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Tracing OpenAI Swarm

warning

OpenAI Swarm integration has been deprecated because the library is being replaced by the new OpenAI Agents SDK. Please consider migrating to the new SDK for the latest features and support.

OpenAI Tracing via autolog

MLflow Tracing provides automatic tracing capability for OpenAI Swarm, a multi-agent framework developed by OpenAI. By enabling auto tracing for OpenAI by calling the mlflow.openai.autolog function, MLflow will capture nested traces and log them to the active MLflow Experiment upon invocation of OpenAI SDK.

Python
import mlflow

mlflow.openai.autolog()

In addition to the basic LLM call tracing for OpenAI, MLflow captures the intermediate steps that the Swarm agent operates and all tool-calling by the agent.

Prerequisites​

To use MLflow Tracing with OpenAI Swarm, you need to install MLflow, the OpenAI SDK, and the openai-swarm library.

For development environments, install the full MLflow package with Databricks extras, openai, and openai-swarm:

Bash
pip install --upgrade "mlflow[databricks]>=3.1" openai openai-swarm

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

note

MLflow 3 is highly recommended. Note that OpenAI Swarm itself has been deprecated in favor of the OpenAI Agents SDK.

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.

API Keys: Ensure your OpenAI API key is set:

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

Basic Example​

Python
import mlflow
from swarm import Swarm, Agent
import os

# Ensure your OPENAI_API_KEY is set in your environment
# os.environ["OPENAI_API_KEY"] = "your-openai-api-key" # Uncomment and set if not globally configured

# Calling the autolog API will enable trace logging by default.
mlflow.openai.autolog()

# Set up MLflow tracking to Databricks
mlflow.set_tracking_uri("databricks")
mlflow.set_experiment("/Shared/openai-swarm-demo")

# Define a simple multi-agent workflow using OpenAI Swarm
client = Swarm()


def transfer_to_agent_b():
    return agent_b


agent_a = Agent(
    name="Agent A",
    instructions="You are a helpful agent.",
    functions=[transfer_to_agent_b],
)

agent_b = Agent(
    name="Agent B",
    instructions="Only speak in Haikus.",
)

response = client.run(
    agent=agent_a,
    messages=[{"role": "user", "content": "I want to talk to agent B."}],
)

Disable auto-tracing​

Auto tracing for OpenAI Swarm can be disabled globally by calling mlflow.openai.autolog(disable=True) or mlflow.autolog(disable=True).

Next Steps​

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