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Get started: MLflow Tracing for GenAI (Local IDE)

This quickstart helps you integrate your GenAI app with MLflow Tracing if you use a local development environment such as an IDE (VS Code, PyCharm, Cursor or others) or a locally-hosted notebook environment (Jupyter or others). If you use a Databricks Notebook, please use the Databricks Notebook quickstart instead.

What you will achieve​

By the end of this tutorial, you will have:

  • An MLflow experiment for your GenAI app
  • Your local development environment connected to MLflow
  • A simple GenAI application instrumented with MLflow Tracing
  • A trace from that app in your MLflow experiment

trace

Prerequisites​

  • Databricks Workspace: Access to a Databricks workspace.

Step 1: Install MLflow​

When working in your local IDE, you need to install MLflow with Databricks connectivity.

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

Step 2: Create a new MLflow experiment​

An MLflow experiment is the container for your GenAI application. Learn more about experiments in the concepts section.

  1. Open your Databricks workspace
  2. In the left sidebar, under AI/ML, click Experiments.
  3. At the top of the Experiments page, click on GenAI apps & agents
  4. Get the experiment ID and path by clicking on the information icon Info icon. in the upper-left. You will use these later.

create experiment

Step 3: Connect your environment to MLflow​

The code snippets below show how to set up authentication using a Databricks Personal Access Token. MLflow also works with the other Databricks-supported authentication methods.

  1. In your MLflow Experiment, click the Kebab menu icon Kebab menu icon. > Log traces locally > click Generate API Key.

  2. Copy and run the generated code in your terminal.

    Bash
    export DATABRICKS_TOKEN=<databricks-personal-access-token>
    export DATABRICKS_HOST=https://<workspace-name>.cloud.databricks.com
    export MLFLOW_TRACKING_URI=databricks
    export MLFLOW_REGISTRY_URI=databricks-uc
    export MLFLOW_EXPERIMENT_ID=<experiment-id>

Step 4: Create and instrument your application​

Create your GenAI app with tracing enabled.

  1. Create a Python file named app.py in your project directory.

  2. Initialize an OpenAI client to connect to either Databricks-hosted LLMs or LLMs hosted by OpenAI.

    Use MLflow to get an OpenAI client that connects to Databricks-hosted LLMs. Select a model from the available foundation models.

    Python
    import mlflow
    from databricks.sdk import WorkspaceClient

    # Enable MLflow's autologging to instrument your application with Tracing
    mlflow.openai.autolog()

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

    # Create an OpenAI client that is connected to Databricks-hosted LLMs
    w = WorkspaceClient()
    client = w.serving_endpoints.get_open_ai_client()

    # Select an LLM
    model_name = "databricks-claude-sonnet-4"

  3. Define and run your application:

    Use the @mlflow.trace decorator, which makes it easy to trace any Python function, combined with OpenAI automatic instrumentation to capture the details of the call to the OpenAI SDK.

    Python
    # Use the trace decorator to capture the application's entry point
    @mlflow.trace
    def my_app(input: str):
        # This call is automatically instrumented by `mlflow.openai.autolog()`
        response = client.chat.completions.create(
            model=model_name,  # This example uses a Databricks hosted LLM - you can replace this with any AI Gateway or Model Serving endpoint. If you provide your own OpenAI credentials, replace with a valid OpenAI model e.g., gpt-4o, etc.
            messages=[
                {
                    "role": "system",
                    "content": "You are a helpful assistant.",
                },
                {
                    "role": "user",
                    "content": input,
                },
            ],
        )
        return response.choices[0].message.content

    result = my_app(input="What is MLflow?")
    print(result)

  4. Run the application

    Bash
    python app.py

For details on adding tracing to apps, see the tracing instrumentation guide and the 20+ library integrations.

Step 5: View the trace in MLflow​

  1. Navigate back to the MLflow experiment UI.
  2. You will now see the generated trace in the Traces tab.
  3. Click on the trace to view its details.

Trace Details

Understanding the trace​

The trace you've just created shows:

  • Root span: Represents the inputs to the my_app(...) function
    • Child span: Represents the OpenAI completion request
  • Attributes: Contains metadata like model name, token counts, and timing information
  • Inputs: The messages sent to the model
  • Outputs: The response received from the model

This simple trace already provides valuable insights into your application's behavior, such as:

  • What was asked
  • What response was generated
  • How long the request took
  • How many tokens were used (affecting cost)

For more complex applications like RAG systems or multi-step agents, MLflow Tracing provides even more value by revealing the inner workings of each component and step.

Guides and references​

For details on concepts and features in this guide, see:

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