Files
Eduard van Valkenburg a2856d3b92 Python: restructure: Python samples into progressive 01-05 layout (#3862)
* restructure: Python samples into progressive 01-05 layout

- 01-get-started/: 6 numbered steps (hello agent → hosting)
- 02-agents/: all agent concept samples (tools, middleware, providers, etc.)
- 03-workflows/: ALL existing workflow samples preserved as-is
- 04-hosting/: azure-functions, durabletask, a2a
- 05-end-to-end/: demos, evaluation, hosted agents
- Old files moved to _to_delete/ for review
- Added AGENTS.md with structure documentation
- autogen-migration/ and semantic-kernel-migration/ preserved at root

* fix: switch to AzureOpenAI Foundry, fix CI failures

- Switch all 01-get-started samples to AzureOpenAIResponsesClient with
  Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT +
  AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential)
- Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes
- Fix test paths in packages/ that referenced old getting_started/ dirs:
  durabletask conftest + streaming test, azurefunctions conftest,
  devui conftest + capture_messages + openai_sdk_integration
- Fix workflow_as_agent_human_in_the_loop.py import (sibling import)
- Update hosting READMEs and tool comment paths
- Replace root README.md with new structure overview
- Update AGENTS.md to document Azure OpenAI Foundry as default provider

* cleanup: remove _to_delete folder, copy resource files to active dirs

All files in _to_delete/ were either:
- Exact duplicates of files in the new structure (240 files)
- Same file with only comment path updates (100 files)
- One import-fix diff (workflow_as_agent_human_in_the_loop.py)
- One superseded minimal_sample.py

Resource files (sample.pdf, countries.json, employees.pdf, weather.json)
copied to 02-agents/sample_assets/ and 02-agents/resources/ since active
samples reference them.

* fix: address PR review comments, centralize resources, remove root duplicates

- Fix type annotation in 04_memory.py (string union -> proper types)
- Fix old sample paths in observability files
- Fix grammar/spelling in observability samples
- Move sample_assets/ and resources/ to shared/ folder
- Remove 8 duplicate observability files from 02-agents root
- Update resource path references in multimodal_input and provider samples

* fix: update broken links from old getting_started paths to new structure

- Update relative paths in READMEs: getting_started/ → 01-get-started/,
  02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/
- Fix absolute GitHub URLs in package READMEs
- Fix broken link in ollama package README

* fix: convert absolute GitHub URLs to relative paths for link checker

Absolute URLs to python/samples/ on main branch 404 until PR merges.
Converted to relative paths that linkspector can verify locally.

* fix: update link for handoff sample moved to orchestrations/

* fix: update chatkit-integration README path from demos/ to 05-end-to-end/

* fix: update broken links in orchestrations README to match flat directory structure
a2856d3b92 · 2026-02-12 17:36:36 +00:00
History
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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 thread 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