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Tracing a GenAI App (Notebook)

This quickstart helps you integrate your GenAI app with MLflow Tracing if you use a Databricks Notebook as your development environment. If you use a local IDE, please use the IDE quickstart instead.

What you'll achieve​

By the end of this tutorial, you will have:

  • A Databricks Notebook with a linked MLflow Experiment for your GenAI app
  • 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: Create a Databricks Notebook​

note

Creating a Databricks Notebook will create an MLflow Experiment that is the container for your GenAI application. Learn more about the Experiment and what it contains in the data model section.

  1. Open your Databricks workspace
  2. Go to New at the top of the left sidebar
  3. Click Notebook

Notebook Creation

Databricks runtimes include MLflow. However, for the best experience with GenAI capabilities, including the most comprehensive tracing features and robust support, it is highly recommended to use the latest version of MLflow.

Update MLflow in your notebook by running the following:

Python
%pip install --upgrade "mlflow[databricks]>=3.1" openai
dbutils.library.restartPython()
  • mlflow[databricks]>=3.1: This command ensures you have MLflow 3.1 or a more recent version, along with the databricks extra for seamless connectivity and functionality within Databricks.
  • dbutils.library.restartPython(): This is crucial to ensure the newly installed version is used by the Python kernel.
MLflow Version Recommendation

While tracing features are available in MLflow 2.15.0+, it is strongly recommended to install MLflow 3 (specifically 3.1 or newer if using mlflow[databricks]) for the latest GenAI capabilities, including expanded tracing features and robust support.

Step 3: Instrument your application​

Databricks provides out-of-the-box access to popular frontier and open source foundation LLMs. To run this quickstart, you can choose from the following model hosting options:

Run the following code in a notebook cell. It uses the @mlflow.trace decorator combined with OpenAI automatic instrumentation to capture the details of the LLM request.

Use MLflow to get an OpenAI client that connects to Databricks-hosted LLMs. The code snippet below uses Anthropic's Claude Sonnet LLM, but you can choose 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()

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

# 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(
    # Replace this model name with any Databricks-hosted LLM, AI Gateway, or Model Serving endpoint name.
    model="databricks-claude-sonnet-4",
    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)

Step 4: View the Trace in MLflow​

The Trace will appear below the Notebook cell.

Notebook tracing UI

Optionally, you can go to the MLflow Experiment UI to see the Trace:

  1. Click the Experiment icon on the right side of your screen.
  2. Click the open icon next to Experiment Runs.
  3. The generated trace appears in the Traces tab.
  4. Click the trace to view its 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)
tip

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.

Next steps​

Continue your journey with these recommended actions and tutorials.

Reference guides​

Explore detailed documentation for concepts and features mentioned in this guide.

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