chore: import upstream snapshot with attribution

python/samples/04-hosting/durabletask/02_multi_agent/README.md

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+# Multi-Agent
+
+This sample demonstrates how to host multiple AI agents with different tools in a single worker-client setup using the Durable Task Scheduler.
+
+## Key Concepts Demonstrated
+
+- Hosting multiple agents (WeatherAgent and MathAgent) in a single worker process.
+- Each agent with its own specialized tools and instructions.
+- Interacting with different agents using separate conversation sessions.
+- Worker-client architecture for multi-agent systems.
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup, you can run the sample using the combined approach or separate worker and client processes:
+
+**Option 1: Combined (Recommended for Testing)**
+
+```bash
+cd samples/04-hosting/durabletask/02_multi_agent
+python sample.py
+```
+
+**Option 2: Separate Processes**
+
+Start the worker in one terminal:
+
+```bash
+python worker.py
+```
+
+In a new terminal, run the client:
+
+```bash
+python client.py
+```
+
+The client will interact with both agents:
+
+```
+Starting Durable Task Multi-Agent Client...
+Using taskhub: default
+Using endpoint: http://localhost:8080
+
+================================================================================
+Testing WeatherAgent
+================================================================================
+
+Created weather conversation session: <guid>
+User: What is the weather in Seattle?
+
+🔧 [TOOL CALLED] get_weather(location=Seattle)
+✓ [TOOL RESULT] {'location': 'Seattle', 'temperature': 72, 'conditions': 'Sunny', 'humidity': 45}
+
+WeatherAgent: The current weather in Seattle is sunny with a temperature of 72°F and 45% humidity.
+
+================================================================================
+Testing MathAgent
+================================================================================
+
+Created math conversation session: <guid>
+User: Calculate a 20% tip on a $50 bill
+
+🔧 [TOOL CALLED] calculate_tip(bill_amount=50.0, tip_percentage=20.0)
+✓ [TOOL RESULT] {'bill_amount': 50.0, 'tip_percentage': 20.0, 'tip_amount': 10.0, 'total': 60.0}
+
+MathAgent: For a $50 bill with a 20% tip, the tip amount is $10.00 and the total is $60.00.
+```
+
+## Viewing Agent State
+
+You can view the state of both agents in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can view:
+   - The state of both WeatherAgent and MathAgent entities (dafx-WeatherAgent, dafx-MathAgent)
+   - Each agent's conversation state across multiple interactions

python/samples/04-hosting/durabletask/02_multi_agent/client.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Client application for interacting with multiple hosted agents.
+
+This client connects to the Durable Task Scheduler and interacts with two different
+agents (WeatherAgent and MathAgent), demonstrating how to work with multiple agents
+each with their own specialized capabilities and tools.
+
+Prerequisites:
+- The worker must be running with both agents registered
+- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL when running the worker
+- Sign in with Azure CLI for AzureCliCredential authentication
+- Durable Task Scheduler must be running
+"""
+
+import asyncio
+import logging
+import os
+
+from agent_framework.azure import DurableAIAgentClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from durabletask.azuremanaged.client import DurableTaskSchedulerClient
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def get_client(
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
+) -> DurableAIAgentClient:
+    """Create a configured DurableAIAgentClient.
+
+    Args:
+        taskhub: Task hub name (defaults to TASKHUB env var or "default")
+        endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
+        log_handler: Optional logging handler for client logging
+
+    Returns:
+        Configured DurableAIAgentClient instance
+    """
+    taskhub_name = taskhub or os.getenv("TASKHUB", "default")
+    endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
+
+    logger.debug(f"Using taskhub: {taskhub_name}")
+    logger.debug(f"Using endpoint: {endpoint_url}")
+
+    credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
+
+    dts_client = DurableTaskSchedulerClient(
+        host_address=endpoint_url,
+        secure_channel=endpoint_url != "http://localhost:8080",
+        taskhub=taskhub_name,
+        token_credential=credential,
+        log_handler=log_handler,
+    )
+
+    return DurableAIAgentClient(dts_client)
+
+
+def run_client(agent_client: DurableAIAgentClient) -> None:
+    """Run client interactions with both WeatherAgent and MathAgent.
+
+    Args:
+        agent_client: The DurableAIAgentClient instance
+    """
+    logger.debug("Testing WeatherAgent")
+
+    # Get reference to WeatherAgent
+    weather_agent = agent_client.get_agent("WeatherAgent")
+    weather_session = weather_agent.create_session()
+
+    logger.debug(f"Created weather conversation session: {weather_session.session_id}")
+
+    # Test WeatherAgent
+    weather_message = "What is the weather in Seattle?"
+    logger.info(f"User: {weather_message}")
+
+    weather_response = weather_agent.run(weather_message, session=weather_session)
+    logger.info(f"WeatherAgent: {weather_response.text} \n")
+
+    logger.debug("Testing MathAgent")
+
+    # Get reference to MathAgent
+    math_agent = agent_client.get_agent("MathAgent")
+    math_session = math_agent.create_session()
+
+    logger.debug(f"Created math conversation session: {math_session.session_id}")
+
+    # Test MathAgent
+    math_message = "Calculate a 20% tip on a $50 bill"
+    logger.info(f"User: {math_message}")
+
+    math_response = math_agent.run(math_message, session=math_session)
+    logger.info(f"MathAgent: {math_response.text} \n")
+
+    logger.debug("Both agents completed successfully!")
+
+
+async def main() -> None:
+    """Main entry point for the client application."""
+    logger.debug("Starting Durable Task Multi-Agent Client...")
+
+    # Create client using helper function
+    agent_client = get_client()
+
+    try:
+        run_client(agent_client)
+    except Exception as e:
+        logger.exception(f"Error during agent interaction: {e}")
+    finally:
+        logger.debug("Client shutting down")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/04-hosting/durabletask/02_multi_agent/requirements.txt

