mlflow

MLflow is the largest open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data. With over 60 million monthly downloads, thousands of organizations rely on MLflow each day to ship AI to production with confidence.

MLflow's comprehensive feature set for agents and LLM applications includes production-grade observability, evaluation, prompt management, prompt optimization and an AI Gateway for managing costs and model access. Learn more at MLflow for LLMs and Agents.

Get Started in 3 Simple Steps

From zero to full-stack LLMOps in minutes. No complex setup or major code changes required. Get Started →

Fastest start — set up tracing with the MLflow setup wizard

curl -LsSf https://mlflow.org/wizard/setup.sh | sh

Run this from your project's Git repository with Claude Code, Codex, or OpenCode installed. The wizard guides you through connecting to an MLflow server or Databricks workspace, then launches your coding agent to add tracing to your app. Prefer to wire it up yourself? Follow the three steps below.

1. Start MLflow Server

uvx mlflow server

2. Enable Logging

import mlflow

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.openai.autolog()

3. Run Your Code

from openai import OpenAI

client = OpenAI()
client.responses.create(
    model="gpt-5.4-mini",
    input="Hello!",
)

Explore traces and metrics in the MLflow UI at http://localhost:5000.

LLMs & Agents

MLflow provides everything you need to build, debug, evaluate, and deploy production-quality LLM applications and AI agents. Supports Python, TypeScript/JavaScript, Java and any other programming language. MLflow also natively integrates with OpenTelemetry and MCP.

Model Training

For machine learning and deep learning model development, MLflow provides a full suite of tools to manage the ML lifecycle:

• Experiment Tracking — Track models, parameters, metrics, and evaluation results across experiments
• Model Evaluation — Automated evaluation tools integrated with experiment tracking
• Model Registry — Collaboratively manage the full lifecycle of ML models
• Deployment — Deploy models to batch and real-time scoring on Docker, Kubernetes, Azure ML, AWS SageMaker, and more

Learn more at MLflow for Model Training.

Integrations

MLflow supports all agent frameworks, LLM providers, tools, and programming languages. We offer one-line automatic tracing for more than 60 frameworks. See the full integrations list.

OpenTelemetry

Agent Frameworks (Python)