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Eduard van Valkenburg d08200d00e Python: Bump package versions for 1.12.0 release (#7238)
* Bump Python package versions for 1.12.0 release

Bump packages represented in the 1.12.0 changelog, promote Foundry Hosting, Azure Content Understanding, Gemini, Mistral, Monty, and Tools to beta, and apply the requested beta cohort date stamp. Root and core move to 1.12.0, released and RC packages use their selected increments, alpha packages including Hosting MCP use the 260721 stamp, and core floors are raised only for proven consumers.

Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf

* fix version in readme

* Add Responses conversation ID changes to release notes

Include the breaking Hosting Responses conversation ID helper changes from #7234 in the Python 1.12.0 changelog.

Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf
2026-07-21 15:45:00 +00:00
..

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