Tracing AutoGen

AutoGen is an open-source framework for building event-driven, distributed, scalable, and resilient AI agent systems.

MLflow Tracing provides automatic tracing capability for AutoGen, an open-source multi-agent framework. By enabling auto tracing for AutoGen by calling the mlflow.autogen.autolog function, MLflow will capture nested traces and log them to the active MLflow Experiment upon agents execution.

Python

import mlflow

mlflow.autogen.autolog()

MLflow captures the following information about the multi-agent execution:

• Which agent is called at different turns
• Messages passed between agents
• LLM and tool calls made by each agent, organized per an agent and a turn
• Latencies
• Any exception if raised

Prerequisites

To use MLflow Tracing with AutoGen, you need to install MLflow and the pyautogen library.

Development

For development environments, install the full MLflow package with Databricks extras and pyautogen:

Bash

pip install --upgrade "mlflow[databricks]>=3.1" pyautogen

The full mlflow[databricks] package includes all features for local development and experimentation on Databricks.

Production

For production deployments, install mlflow-tracing and pyautogen:

Bash

pip install --upgrade mlflow-tracing pyautogen

The mlflow-tracing package is optimized for production use.

note

MLflow 3 is highly recommended for the best tracing experience with AutoGen.

Before running the examples, you'll need to configure your environment:

For users outside Databricks notebooks: Set your Databricks environment variables:

Bash

export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-personal-access-token"

For users inside Databricks notebooks: These credentials are automatically set for you.

OpenAI API key: Set your API key as an environment variable:

Bash

export OPENAI_API_KEY="your-openai-api-key"

Basic Example

Python

import os
from typing import Annotated, Literal

from autogen import ConversableAgent

import mlflow

# Ensure your OPENAI_API_KEY (or other LLM provider keys) is set in your environment
# os.environ["OPENAI_API_KEY"] = "your-openai-api-key" # Uncomment and set if not globally configured

# Turn on auto tracing for AutoGen
mlflow.autogen.autolog()

# Set up MLflow tracking on Databricks
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Shared/autogen-tracing-demo")


# Define a simple multi-agent workflow using AutoGen
config_list = [
    {
        "model": "gpt-4o-mini",
        # Please set your OpenAI API Key to the OPENAI_API_KEY env var before running this example
        "api_key": os.environ.get("OPENAI_API_KEY"),
    }
]

Operator = Literal["+", "-", "*", "/"]


def calculator(a: int, b: int, operator: Annotated[Operator, "operator"]) -> int:
    if operator == "+":
        return a + b
    elif operator == "-":
        return a - b
    elif operator == "*":
        return a * b
    elif operator == "/":
        return int(a / b)
    else:
        raise ValueError("Invalid operator")


# First define the assistant agent that suggests tool calls.
assistant = ConversableAgent(
    name="Assistant",
    system_message="You are a helpful AI assistant. "
    "You can help with simple calculations. "
    "Return 'TERMINATE' when the task is done.",
    llm_config={"config_list": config_list},
)

# The user proxy agent is used for interacting with the assistant agent
# and executes tool calls.
user_proxy = ConversableAgent(
    name="Tool Agent",
    llm_config=False,
    is_termination_msg=lambda msg: msg.get("content") is not None
    and "TERMINATE" in msg["content"],
    human_input_mode="NEVER",
)

# Register the tool signature with the assistant agent.
assistant.register_for_llm(name="calculator", description="A simple calculator")(
    calculator
)
user_proxy.register_for_execution(name="calculator")(calculator)
response = user_proxy.initiate_chat(
    assistant, message="What is (44231 + 13312 / (230 - 20)) * 4?"
)

Disable auto-tracing

Auto tracing for AutoGen can be disabled globally by calling mlflow.autogen.autolog(disable=True) or mlflow.autolog(disable=True).