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AutoGen AgentChat is the framework within Microsoft’s AutoGen that enables robust multi-agent application.
  • Phoenix Cloud
  • Command Line
  • Docker
  • Notebook
Sign up for Phoenix:
  1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
  2. Click Create Space, then follow the prompts to create and launch your space.
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint and API Key:From your new Phoenix Space
  1. Create your API key from the Settings page
  2. Copy your Hostname from the Settings page
  3. In your code, set your endpoint and API key: 
import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"
Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

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Install

pip install openinference-instrumentation-autogen-agentchat autogen-agentchat autogen_ext

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Setup

Connect to your Phoenix instance using the register function. 
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="agentchat-agent", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

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Run AutoGen 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_exit 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())

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Observe

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

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