Beta Telemetry

AG2 Beta includes a TelemetryMiddleware that emits OpenTelemetry spans for agent turns, LLM calls, tool executions, and human-in-the-loop interactions.

The middleware follows the OpenTelemetry GenAI Semantic Conventions, so traces can be exported to any compatible backend -- Jaeger, Grafana Tempo, Datadog, Honeycomb, Langfuse, and others.

Installation

pip install "ag2[openai,tracing]"

Quick Start

from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter

from autogen.beta import Agent
from autogen.beta.config import OpenAIConfig
from autogen.beta.middleware.builtin import TelemetryMiddleware

# 1. Configure OpenTelemetry
resource = Resource.create(attributes={"service.name": "ag2-beta-quickstart"})
tracer_provider = TracerProvider(resource=resource)
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
trace.set_tracer_provider(tracer_provider)

# 2. Create agent with telemetry middleware
agent = Agent(
    "assistant",
    prompt="You are a helpful assistant.",
    config=OpenAIConfig(model="gpt-4o-mini"),
    middleware=[\
        TelemetryMiddleware(\
            tracer_provider=tracer_provider,\
            agent_name="assistant",\
        ),\
    ],
)

# 3. Run -- spans are emitted automatically
import asyncio
reply = asyncio.run(agent.ask("What is the capital of France?"))

Trace Hierarchy

Each ask() call produces a root span with child spans for LLM calls, tool executions, and human input:

invoke_agent assistant
  |-- chat gpt-4o-mini              # LLM API call
  |-- execute_tool get_weather      # tool execution
  |-- chat gpt-4o-mini              # LLM call after tool result
  +-- await_human_input assistant   # human-in-the-loop

Span Types

Every span includes an ag2.span.type attribute:

Semantic Attributes

Spans carry standard OpenTelemetry GenAI attributes: