AutoGen AgentChat Tracing

Instrument and observe your AgentChat application

AutoGen AgentChat is the framework within Microsoft's AutoGen that enables robust multi-agent application.

Launch Arize

To get started, sign up for a free Arize account and get your Space ID and API Key.

Install

pip install openinference-instrumentation-autogen-agentchat autogen-agentchat arize-otel

Setup

Connect your application to Arize and then apply the AutogenAgentChatInstrumentor .

# Import Arize OTel registration
from arize.otel import register

# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.autogen_agentchat import AutogenAgentChatInstrumentor

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "my-agentchat-project", # name this to whatever you would like
)

# Start the instrumentor for AutogenAgentChat
AutogenAgentChatInstrumentor().instrument(tracer_provider=tracer_provider)

Run AgentChat

We’re going to run an AgentChat example using a multi-agent team. To get started, install the required packages to use your LLMs with AgentChat. In this example, we’ll use OpenAI as the LLM provider.

pip install autogen_ext openai

import asyncio
import os
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import TextMentionTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai._openai_client import OpenAIChatCompletionClient

os.environ["OPENAI_API_KEY"] = "your-api-key"

async def main():
    model_client = OpenAIChatCompletionClient(
        model="gpt-4",
    )

    # Create two agents: a primary and a critic
    primary_agent = AssistantAgent(
        "primary",
        model_client=model_client,
        system_message="You are a helpful AI assistant.",
    )

    critic_agent = AssistantAgent(
        "critic",
        model_client=model_client,
        system_message="""
        Provide constructive feedback.
        Respond with 'APPROVE' when your feedbacks are addressed.
        """,
    )

    # Termination condition: stop when the critic says "APPROVE"
    text_termination = TextMentionTermination("APPROVE")

    # Create a team with both agents
    team = RoundRobinGroupChat(
        [primary_agent, critic_agent],
        termination_condition=text_termination
    )

    # Run the team on a task
    result = await team.run(task="Write a short poem about the fall season.")
    await model_client.close()
    print(result)

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

Observe

Arize provides visibility into your AgentChat operations by automatically tracing all interactions.

Resources

• AutoGen AgentChat documentation

• AutoGen AgentChat OpenInference Package