Get started: MLflow Tracing for GenAI (Databricks 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.

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

Environment setup

1. Create a new notebook in your Databricks workspace. The notebook will have a default MLflow experiment that is the container for your GenAI application. Learn more about MLflow experiments in the MLflow concepts section.

2. Install required packages:

   • mlflow[databricks]: Use the latest version of MLflow to get more features and improvements.
   • openai: This tutorial will use the OpenAI API client to call Databricks-hosted models.

Python

%pip install -qq --upgrade "mlflow[databricks]>=3.1.0" openai
dbutils.library.restartPython()

Step 1: Instrument your application with tracing

The code snippets below define a simple GenAI app that completes sentence templates using an LLM.

First, create an OpenAI client to connect to Databricks-hosted foundation models:

Python

from databricks.sdk import WorkspaceClient

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

model_name = "databricks-claude-sonnet-4"

Alternatively, you could use the OpenAI SDK to connect to OpenAI-hosted models:

Python

import openai

# Ensure your OPENAI_API_KEY is set in your environment
# os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>" # Uncomment and set if not globally configured

client = openai.OpenAI()

model_name = "gpt-4o-mini"

Second, define and run your application. Instrumenting the app with tracing simply uses:

• mlflow.openai.autolog(): Automatic instrumentation to capture the details of the call to the OpenAI SDK
• @mlflow.trace: Decorator that makes it easy to trace any Python function

Python

import mlflow
import os

# Enable auto-tracing for OpenAI
mlflow.openai.autolog()

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

# 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,
    temperature=0.1,
    max_tokens=200,
    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)

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

Step 2: View the trace in MLflow

The trace will appear below the notebook cell.

Optionally, you can go to the MLflow experiment UI to see the trace:

1. Click the experiments icon on the right sidebar.
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.

Understand the trace

The trace you 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.

Next steps

• MLflow Tracing guide - Start here for more in-depth learning about MLflow Tracing
• MLflow Tracing integrations - 20+ libraries with automatic tracing integrations
• Tracing concepts - Understand the fundamentals of MLflow Tracing

Example notebook

Get started: MLflow Tracing for GenAI (Databricks Notebook)

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