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AutoGen

AutoGen is an open-source Python framework for orchestrating multi-agent LLM interactions with shared memory and tool integrations to build scalable AI workflows

Website: https://microsoft.github.io/autogen/stable/

Featured Tutorials

AutoGen Tracing

AutoGen is an agent framework from Microsoft that allows for complex Agent creation. It is unique in its ability to create multiple agents that work together.

The AutoGen Agent framework allows creation of multiple agents and connection of those agents to work together to accomplish tasks.

Launch Phoenix

Install

Phoenix instruments Autogen by instrumenting the underlying model library it's using. If your agents are set up to call OpenAI, use our OpenAI instrumentor per the example below.

If your agents are using a different model, be sure to instrument that model instead by installing its respective OpenInference library.

pip install openinference-instrumentation-openai arize-phoenix-otel arize-phoenix

Setup

Connect to your Phoenix instance using the register function.

from phoenix.otel import register

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

Run Autogen

From here you can use Autogen as normal, and Phoenix will automatically trace any model calls made.

Observe

The Phoenix support is simple in its first incarnation but allows for capturing all of the prompt and responses that occur under the framework between each agent.

The individual prompt and responses are captured directly through OpenAI calls. If you're using a different underlying model provider than OpenAI, instrument your application using the respective instrumentor instead.

Resources:

Example notebook

AutoGen AgentChat Tracing

Auto-instrument your AgentChat application for seamless observability

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

Install

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

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
)

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())

Observe

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

Resources

AutoGen Tracing

AutoGen is an agent framework from Microsoft that allows for complex Agent creation. It is unique in its ability to create multiple agents that work together.

The AutoGen Agent framework allows creation of multiple agents and connection of those agents to work together to accomplish tasks.

Launch Phoenix

Install

Phoenix instruments Autogen by instrumenting the underlying model library it's using. If your agents are set up to call OpenAI, use our OpenAI instrumentor per the example below.

If your agents are using a different model, be sure to instrument that model instead by installing its respective OpenInference library.

pip install openinference-instrumentation-openai arize-phoenix-otel arize-phoenix

Setup

Connect to your Phoenix instance using the register function.

from phoenix.otel import register

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

Run Autogen

From here you can use Autogen as normal, and Phoenix will automatically trace any model calls made.

Observe

The Phoenix support is simple in its first incarnation but allows for capturing all of the prompt and responses that occur under the framework between each agent.

Resources:

Example notebook

AutoGen AgentChat Tracing

Auto-instrument your AgentChat application for seamless observability

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

Install

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

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
)

Run AutoGen AgentChat

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())

Observe

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

Resources

Sign up for Phoenix:

Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
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

Create your API key from the Settings page
Copy your Hostname from the Settings page
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

Launch your local Phoenix instance:

pip install arize-phoenix
phoenix serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

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

See Terminal for more details.

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

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

For more info on using Phoenix with Docker, see Docker.

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See self-hosting or use one of the other deployment options to retain traces.

Sign up for Phoenix:

Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
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

Create your API key from the Settings page
Copy your Hostname from the Settings page
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

Launch your local Phoenix instance:

pip install arize-phoenix
phoenix serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

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

See Terminal for more details.

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

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

For more info on using Phoenix with Docker, see Docker.

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()