        # Pass host/port here if the common A2AServer uses them for its internal uvicorn run
+        server = A2AServer(
+            agent_card=agent_card,
+            task_manager=task_manager,
+            host=host,
+            port=port,
+            # endpoint="/a2a" # Specify the endpoint path for A2A methods
+        )
+        logger.info(f"A2A Server configured to listen on {host}:{port} at endpoint '{server.endpoint}'")
+
+        # --- Start the Server ---
+        server.start()
+
+    except ValueError as ve:
+         logger.error(f"Configuration error during startup: {ve}")
+         click.echo(f"Configuration Error: {ve}", err=True)
+    except FileNotFoundError as fnf:
+        logger.error(f"File not found error during startup: {fnf}")
+        click.echo(f"File Not Found Error: {fnf}", err=True)
+    except Exception as e:
+        logger.critical(f"An unexpected error occurred during server startup: {e}", exc_info=True)
+        click.echo(f"Critical Error: {e}", err=True)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/agent.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/agent.py
new file mode 100644
index 000000000..d60ca7b22
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/agent.py
@@ -0,0 +1,102 @@
+import asyncio
+import os
+import sys
+import logging
+from contextlib import AsyncExitStack # Crucial for managing the MCP connection
+
+# --- ADK Imports ---
+from google.adk.agents import Agent  # Using the alias
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.genai import types as genai_types # Use alias to avoid confusion
+
+# --- MCP Client Imports ---
+# Import the toolset and connection parameters from ADK's MCP integration
+from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters
+
+# --- Environment Loading (Optional but recommended) ---
+from dotenv import load_dotenv
+# Load .env file from the parent directory (live-agent-project)
+dotenv_path = os.path.join(os.path.dirname(__file__), '..', '..', '.env')
+print(f"dotenv_path: {dotenv_path}")
+load_dotenv(dotenv_path=dotenv_path, override=True)
+
+
+# --- Logging Setup ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# In specialist_agents/stock_info_agent/agent.py, after load_dotenv:
+logger.info(f"--- Loaded Environment Variables ---")
+logger.info(f"VERTEX_AI?: {os.getenv('GOOGLE_GENAI_USE_VERTEXAI')}")
+logger.info(f"PROJECT_ID: {os.getenv('GOOGLE_CLOUD_PROJECT')}")
+logger.info(f"LOCATION:   {os.getenv('GOOGLE_CLOUD_LOCATION')}")
+logger.info(f"---------------------------------")
+
+# --- Constants ---
+APP_NAME = "stock_info_adk_app"
+USER_ID = "test_user_stock"
+SESSION_ID = "session_stock_001"
+MODEL = os.getenv('STOCK_INFO_AGENT_MODEL', "gemini-2.0-flash-001")
+
+# --- MCP Tool Loading Function ---
+async def get_mcp_tools_async(mcp_server_script_path: str) -> tuple[list, AsyncExitStack]:
+    """
+    Connects to the MCP server via stdio and retrieves its tools.
+
+    Args:
+        mcp_server_script_path: The absolute path to the MCP server Python script.
+
+    Returns:
+        A tuple containing the list of ADK-compatible tools and the AsyncExitStack
+        for managing the connection lifecycle.
+    """
+    logger.info(f"Attempting to connect to MCP server via stdio: {mcp_server_script_path}")
+    try:
+        # Configure parameters to launch the MCP server script via stdio
+        connection_params = StdioServerParameters(
+            command=sys.executable,  # Use the current Python interpreter
+            args=[mcp_server_script_path], # Pass the script path as an argument
+            # Add cwd if necessary, depending on how server.py resolves things
+            # cwd=os.path.dirname(mcp_server_script_path)
+        )
+
+        # Use ADK's MCPToolset to connect and get tools
+        # This starts the server.py script as a subprocess
+        tools, exit_stack = await MCPToolset.from_server(
+            connection_params=connection_params
+        )
+        logger.info(f"Successfully connected to MCP server and retrieved {len(tools)} tool(s).")
