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

Complete the shared environment setup steps in ../README.md, including creating a virtual environment, installing dependencies, and configuring Azure OpenAI credentials and storage settings.

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

Send a prompt to the agent:

curl -X POST http://localhost:7071/api/agents/CallbackAgent/run \
   -H "Content-Type: application/json" \
   -d '{"message": "Tell me a short joke"}'

Poll callback telemetry (replace <conversationId> with the value from the POST response):

curl http://localhost:7071/api/agents/CallbackAgent/callbacks/<conversationId>

Reset stored events:

curl -X DELETE http://localhost:7071/api/agents/CallbackAgent/callbacks/<conversationId>

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
    }
  }
]