Add Strands sample

samples/agentic-strands/.devcontainer/Dockerfile

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+
+FROM mcr.microsoft.com/devcontainers/python:3.11-bookworm

samples/agentic-strands/.devcontainer/devcontainer.json

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+{
+  "build": {
+    "dockerfile": "Dockerfile",
+    "context": ".."
+  },
+  "features": {
+    "ghcr.io/defanglabs/devcontainer-feature/defang-cli:1.0.4": {},
+    "ghcr.io/devcontainers/features/docker-in-docker:2": {},
+    "ghcr.io/devcontainers/features/aws-cli:1": {}
+  }
+}

samples/agentic-strands/.github/workflows/deploy.yaml

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+name: Deploy
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    environment: playground
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      id-token: write
+
+    steps:
+    - name: Checkout Repo
+      uses: actions/checkout@v4
+
+    - name: Deploy
+      uses: DefangLabs/defang-github-action@v1.1.3

samples/agentic-strands/README.md

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+# Agentic Strands
+
+[![1-click-deploy](https://raw.githubusercontent.com/DefangLabs/defang-assets/main/Logos/Buttons/SVG/deploy-with-defang.svg)](https://portal.defang.dev/redirect?url=https%3A%2F%2Fgithub.com%2Fnew%3Ftemplate_name%3Dsample-agentic-strands-template%26template_owner%3DDefangSamples)
+
+This sample demonstrates a Strands Agent application, deployed with Defang. This [Strands](https://strandsagents.com/latest/) Agent can use tools, and is compatible with the [Defang OpenAI Access Gateway](https://github.com/DefangLabs/openai-access-gateway/).
+
+## Prerequisites
+
+1. Download [Defang CLI](https://github.com/DefangLabs/defang)
+2. (Optional) If you are using [Defang BYOC](https://docs.defang.io/docs/concepts/defang-byoc) authenticate with your cloud provider account
+3. (Optional for local development) [Docker CLI](https://docs.docker.com/engine/install/)
+
+## Development
+
+To run the application locally, you can use the following command:
+
+```bash
+docker compose -f compose.dev.yaml up --build
+```
+
+## Configuration
+
+For this sample, you will not need to provide any [configuration](https://docs.defang.io/docs/concepts/configuration). However, if you ever need to, below is an example of how to do so in Defang:
+
+```bash
+defang config set API_KEY
+```
+
+## Deployment
+
+> [!NOTE]
+> Download [Defang CLI](https://github.com/DefangLabs/defang)
+
+### Defang Playground
+
+Deploy your application to the Defang Playground by opening up your terminal and typing:
+```bash
+defang compose up
+```
+
+### BYOC
+
+If you want to deploy to your own cloud account, you can [use Defang BYOC](https://docs.defang.io/docs/tutorials/deploy-to-your-cloud).
+
+---
+
+Title: Agentic Strands
+
+Short Description: A Strands Agent application, deployed with Defang.
+
+Tags: Python, Flask, Strands, AI, Agent
+
+Languages: Python

samples/agentic-strands/app/.dockerignore

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+# Default .dockerignore file for Defang
+**/__pycache__
+**/.direnv
+**/.DS_Store
+**/.envrc
+**/.git
+**/.github
+**/.idea
+**/.next
+**/.vscode
+**/compose.*.yaml
+**/compose.*.yml
+**/compose.yaml
+**/compose.yml
+**/docker-compose.*.yaml
+**/docker-compose.*.yml
+**/docker-compose.yaml
+**/docker-compose.yml
+**/node_modules
+**/Thumbs.db
+Dockerfile
+*.Dockerfile
+# Ignore our own binary, but only in the root to avoid ignoring subfolders
+defang
+defang.exe
+# Ignore our project-level state
+.defang

samples/agentic-strands/app/.gitignore

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+.env
+__pycache__/

samples/agentic-strands/app/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["python", "agent.py"]

