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Update ADK doc according to issue #796 - 6 #805

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168 changes: 44 additions & 124 deletions docs/tools/built-in-tools.md
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,31 @@ The `GkeCodeExecutor` can be configured with the following parameters:
| `kubeconfig_path` | `str` | Path to a kubeconfig file to use for authentication. Falls back to in-cluster config or the default local kubeconfig. |
| `kubeconfig_context` | `str` | The `kubeconfig` context to use. |

### Agent Engine Sandbox Code Executor

The `AgentEngineSandboxCodeExecutor` provides a secure way to execute LLM-generated code within the Agent Engine Code Execution Sandbox. This is particularly useful for data analysis tasks.

#### How to use

1. Follow the instructions at https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/code-execution/overview to create a code execution sandbox environment.
2. Replace `SANDBOX_RESOURCE_NAME` with the one you just created.

**Example:**
```python
from google.adk.agents.llm_agent import Agent
from google.adk.code_executors.agent_engine_sandbox_code_executor import AgentEngineSandboxCodeExecutor

root_agent = Agent(
model="gemini-2.0-flash-001",
name="agent_engine_code_execution_agent",
instruction="<your instruction here>",
code_executor=AgentEngineSandboxCodeExecutor(
sandbox_resource_name="SANDBOX_RESOURCE_NAME",
agent_engine_resource_name="AGENT_ENGINE_RESOURCE_NAME",
),
)
```
For a complete example, see the [agent_engine_code_execution sample](https://github.com/google/adk-python/tree/main/contributing/samples/agent_engine_code_execution).
### Vertex AI RAG Engine

The `vertex_ai_rag_retrieval` tool allows the agent to perform private data retrieval using Vertex
Expand Down Expand Up @@ -230,134 +255,29 @@ They are packaged in the toolset `BigtableToolset`.

## Use Built-in tools with other tools

The following code sample demonstrates how to use multiple built-in tools or how
to use built-in tools with other tools by using multiple agents:

=== "Python"

```py
from google.adk.tools.agent_tool import AgentTool
from google.adk.agents import Agent
from google.adk.tools import google_search
from google.adk.code_executors import BuiltInCodeExecutor


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
""",
code_executor=BuiltInCodeExecutor(),
)
root_agent = Agent(
name="RootAgent",
model="gemini-2.0-flash",
description="Root Agent",
tools=[AgentTool(agent=search_agent), AgentTool(agent=coding_agent)],
)
```

=== "Java"

```java
import com.google.adk.agents.BaseAgent;
import com.google.adk.agents.LlmAgent;
import com.google.adk.tools.AgentTool;
import com.google.adk.tools.BuiltInCodeExecutionTool;
import com.google.adk.tools.GoogleSearchTool;
import com.google.common.collect.ImmutableList;

public class NestedAgentApp {

private static final String MODEL_ID = "gemini-2.0-flash";

public static void main(String[] args) {

// Define the SearchAgent
LlmAgent searchAgent =
LlmAgent.builder()
.model(MODEL_ID)
.name("SearchAgent")
.instruction("You're a specialist in Google Search")
.tools(new GoogleSearchTool()) // Instantiate GoogleSearchTool
.build();


// Define the CodingAgent
LlmAgent codingAgent =
LlmAgent.builder()
.model(MODEL_ID)
.name("CodeAgent")
.instruction("You're a specialist in Code Execution")
.tools(new BuiltInCodeExecutionTool()) // Instantiate BuiltInCodeExecutionTool
.build();

// Define the RootAgent, which uses AgentTool.create() to wrap SearchAgent and CodingAgent
BaseAgent rootAgent =
LlmAgent.builder()
.name("RootAgent")
.model(MODEL_ID)
.description("Root Agent")
.tools(
AgentTool.create(searchAgent), // Use create method
AgentTool.create(codingAgent) // Use create method
)
.build();

// Note: This sample only demonstrates the agent definitions.
// To run these agents, you'd need to integrate them with a Runner and SessionService,
// similar to the previous examples.
System.out.println("Agents defined successfully:");
System.out.println(" Root Agent: " + rootAgent.name());
System.out.println(" Search Agent (nested): " + searchAgent.name());
System.out.println(" Code Agent (nested): " + codingAgent.name());
}
}
```

You can now use `GoogleSearchTool` and `VertexAiSearchTool` with other tools by setting `bypass_multi_tools_limit=True`.

**Example:**
```python
from google.adk.tools.google_search_tool import GoogleSearchTool
from google.adk.tools.vertex_ai_search_tool import VertexAiSearchTool

root_agent = Agent(
name="MyAgent",
model="gemini-2.5-flash",
tools=[
my_custom_tool,
GoogleSearchTool(bypass_multi_tools_limit=True),
VertexAiSearchTool(data_store_id="my-data-store", bypass_multi_tools_limit=True),
],
)
```

### Limitations

!!! warning

Currently, for each root agent or single agent, only one built-in tool is
supported. No other tools of any type can be used in the same agent.

For example, the following approach that uses ***a built-in tool along with
other tools*** within a single agent is **not** currently supported:

=== "Python"

```py
root_agent = Agent(
name="RootAgent",
model="gemini-2.0-flash",
description="Root Agent",
tools=[custom_function],
code_executor=BuiltInCodeExecutor() # <-- not supported when used with tools
)
```

=== "Java"

```java
LlmAgent searchAgent =
LlmAgent.builder()
.model(MODEL_ID)
.name("SearchAgent")
.instruction("You're a specialist in Google Search")
.tools(new GoogleSearchTool(), new YourCustomTool()) // <-- not supported
.build();
```
When using `GoogleSearchTool` or `VertexAiSearchTool` with other tools, you must set `bypass_multi_tools_limit=True`.

!!! warning

Expand Down Expand Up @@ -423,4 +343,4 @@ is **not** currently supported:
.description("Root Agent")
.subAgents(searchAgent, codingAgent) // Not supported, as the sub agents use built in tools.
.build();
```
```