+        return tools, exit_stack
+    except Exception as e:
+        logger.error(f"Failed to connect to MCP server or get tools: {e}", exc_info=True)
+        raise # Re-raise the exception to stop execution if connection fails
+
+# --- ADK Agent Definition Function ---
+async def create_agent_with_mcp_tools(mcp_server_script_path: str) -> tuple[Agent, AsyncExitStack]:
+    """
+    Creates the ADK Agent, equipping it with tools loaded from the MCP server.
+
+    Args:
+        mcp_server_script_path: Absolute path to the MCP server script.
+
+    Returns:
+        A tuple containing the configured ADK Agent instance and the AsyncExitStack.
+    """
+    mcp_tools, exit_stack = await get_mcp_tools_async(mcp_server_script_path)
+
+    # Define the ADK Agent that will use the MCP tool(s)
+    stock_info_agent = Agent(
+        name="stock_info_agent",
+        model=MODEL,
+        description="Provides current stock price information using an external tool.",
+        instruction="You are an assistant that provides stock prices. "
+                    "When asked for the price of a stock, use the 'get_current_stock_price' tool. "
+                    "The tool takes a 'symbol' argument (e.g., 'MSFT'). "
+                    "The tool returns a dictionary with 'price' and 'currency', or an 'error'. "
+                    "Relay the information clearly to the user. If the tool returns an error, state that.",
+        # Provide the tools loaded from the MCP server to the ADK agent
+        tools=mcp_tools,
+    )
+    logger.info(f"ADK Agent '{stock_info_agent.name}' created with MCP tools.")
+    return stock_info_agent, exit_stack
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/task_manager.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/task_manager.py
new file mode 100644
index 000000000..fff8068bc
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/specialist_agents/stock_info_agent/task_manager.py
@@ -0,0 +1,238 @@
+import os
+import logging
+import json
+from typing import Union, AsyncIterable # <--- Import these
+
+from google.adk.agents import Agent # Import ADK Agent
+from google.adk.runners import Runner # Import ADK Runner
+from google.adk.sessions import InMemorySessionService, Session # Need Session components
+from google.genai import types as genai_types # For Content/Part
+
+from common_impl.server.task_manager import InMemoryTaskManager # Use the base class
+from common_impl.types import (
+    SendTaskRequest, SendTaskResponse, Task, TaskState, TaskStatus,
+    Artifact, TextPart, DataPart, JSONRPCError, InternalError,
+    SendTaskStreamingRequest, SendTaskStreamingResponse, # <--- Import these
+    UnsupportedOperationError, JSONRPCResponse # <--- Import these
+)
+
+
+from .agent import create_agent_with_mcp_tools
+from mcp.types import TextContent, CallToolResult
+
+
+
+logger = logging.getLogger(__name__)
+
+class StockInfoTaskManager(InMemoryTaskManager):
+    """Handles A2A tasks by running the ADK StockInfoAgent."""
+
+    def __init__(self, mcp_server_script_path: str):
+        super().__init__()
+        # Store the path needed by create_agent_with_mcp_tools
+        self.mcp_server_script_path = mcp_server_script_path
+        logger.info(f"StockInfoTaskManager initialized (ADK Runner Mode). Will use MCP server at: {self.mcp_server_script_path}")
+        # NOTE: Agent and Runner are now created PER request in this model
+
+    async def on_send_task(self, request: SendTaskRequest) -> SendTaskResponse:
+        """Handles A2A task by instantiating and running the ADK agent."""
+        task_params = request.params
+        task_id = task_params.id
+        session_id = task_params.sessionId
+        input_message = task_params.message
+
+        logger.info(f"A2A Task Mgr (ADK Mode): Received task '{task_id}' in session '{session_id}'")
+
+        # 1. Create/Update Task State (same as before)
+        task = await self.upsert_task(task_params)
+        task.status.state = TaskState.WORKING
+        await self.update_store(task_id, task.status, None)
+        logger.debug(f"A2A Task Mgr (ADK Mode): Task '{task_id}' state set to WORKING.")