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+# Agent Framework packages
+# To use the deployed version, uncomment the lines below and comment out the local installation lines
+# agent-framework-foundry
+# agent-framework-durabletask
+
+# Local installation (for development and testing)
+# Each package must be listed explicitly because pip doesn't resolve uv workspace sources.
+# Without explicit entries, pip would fetch transitive dependencies from PyPI instead of local source.
+-e ../../../../packages/core  # Core framework - base dependency for all packages
+-e ../../../../packages/foundry  # Foundry support - dependency for hosted chat/agent samples
+-e ../../../../packages/durabletask  # Durable Task support - the main package for this sample
+
+# Azure authentication
+azure-identity

python/samples/04-hosting/durabletask/02_multi_agent/sample.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Multi-Agent Sample - Durable Task Integration (Combined Worker + Client)
+This sample demonstrates running both the worker and client in a single process
+for multiple agents with different tools. The worker registers two agents
+(WeatherAgent and MathAgent), each with their own specialized capabilities.
+Prerequisites:
+- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
+- Sign in with Azure CLI for AzureCliCredential authentication
+- Durable Task Scheduler must be running (e.g., using Docker)
+To run this sample:
+    python sample.py
+"""
+
+import logging
+
+# Import helper functions from worker and client modules
+from client import get_client, run_client  # pyrefly: ignore[missing-import]
+from dotenv import load_dotenv
+from worker import get_worker, setup_worker  # pyrefly: ignore[missing-import]
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, force=True)
+logger = logging.getLogger(__name__)
+
+
+def main():
+    """Main entry point - runs both worker and client in single process."""
+    logger.debug("Starting Durable Task Multi-Agent Sample (Combined Worker + Client)...")
+    silent_handler = logging.NullHandler()
+    # Create and start the worker using helper function and context manager
+    dts_worker = get_worker(log_handler=silent_handler)
+    with dts_worker:
+        # Register agents using helper function
+        setup_worker(dts_worker)
+        # Start the worker
+        dts_worker.start()
+        logger.debug("Worker started and listening for requests...")
+        # Create the client using helper function
+        agent_client = get_client(log_handler=silent_handler)
+        try:
+            # Run client interactions using helper function
+            run_client(agent_client)
+        except Exception as e:
+            logger.exception(f"Error during agent interaction: {e}")
+        logger.debug("Sample completed. Worker shutting down...")
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    main()

