* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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-responseheader or include"wait_for_response": falsein 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