+
+        # 2. Extract Symbol (same as before)
+        symbol = None
+        if input_message.parts:
+            for part in input_message.parts:
+                if isinstance(part, TextPart) and part.text:
+                    symbol = part.text.strip()
+                    logger.info(f"A2A Task Mgr (ADK Mode): Extracted symbol '{symbol}' for task '{task_id}'.")
+                    break
+        if not symbol:
+            # Handle error (same as before)
+            logger.error(f"A2A Task Mgr (ADK Mode): No symbol found for task '{task_id}'.")
+            task.status.state = TaskState.FAILED
+            error_artifact = Artifact(name="error_details", parts=[DataPart(data={"error": "Ticker symbol not provided."})])
+            task.artifacts = [error_artifact]
+            await self.update_store(task_id, task.status, task.artifacts)
+            return SendTaskResponse(id=request.id, result=task)
+
+        # --- 3. Run the ADK Agent ---
+        adk_agent: Agent | None = None
+        exit_stack = None
+        adk_result_dict: Dict[str, Any] = {"error": "ADK agent execution failed to produce a result."} # Default error
+
+        try:
+            # --- Instantiate ADK components for this request ---
+            logger.info(f"A2A Task Mgr (ADK Mode): Creating ADK Agent for task '{task_id}'...")
+            # This starts the MCP server subprocess and gets the exit_stack
+            adk_agent, exit_stack = await create_agent_with_mcp_tools(self.mcp_server_script_path)
+
+            # Use a transient session service and session for this specific ADK run
+            temp_session_service = InMemorySessionService()
+            temp_adk_session = temp_session_service.create_session(
+                app_name=f"adk_task_{task_id}", # Unique app name per task
+                user_id=f"a2a_user_{session_id}",
+                session_id=f"adk_run_{task_id}",
+                state={} # Start with empty state for the ADK agent run
+            )
+
+            adk_runner = Runner(
+                agent=adk_agent,
+                app_name=temp_adk_session.app_name,
+                session_service=temp_session_service,
+            )
+            logger.info(f"A2A Task Mgr (ADK Mode): ADK Runner initialized for agent '{adk_agent.name}'.")
+
+            # Prepare the input specifically for the ADK agent's tool
+            # The ADK agent expects a natural language query that will trigger the tool
+            adk_query = f"What is the stock price for {symbol}?"
+            adk_content = genai_types.Content(role='user', parts=[genai_types.Part(text=adk_query)])
+
+            logger.info(f"A2A Task Mgr (ADK Mode): Running ADK agent for task '{task_id}'...")
+            async for event in adk_runner.run_async(
+                session_id=temp_adk_session.id,
+                user_id=temp_adk_session.user_id,
+                new_message=adk_content
+            ):
+                 logger.debug(f"A2A Task Mgr (ADK Mode): ADK Event: {event.author} - Final: {event.is_final_response()}")
+                 # We need to extract the *tool result* that the agent processed
+                 # The final response *from the ADK agent* isn't directly what the A2A server should return.