python/samples/04-hosting/durabletask/02_multi_agent/worker.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Worker process for hosting multiple Azure OpenAI agents with different tools using Durable Task.
+
+This worker registers two agents - a weather assistant and a math assistant - each
+with their own specialized tools. This demonstrates how to host multiple agents
+with different capabilities in a single worker process.
+
+Prerequisites:
+- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_MODEL
+- Sign in with Azure CLI for AzureCliCredential authentication
+- Start a Durable Task Scheduler (e.g., using Docker)
+"""
+
+import asyncio
+import logging
+import os
+from typing import Any
+
+from agent_framework import Agent, tool
+from agent_framework.azure import DurableAIAgentWorker
+from agent_framework.openai import OpenAIChatCompletionClient
+from azure.identity import AzureCliCredential
+from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential
+from dotenv import load_dotenv
+from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Agent names
+WEATHER_AGENT_NAME = "WeatherAgent"
+MATH_AGENT_NAME = "MathAgent"
+
+
+@tool
+def get_weather(location: str) -> dict[str, Any]:
+    """Get current weather for a location."""
+    logger.info(f"🔧 [TOOL CALLED] get_weather(location={location})")
+    result = {
+        "location": location,
+        "temperature": 72,
+        "conditions": "Sunny",
+        "humidity": 45,
+    }
+    logger.info(f"✓ [TOOL RESULT] {result}")
+    return result
+
+
+@tool
+def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> dict[str, Any]:
+    """Calculate tip amount and total bill."""
+    logger.info(f"🔧 [TOOL CALLED] calculate_tip(bill_amount={bill_amount}, tip_percentage={tip_percentage})")
+    tip = bill_amount * (tip_percentage / 100)
+    total = bill_amount + tip
+    result = {
+        "bill_amount": bill_amount,
+        "tip_percentage": tip_percentage,
+        "tip_amount": round(tip, 2),
+        "total": round(total, 2),
+    }
+    logger.info(f"✓ [TOOL RESULT] {result}")
+    return result
+
+
+def create_weather_agent():
+    """Create the Weather agent using Azure OpenAI.
+
+    Returns:
+        Agent: The configured Weather agent with weather tool
+    """
+    return Agent(
+        client=OpenAIChatCompletionClient(
+            credential=AsyncAzureCliCredential(),
+        ),
+        name=WEATHER_AGENT_NAME,
+        instructions="You are a helpful weather assistant. Provide current weather information.",
+        tools=[get_weather],
+    )
+
+
+def create_math_agent():
+    """Create the Math agent using Azure OpenAI.
+
+    Returns:
+        Agent: The configured Math agent with calculation tools
+    """
+    return Agent(
+        client=OpenAIChatCompletionClient(
+            credential=AsyncAzureCliCredential(),
+        ),
+        name=MATH_AGENT_NAME,
+        instructions="You are a helpful math assistant. Help users with calculations like tip calculations.",
+        tools=[calculate_tip],
+    )
+
+
+def get_worker(
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
+) -> DurableTaskSchedulerWorker:
+    """Create a configured DurableTaskSchedulerWorker.
+
+    Args:
+        taskhub: Task hub name (defaults to TASKHUB env var or "default")
+        endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
+        log_handler: Optional logging handler for worker logging
+
+    Returns:
+        Configured DurableTaskSchedulerWorker instance
+    """
+    taskhub_name = taskhub or os.getenv("TASKHUB", "default")
+    endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
+
+    logger.debug(f"Using taskhub: {taskhub_name}")
+    logger.debug(f"Using endpoint: {endpoint_url}")
+
+    credential = None if endpoint_url == "http://localhost:8080" else AzureCliCredential()
+
+    return DurableTaskSchedulerWorker(
+        host_address=endpoint_url,
+        secure_channel=endpoint_url != "http://localhost:8080",
+        taskhub=taskhub_name,
+        token_credential=credential,
+        log_handler=log_handler,
+    )
+
+
+def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
+    """Set up the worker with multiple agents registered.
+
+    Args:
+        worker: The DurableTaskSchedulerWorker instance
+
+    Returns:
+        DurableAIAgentWorker with agents registered
+    """
+    # Wrap it with the agent worker
+    agent_worker = DurableAIAgentWorker(worker)
+
+    # Create and register both agents
+    logger.debug("Creating and registering agents...")
+    weather_agent = create_weather_agent()
+    math_agent = create_math_agent()
+
+    agent_worker.add_agent(weather_agent)
+    agent_worker.add_agent(math_agent)
+
+    logger.debug(f"✓ Registered agents: {weather_agent.name}, {math_agent.name}")
+
+    return agent_worker
+
+
+async def main():
+    """Main entry point for the worker process."""
+    logger.debug("Starting Durable Task Multi-Agent Worker...")
+
+    # Create a worker using the helper function
+    worker = get_worker()
+
+    # Setup worker with agents
+    setup_worker(worker)
+
+    logger.info("Worker is ready and listening for requests...")
+    logger.info("Press Ctrl+C to stop. \n")
+
+    try:
+        # Start the worker (this blocks until stopped)
+        worker.start()
+
+        # Keep the worker running
+        while True:
+            await asyncio.sleep(1)
+    except KeyboardInterrupt:
+        logger.debug("Worker shutdown initiated")
+
+    logger.info("Worker stopped")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())