samples/agentic-strands/app/__init__.py

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+from . import agent

samples/agentic-strands/app/agent.py

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+from strands import Agent, tool
+from strands.models.openai import OpenAIModel
+from flask import Flask, request, jsonify, send_from_directory
+import requests
+
+import os
+import dotenv
+
+dotenv.load_dotenv()
+
+message = """
+You are a helpful library assistant. 
+Your goal is to help users discover books available through the library's book API, based on the user's preferences. 
+When a user makes a request, you should search the API and suggest books that match their query. 
+
+When interacting, ask the user clear questions to guide the search. 
+Make sure to explicitly state the question you are asking, 
+and provide simple sample answers so the user knows what to type.
+Keep it to a maximum of 3 simple questions.
+"""
+
+app = Flask(__name__)
+latest_response = {"message": "Hello! I'm your library assistant. How can I help you with your reading today?"}
+
+model = OpenAIModel(
+    client_args={
+        "base_url": os.getenv("LLM_URL"),
+        # "api_key": os.getenv("OPENAI_API_KEY")
+    },
+    model_id=os.getenv("LLM_MODEL"),
+    params={
+        "max_tokens": 1000,
+        "temperature": 0.7,
+    }
+)
+
+def parse_assistant_response(**kwargs):
+    # Extract the assistant's text message from JSON
+    assistant_text = kwargs["message"]["content"][0]["text"]
+
+    print("Assistant Text: ", assistant_text)
+    return assistant_text
+
+
+def message_buffer_handler(**kwargs):
+    # When a new message is created from the assistant, print its content
+    global latest_response
+    try:
+        if "message" in kwargs and kwargs["message"].get("role") == "assistant":
+            # Parse the assistant's response from JSON
+            assistant_text = parse_assistant_response(**kwargs)
+
+            # Send the assistant's message content back to the UI
+            latest_response = {"message": assistant_text}
+
+            # Prevent the agent from closing by not calling exit() or any termination logic here.
+            # If you have any cleanup or state reset, do it here, but do not terminate the process.
+            pass
+
+    except Exception as e:
+        print(f"Error in message_buffer_handler: {str(e)}")
+
+@tool
+def search_for_books(query, filters=None) -> str:
+    """
+    Search for detailed information about books using the Open Library API.
+
+    Args:
+        query: The search term to look up books.
+
+    Returns:
+        A string summarizing the list of matching books, or a message if none are found.
+    """
+
+    # Replace spaces in the query with plus signs for URL encoding
+    clean_query = query.replace(' ', '+')
+
+    url = f"https://openlibrary.org/search.json"
+    headers = {}
+    params = {
+        "q": clean_query,
+        "page": 1,
+        "limit": 10
+    }
+
+    try:
+        response = requests.get(url, headers=headers, params=params)
+        if response.ok:
+            book_list = response.json()
+            if book_list.get("num_found", 0) == 0:
+                return "No books found matching your query."
+
+            message = "Here are the books I found:"
+            for book in book_list.get("docs", []):
+                title = book.get("title")
+                author = book.get("author_name", ["Unknown"])[0]
+                year = book.get("first_publish_year")
+                message += f"\n- Title: {title}, Author: {author}, Year: {year}"
+            print(message)
+            return message
+        else:
+            return f"Error: API request failed: {response.status_code}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+TOOL_SPEC = {
+    "name": "search_for_books",
+    "description": "Get detailed information about books from Open Library, based on a search query.",
+    "inputSchema": {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "Search query for books",
+            }
+        },
+        "required": ["query"],
+    },
+}
+
+agent = Agent(
+    tools=[search_for_books], 
+    model=model, 
+    callback_handler=message_buffer_handler, 
+    system_prompt=message
+)
+
+print("Agent model:", agent.model.config)
+
+# Flask routes
+@app.route('/')
+def index():
+    # This assumes index.html is in the same directory as this script
+    return send_from_directory('.', 'index.html')
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    try:
+        global latest_response
+        data = request.json
+        if not data:
+            return jsonify({"error": "No JSON data received"}), 400
+
+        user_message = data.get('message')
+        if not user_message:
+            return jsonify({"error": "No message provided"}), 400
+        print(f"Received message: {user_message}")
+
+        agent(f"Continue the conversation with the user. The user says: {user_message}")
+
+        response_content = latest_response.get("message", "I'm thinking about your question...")
+
+        return jsonify({
+            "response": response_content
+        })
+
+    except Exception as e:
+        print(f"Error in /chat endpoint: {str(e)}")
+        return jsonify({"error": str(e), "response": str(e)}), 500
+
+# Start Flask server when this script is run directly
+if __name__ == '__main__':
+
+    print("Environment variables:")
+    print(f"- LLM_URL: {os.getenv('LLM_URL')}")
+
+    print("Starting Flask server on port 5001")
+    app.run(host='0.0.0.0', port=5001, debug=False)