+                 if event.get_function_responses():
+                     for func_resp in event.get_function_responses():
+                          # Check the tool name is the one we expect
+                          if func_resp.name == "get_current_stock_price":
+                               try:
+                                   # 1. Get the outer dictionary
+                                   response_wrapper = func_resp.response
+                                   if not isinstance(response_wrapper, dict):
+                                        logger.error(f"A2A Task Mgr (ADK Mode): Outer response wasn't a dict: {type(response_wrapper)}")
+                                        adk_result_dict = {"error": "Invalid outer tool response format from ADK Agent run."}
+                                        break
+
+                                   # 2. Access the nested 'result' which should hold the CallToolResult object (or similar structure)
+                                   # Use .get() for safety
+                                   mcp_tool_result_obj = response_wrapper.get("result")
+                                   if mcp_tool_result_obj is None:
+                                        logger.error(f"A2A Task Mgr (ADK Mode): Outer response dict missing 'result' key: {response_wrapper}")
+                                        adk_result_dict = {"error": "Missing 'result' in outer tool response format."}
+                                        break
+
+                                   # 3. Check if the MCP tool reported an error via the isError attribute
+                                   # Access attributes safely using getattr
+                                   is_error = getattr(mcp_tool_result_obj, 'isError', False)
+                                   mcp_content = getattr(mcp_tool_result_obj, 'content', None)
+
+                                   if is_error:
+                                       error_text = "Unknown MCP tool error"
+                                       if mcp_content and len(mcp_content) > 0:
+                                           # Check if the first content part has 'text'
+                                           first_content_part = mcp_content[0]
+                                           error_text = getattr(first_content_part, 'text', str(first_content_part))
+                                           try: # Try parsing JSON error from tool's text content
+                                               parsed_error = json.loads(error_text)
+                                               if isinstance(parsed_error, dict) and "error" in parsed_error:
+                                                   adk_result_dict = parsed_error # Use the tool's error dict
+                                               else:
+                                                   adk_result_dict = {"error": f"MCP Tool Error: {error_text}"}
+                                           except (json.JSONDecodeError, TypeError): # Catch TypeError just in case
+                                                adk_result_dict = {"error": f"MCP Tool Error: {error_text}"} # Treat raw text as error
+                                       else:
+                                            adk_result_dict = {"error": "MCP Tool reported error with no content."}
+                                       logger.error(f"A2A Task Mgr (ADK Mode): MCP Tool reported error: {adk_result_dict}")
+
+                                   # 4. If not an error, extract and parse the JSON from TextContent
+                                   elif mcp_content and len(mcp_content) > 0:
+                                       first_content_part = mcp_content[0]
+                                       # Check it has 'type' == 'text' and a 'text' attribute
+                                       if getattr(first_content_part, 'type', None) == 'text' and hasattr(first_content_part, 'text'):
+                                            result_text = getattr(first_content_part, 'text', '')
+                                            try:
+                                                parsed_dict = json.loads(result_text)
+                                                if isinstance(parsed_dict, dict) and "price" in parsed_dict and "symbol" in parsed_dict:
+                                                    adk_result_dict = parsed_dict # Successfully parsed the result
+                                                    logger.info(f"A2A Task Mgr (ADK Mode): Successfully captured tool result: {adk_result_dict}")
+                                                else:
+                                                    logger.error(f"A2A Task Mgr (ADK Mode): Parsed JSON from tool lacks expected keys: {parsed_dict}")
+                                                    adk_result_dict = {"error": "Invalid data structure from MCP tool (missing keys)."}
+                                            except json.JSONDecodeError:
+                                                logger.error(f"A2A Task Mgr (ADK Mode): Failed to parse JSON from successful MCP tool response text: {result_text}")
+                                                adk_result_dict = {"error": "Non-JSON success response text from MCP tool."}
+                                       else:
+                                           logger.error(f"A2A Task Mgr (ADK Mode): Expected TextContent, but got different type or missing text: {first_content_part}")
+                                           adk_result_dict = {"error": "Unexpected content type in successful MCP response."}
+                                   else:
+                                       # Handle unexpected successful response format (no content)
+                                       logger.error(f"A2A Task Mgr (ADK Mode): Successful MCP response has no content: {mcp_tool_result_obj}")
+                                       adk_result_dict = {"error": "Missing content in successful MCP response."}
+
+                                   break # Processed the relevant function response
+
+                               except Exception as process_err:
+                                    logger.error(f"A2A Task Mgr (ADK Mode): Error processing ADK tool response event: {process_err}", exc_info=True)
+                                    adk_result_dict = {"error": "Internal error processing specialist response."}
+                                    break # Stop processing on error
+                               
+            logger.info(f"A2A Task Mgr (ADK Mode): ADK agent run finished for task '{task_id}'.")
+
+        except Exception as adk_err:
+            logger.exception(f"A2A Task Mgr (ADK Mode): Error during ADK agent execution for task '{task_id}': {adk_err}")
+            adk_result_dict = {"error": f"Failed to execute ADK agent: {str(adk_err)}"}
+        finally:
+            # --- CRUCIAL: Cleanup the exit_stack for the MCP process ---
+            if exit_stack:
+                logger.info(f"A2A Task Mgr (ADK Mode): Cleaning up MCP connection for task '{task_id}'...")
