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Eduard van Valkenburg b1b528e4a8 Python: [BREAKING] Remove deprecated kwargs compatibility paths (#4858)
* [BREAKING] Remove deprecated kwargs compatibility paths

Remove the deprecated kwargs compatibility shims across core agents, clients, tools, middleware, and telemetry.

Keep workflow kwargs behavior intact in this branch and follow up separately in #4850.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix PR CI fallout for kwargs removal

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address PR review feedback

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updates

* Fix Azure AI CI fallout

Remove the stale _get_current_conversation_id override from the Azure AI client after the OpenAI base helper was deleted.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fixed new classes

* Fix Assistants deprecated import gating

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix integration replay regressions

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Switch multi-agent hosting samples to Azure chat completions

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Simplify Azure multi-agent sample config

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
b1b528e4a8 · 2026-03-27 21:00:12 +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