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agent-framework/python/samples/04-hosting/azure_functions/02_multi_agent
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Copilot b05fc9e849 Python: Add load_dotenv() to samples for .env file support (#4043)
* Initial plan

* Add load_dotenv() to 303 Python samples for environment variable loading

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* Update SAMPLE_GUIDELINES.md to document load_dotenv() requirement

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* Update samples README.md to document .env file usage

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* Run ruff format on all changed sample files

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* Clarify load_dotenv() usage in README - local dev vs production

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* Remove deprecated getting_started folder as requested

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* Document env_file_path parameter for per-client configuration

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* Merge main branch to resolve conflicts

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* Fix run_evaluation.py file that was empty in merge commit

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* Remove dotnet changes from merge - out of scope for this PR

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* Remove package and test changes from merge - only sample changes needed

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* Remove test_func_utils.py - only sample changes needed

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* Revert sample files not in original changeset - keep only load_dotenv additions

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* Move load_dotenv() outside snippet tag in 06_host_your_agent.py

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* Fix comment placement - move load_dotenv before code comments

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* Fix load_dotenv() placement across all samples - after docstring, before code comments

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* Merge latest main branch with load_dotenv changes

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* Remove non-sample changes from merge - keep only load_dotenv additions

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* Revert non-load_dotenv sample changes from merge

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* Fix run_evaluation.py - use main's improved version (file already had load_dotenv)

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* Manual update

* Manual update 2

* Fix Role usage and load_dotenv placement per PR review feedback

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* Fix Role usage - use string literals not enum attributes

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* Fix SAMPLE_GUIDELINES.md example - load_dotenv before docstring per guidance

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* Move load_dotenv() before docstrings in all samples per SAMPLE_GUIDELINES ordering

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* Address PR review: rename files, fix placement, add session usage, remove note

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* Update Redis README to reference renamed file redis_history_provider.py

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---------

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b05fc9e849 · 2026-02-19 10:55:13 +00:00
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Multi-Agent Sample

This sample demonstrates how to use the Durable Extension for Agent Framework to create an Azure Functions app that hosts multiple AI agents and provides direct HTTP API access for interactive conversations with each agent.

Key Concepts Demonstrated

  • Using the Microsoft Agent Framework to define multiple AI agents with unique names and instructions.
  • Registering multiple agents with the Function app and running them using HTTP.
  • Conversation management (via session IDs) for isolated interactions per agent.
  • Two different methods for registering agents: list-based initialization and incremental addition.

Prerequisites

Complete the common environment preparation steps described in ../README.md, including installing Azure Functions Core Tools, starting Azurite, configuring Azure OpenAI settings, and installing this sample's requirements.

Running the Sample

With the environment setup and function app running, you can test the sample by sending HTTP requests to the different agent endpoints.

You can use the demo.http file to send messages to the agents, or a command line tool like curl as shown below:

Note: Each endpoint waits for the agent response by default. To receive an immediate HTTP 202 instead, set the x-ms-wait-for-response header or include "wait_for_response": false in the request body.

Test the Weather Agent

Bash (Linux/macOS/WSL): Weather agent request:

curl -X POST http://localhost:7071/api/agents/WeatherAgent/run \
    -H "Content-Type: application/json" \
    -d '{"message": "What is the weather in Seattle?"}'

Expected HTTP 202 payload:

{
  "status": "accepted",
  "response": "Agent request accepted",
  "message": "What is the weather in Seattle?",
  "thread_id": "<guid>",
  "correlation_id": "<guid>"
}

Math agent request:

curl -X POST http://localhost:7071/api/agents/MathAgent/run \
    -H "Content-Type: application/json" \
    -d '{"message": "Calculate a 20% tip on a $50 bill"}'

Expected HTTP 202 payload:

{
  "status": "accepted",
  "response": "Agent request accepted",
  "message": "Calculate a 20% tip on a $50 bill",
  "thread_id": "<guid>",
  "correlation_id": "<guid>"
}

Health check (optional):

curl http://localhost:7071/api/health

Expected response:

{
  "status": "healthy",
  "agents": [
    {"name": "WeatherAgent", "type": "Agent"},
    {"name": "MathAgent", "type": "Agent"}
  ],
  "agent_count": 2
}

Code Structure

The sample demonstrates two ways to register multiple agents:

Option 1: Pass list of agents during initialization

app = AgentFunctionApp(agents=[weather_agent, math_agent])

Option 2: Add agents incrementally (commented in sample)

app = AgentFunctionApp()
app.add_agent(weather_agent)
app.add_agent(math_agent)

Each agent automatically gets:

  • POST /api/agents/{agent_name}/run - Send messages to the agent