+                await exit_stack.aclose()
+                logger.info(f"A2A Task Mgr (ADK Mode): MCP connection closed for task '{task_id}'.")
+
+        # --- 4. Process Result and Finalize A2A Task ---
+        if "error" not in adk_result_dict and "price" in adk_result_dict:
+            task.status.state = TaskState.COMPLETED
+            task.status.message = None
+            result_artifact = Artifact(name="stock_price_data", parts=[DataPart(data=adk_result_dict)])
+            task.artifacts = [result_artifact]
+            logger.info(f"A2A Task Mgr (ADK Mode): Task '{task_id}' COMPLETED successfully.")
+        else:
+            task.status.state = TaskState.FAILED
+            task.status.message = None
+            error_msg = adk_result_dict.get("error", "Unknown error from ADK Agent execution.")
+            logger.error(f"A2A Task Mgr (ADK Mode): Task '{task_id}' FAILED. Reason: {error_msg}")
+            error_artifact = Artifact(name="error_details", parts=[DataPart(data={"error": error_msg})])
+            task.artifacts = [error_artifact]
+
+        await self.update_store(task_id, task.status, task.artifacts)
+
+        # 5. Return A2A Response
+        return SendTaskResponse(id=request.id, result=task)
+    
+    async def on_send_task_subscribe(
+        self, request: SendTaskStreamingRequest
+    ) -> Union[AsyncIterable[SendTaskStreamingResponse], JSONRPCResponse]:
+        """Handles streaming requests - intentionally not supported by this agent."""
+        task_id = request.params.id if request.params else "unknown"
+        logger.warning(f"A2A Task Manager: Received 'tasks/sendSubscribe' request for task {task_id}, but streaming is not supported by this agent.")
+        # Log the incoming request
+        logger.info(f"A2A Task Manager: Received Full Request (sendSubscribe): {request.model_dump_json(indent=2)}")
+        # Return an error compliant with JSON-RPC and A2A types
+        response = JSONRPCResponse(
+            id=request.id,
+            error=UnsupportedOperationError(
+                message="Streaming (tasks/sendSubscribe) is not supported by this agent."
+            )
+        )
+        logger.warning(f"A2A Task Manager: Sending UnsupportedOperation Error Response: {response.model_dump_json(indent=2)}")
+        return response
+
+    # Override other handlers as needed, e.g., return "Unsupported" error
+    # async def on_get_task(...) -> GetTaskResponse: ...
+    # async def on_cancel_task(...) -> CancelTaskResponse: ...
+    # async def on_send_task_subscribe(...) -> ... : # Return UnsupportedOperationError
\ No newline at end of file
diff --git a/project-horizon-adk/examples/stock_lookup_custom_a2a/tests/test_a2a_stock_client.py b/project-horizon-adk/examples/stock_lookup_custom_a2a/tests/test_a2a_stock_client.py
new file mode 100644
index 000000000..63c5c94f8
--- /dev/null
+++ b/project-horizon-adk/examples/stock_lookup_custom_a2a/tests/test_a2a_stock_client.py
@@ -0,0 +1,126 @@
+# tests/test_a2a_stock_client.py
+import sys
+import os
+
+# Add the project root to the Python path
+project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+import asyncio
+import argparse
+import uuid
+import logging
+import json
+from typing import Optional
+
+from common_impl.client import A2AClient
+from common_impl.types import (
+    TaskSendParams, Message, TextPart, SendTaskResponse, TaskState, Artifact,
+    DataPart, A2AClientHTTPError, A2AClientJSONError
+)
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - A2A_TEST_CLIENT - %(message)s')
+logger = logging.getLogger(__name__)
+
+async def run_a2a_test(symbol: str, server_url: str, session_id: Optional[str] = None):
+    """
+    Sends a task to the StockInfoAgent A2A server and prints the result.
