> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140-claude-llms-txt-2026-08-12.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AutoGen AgentChat Tracing

> Auto-instrument your AgentChat application for seamless observability

export const projectName_0 = "agentchat-agent"

[AutoGen AgentChat](https://microsoft.github.io/autogen/stable//user-guide/agentchat-user-guide/index.html) is the framework within Microsoft's AutoGen that enables robust multi-agent application.

## Launch Phoenix

<Card>
  <Tabs>
    <Tab title="Self-Host">
      Run Phoenix on your own infrastructure, backed by PostgreSQL so traces persist beyond a single process. This is the option to reach for once Phoenix is shared across a team or environment.

      The [self-hosting guide](/docs/phoenix/self-hosting) covers [Kubernetes](/docs/phoenix/self-hosting/deployment-options/kubernetes), [Helm](/docs/phoenix/self-hosting/deployment-options/kubernetes-helm), [Railway](/docs/phoenix/self-hosting/deployment-options/railway), [AWS CloudFormation](/docs/phoenix/self-hosting/deployment-options/aws-with-cloudformation), [Google Cloud Run](/docs/phoenix/self-hosting/deployment-options/google-cloud-run), [Azure](/docs/phoenix/self-hosting/deployment-options/azure), and [Render](/docs/phoenix/self-hosting/deployment-options/render), plus authentication and configuration.
    </Tab>

    <Tab title="Local">
      ```bash theme={null}
      uvx arize-phoenix serve
      ```

      No [uv](https://docs.astral.sh/uv/)? `pip install arize-phoenix && phoenix serve` does the same thing. See [Terminal setup](/docs/phoenix/environments#terminal) for customization.
    </Tab>

    <Tab title="Container">
      ```bash theme={null}
      docker run -p 6006:6006 -p 4317:4317 arizephoenix/phoenix:latest
      ```

      Images are published to [Docker Hub](https://hub.docker.com/r/arizephoenix/phoenix). See [Docker](/docs/phoenix/self-hosting/deployment-options/docker) for volumes, PostgreSQL, and other options.
    </Tab>
  </Tabs>

  Phoenix serves its UI and OTLP HTTP on port **6006**, and OTLP gRPC on port **4317**. For a local instance that's [http://localhost:6006](http://localhost:6006) — leave it running while you work.
</Card>

**Install packages:**

```bash theme={null}
pip install arize-phoenix-otel
```

Point your code at the Phoenix instance you started. The endpoint below is the default for a local `phoenix serve`; for a deployment running elsewhere, use its hostname instead.

```python theme={null}
import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

# Only if the deployment has authentication enabled
# os.environ["PHOENIX_API_KEY"] = "your-api-key"
```

## Install

```bash theme={null}
pip install openinference-instrumentation-autogen-agentchat autogen-agentchat autogen_ext
```

## Setup

Connect your application to Phoenix with the `register` function:

<CodeBlock language="python">
  {`from phoenix.otel import register

    # configure the Phoenix tracer
    tracer_provider = register(
    project_name="${projectName_0}", # Default is 'default'
    auto_instrument=True # Auto-instrument your app based on installed OI dependencies
    )`}
</CodeBlock>

## 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.

```sh theme={null}
pip install autogen_ext openai
```

```python expandable theme={null}
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

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

<Frame>
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/agentchat-phoenix.png" />
</Frame>

## Resources

* [AutoGen AgentChat documentation](https://microsoft.github.io/autogen/stable//user-guide/agentchat-user-guide/index.html)

* [AutoGen AgentChat OpenInference Package](https://pypi.org/project/openinference-instrumentation-autogen-agentchat/)