Skip to main content
LangSmith can capture traces generated by AutoGen using OpenTelemetry instrumentation. This guide shows you how to automatically capture traces from your AutoGen multi-agent conversations and send them to LangSmith for monitoring and analysis.

​
Installation

Install the required packages using your preferred package manager: 
pip install langsmith autogen-agentchat autogen-ext opentelemetry-instrumentation-openai

​
Setup

​
1. Configure environment variables

Set your API keys and project name: 
export LANGSMITH_API_KEY=<your_langsmith_api_key>
export LANGSMITH_PROJECT=<your_project_name>
export OPENAI_API_KEY=<your_openai_api_key>

​
2. Configure OpenTelemetry integration

In your AutoGen application, configure the LangSmith OpenTelemetry integration along with the OpenAI instrumentor: 
from langsmith.integrations.otel import OtelSpanProcessor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

# Set up tracer provider
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(OtelSpanProcessor())
trace.set_tracer_provider(tracer_provider)

# Instrument OpenAI calls
OpenAIInstrumentor().instrument()

​
3. Create and run your AutoGen application

Once configured, your AutoGen application will automatically send traces to LangSmith. Pass the tracer provider to the runtime for full tracing coverage: 
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.ui import Console
from autogen_core import SingleThreadedAgentRuntime
from autogen_ext.models.openai import OpenAIChatCompletionClient
from langsmith.integrations.otel import OtelSpanProcessor
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.instrumentation.openai import OpenAIInstrumentor

# Set up tracing
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(OtelSpanProcessor())
trace.set_tracer_provider(tracer_provider)
OpenAIInstrumentor().instrument()

# Define a tool
def percentage_change(start: float, end: float) -> float:
    """Calculate percentage change between two values."""
    if start == 0:
        return float("inf")
    return ((end - start) / start) * 100

async def main():
    model_client = OpenAIChatCompletionClient(model="gpt-4o")
    tracer = trace.get_tracer("autogen-demo")

    with tracer.start_as_current_span("run_team"):
        planning_agent = AssistantAgent(
            "PlanningAgent",
            description="Plans tasks and delegates.",
            model_client=model_client,
            system_message=(
                "You are a planning agent. Plan and delegate tasks.\n"
                "When assigning tasks, use: 1. <agent> : <task>\n"
                'After tasks complete, summarize and end with "TERMINATE".'
            ),
        )

        data_analyst = AssistantAgent(
            "DataAnalystAgent",
            description="Performs calculations.",
            model_client=model_client,
            tools=[percentage_change],
            system_message="You are a data analyst. Use tools to compute results.",
        )

        termination = TextMentionTermination("TERMINATE") | MaxMessageTermination(max_messages=25)

        # Pass tracer_provider to the runtime
        runtime = SingleThreadedAgentRuntime(tracer_provider=trace.get_tracer_provider())
        runtime.start()

        team = SelectorGroupChat(
            [planning_agent, data_analyst],
            model_client=model_client,
            termination_condition=termination,
            allow_repeated_speaker=True,
            runtime=runtime,
        )

        task = "You started with 100 apples, now you have 120 apples. What is the percentage change?"
        await Console(team.run_stream(task=task))

        await runtime.stop()

    await model_client.close()

if __name__ == "__main__":
    asyncio.run(main())

​
Advanced usage

​
Custom metadata and tags

You can add custom metadata to your traces by setting span attributes: 
from opentelemetry import trace

tracer = trace.get_tracer(__name__)

async def run_with_metadata():
    with tracer.start_as_current_span("autogen_workflow") as span:
        span.set_attribute("langsmith.metadata.session_type", "multi_agent")
        span.set_attribute("langsmith.metadata.agent_count", "2")
        span.set_attribute("langsmith.span.tags", "autogen,planning")

        # Your AutoGen code here
        await Console(team.run_stream(task=task))

​
Combining with other instrumentors

You can combine AutoGen tracing with other OpenTelemetry instrumentors: 
from opentelemetry.instrumentation.openai import OpenAIInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

# Initialize multiple instrumentors
OpenAIInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()

​
Resources

Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.