+    """
+    if not session_id:
+        session_id = f"test_session_{uuid.uuid4().hex[:8]}"
+        logger.info(f"No session ID provided, generated one: {session_id}")
+
+    task_id = uuid.uuid4().hex
+    logger.info(f"Creating A2A task {task_id} for session {session_id} with symbol '{symbol}'")
+
+    # Create the A2A client targeting the StockInfoAgent server
+    a2a_client = A2AClient(url=server_url) # Pass the URL directly
+
+    # Construct the A2A TaskSendParams
+    task_params = TaskSendParams(
+        id=task_id,
+        sessionId=session_id,
+        message=Message(
+            role="user",
+            parts=[TextPart(text=symbol)] # Send the symbol as text
+        ),
+        # acceptedOutputModes=["application/json"] # Optional: Specify expected output
+    )
+
+    logger.info(f"Sending task to A2A server at {server_url}...")
+
+    try:
+        # Send the task using the A2A client
+        # Use .model_dump() for Pydantic v2+ or .dict() for v1
+        response: SendTaskResponse = await a2a_client.send_task(task_params.model_dump())
+
+        logger.info("Received response from A2A server.")
+
+        # Process the response
+        if response.error:
+            logger.error(f"A2A Server returned an error: {response.error.model_dump()}")
+        elif response.result:
+            task_result = response.result
+            logger.info(f"Task {task_result.id} final state: {task_result.status.state}")
+
+            if task_result.status.state == TaskState.COMPLETED:
+                logger.info("Task completed successfully.")
+                if task_result.artifacts:
+                    logger.info("Artifacts received:")
+                    for artifact in task_result.artifacts:
+                        logger.info(f"- Artifact Name: {artifact.name or 'N/A'}")
+                        for part in artifact.parts:
+                            if isinstance(part, DataPart):
+                                logger.info(f"  DataPart Content: {json.dumps(part.data, indent=2)}")
+                            elif isinstance(part, TextPart):
+                                 logger.info(f"  TextPart Content: {part.text}")
+                            else:
+                                 logger.info(f"  Other Part Type: {part.type}")
+                else:
+                    logger.warning("Task completed but no artifacts found.")
+
+            elif task_result.status.state == TaskState.FAILED:
+                logger.error("Task failed.")
+                if task_result.artifacts:
+                     logger.error("Error details from artifact:")
+                     for artifact in task_result.artifacts:
+                         if artifact.name == "error_details":
+                             for part in artifact.parts:
+                                 if isinstance(part, DataPart):
+                                     logger.error(f"  {json.dumps(part.data, indent=2)}")
+                                 else:
+                                      logger.error(f"  Unexpected error artifact part: {part}")
+                elif task_result.status.message:
+                     logger.error(f"Error message from status: {task_result.status.message}")
+                else:
+                     logger.error("Task failed with no specific error details provided.")
+            else:
+                logger.warning(f"Task ended in unexpected state: {task_result.status.state}")
+
+        else:
+            logger.error("Received an empty response from the server.")
+
+    except A2AClientHTTPError as http_err:
+        logger.error(f"HTTP Error connecting to A2A server: {http_err.status_code} - {http_err.message}")
+    except A2AClientJSONError as json_err:
+         logger.error(f"JSON Error processing A2A server response: {json_err.message}")
+    except ConnectionRefusedError:
+         logger.error(f"Connection refused. Is the A2A server running at {server_url}?")
+    except Exception as e:
+        logger.error(f"An unexpected error occurred: {e}", exc_info=True)
+
+# --- Script Entry Point ---
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Test client for the StockInfoAgent A2A server.")
+    parser.add_argument("symbol", help="The stock ticker symbol to query (e.g., MSFT).")
+    parser.add_argument("--url", default="http://127.0.0.1:8001", help="The full URL of the A2A server endpoint.")
+    parser.add_argument("--session", default=None, help="Optional session ID to use.")
+
+    args = parser.parse_args()
+
+    try:
+        asyncio.run(run_a2a_test(args.symbol, args.url, args.session))
+    except KeyboardInterrupt:
+        logger.info("Test client stopped by user.")
\ No newline at end of file