agent-framework/python/samples/getting_started/azure_functions/03_callbacks

Callback Telemetry Sample

This sample demonstrates how to use the Durable Extension for Agent Framework's response callbacks to observe streaming updates and final agent responses in real time. The ConversationAuditTrail callback records each chunk received from the Azure OpenAI agent and exposes the collected events through an HTTP API that can be polled by a web client or dashboard.

Highlights

• Registers a default AgentResponseCallbackProtocol implementation that logs streaming and final responses.
• Persists callback events in an in-memory store and exposes them via GET /api/agents/{agentName}/callbacks/{conversationId}.
• Shows how to reset stored callback events with DELETE /api/agents/{agentName}/callbacks/{conversationId}.
• Works alongside the standard /api/agents/{agentName}/run endpoint so you can correlate callback telemetry with agent responses.

Prerequisites

• Python 3.11+
• Azure Functions Core Tools v4
• Access to an Azure OpenAI deployment (configure the environment variables listed in local.settings.json or export them in your shell)
• Dependencies from requirements.txt installed in your environment

Note: The sample stores callback events in memory for simplicity. For production scenarios you should persist events to Application Insights, Azure Storage, Cosmos DB, or another durable store.

Running the Sample

1. Create and activate a virtual environment:

   Windows (PowerShell):

   python -m venv .venv
   .venv\Scripts\Activate.ps1
   

   Linux/macOS:

   python -m venv .venv
   source .venv/bin/activate
   

2. Install dependencies (from the repository root or this directory):

   pip install -r requirements.txt
   

3. Copy local.settings.json.template to local.settings.json and update the values (or export them as environment variables) with your Azure resources.

4. Start the Functions host:

   func start
   

5. Use the demo.http file (VS Code REST Client) or any HTTP client to:

   • Send a message to the agent: POST /api/agents/CallbackAgent/run
   • Query callback telemetry: GET /api/agents/CallbackAgent/callbacks/{conversationId}
   • Clear stored events: DELETE /api/agents/CallbackAgent/callbacks/{conversationId}

Example workflow after the host starts:

POST /api/agents/CallbackAgent/run        # send a conversation message
GET  /api/agents/CallbackAgent/callbacks/test-session   # inspect streaming + final events
DELETE /api/agents/CallbackAgent/callbacks/test-session # reset telemetry for the session

The GET endpoint returns an array of events captured by the callback, including timestamps, streaming chunk previews, and the final response metadata. This makes it easy to build real-time UI updates or audit logs on top of Durable Agents.

Expected Output

When you call GET /api/agents/CallbackAgent/callbacks/{conversationId} after sending a request to the agent, the API returns a list of streaming and final callback events similar to the following:

[
  {
    "timestamp": "2024-01-01T00:00:00Z",
    "agent_name": "CallbackAgent",
    "conversation_id": "<conversationId>",
    "correlation_id": "<guid>",
    "request_message": "Tell me a short joke",
    "event_type": "stream",
    "update_kind": "text",
    "text": "Sure, here's a joke..."
  },
  {
    "timestamp": "2024-01-01T00:00:01Z",
    "agent_name": "CallbackAgent",
    "conversation_id": "<conversationId>",
    "correlation_id": "<guid>",
    "request_message": "Tell me a short joke",
    "event_type": "final",
    "response_text": "Why did the cloud...",
    "usage": {
      "type": "usage_details",
      "input_token_count": 159,
      "output_token_count": 29,
      "total_token_count": 188
    }
  }
]