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.

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

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.

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.

Observability Capture complete traces of your LLM applications and agents for deep behavioral insights. Built on OpenTelemetry, supporting any LLM provider and agent framework. Monitor production quality, costs, and safety. Getting Started →	Evaluation Run systematic evaluations, track quality metrics over time, and catch regressions before they reach production. Choose from 50+ built-in metrics and LLM judges, or define your own. Getting Started →
Prompts & Optimization Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts with state-of-the-art algorithms to improve performance. Getting Started →	AI Gateway Unified API gateway for all LLM providers. Route requests, manage rate limits, handle fallbacks, and control costs through an OpenAI-compatible interface with built-in credential management, guardrails and traffic splitting for A/B testing. Getting Started →

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.

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

LangChain	LangGraph	OpenAI Agent	DSPy	PydanticAI	Google ADK
Microsoft Agent	CrewAI	LlamaIndex	AutoGen	Strands	LiveKit Agents
Agno	Bedrock AgentCore	Smolagents	Semantic Kernel	DeepAgent	AG2
Haystack	Koog	txtai	Pipecat	Watsonx

LangChain

LangGraph

Vercel AI SDK

Mastra

VoltAgent

Spring AI

Quarkus LangChain4j

OpenAI	Anthropic	Databricks	Gemini	Amazon Bedrock	LiteLLM
Mistral	xAI / Grok	Ollama	Groq	DeepSeek	Qwen
Moonshot AI	Cohere	BytePlus	Novita AI	FireworksAI	Together AI

Databricks	LiteLLM Proxy	Vercel AI Gateway	OpenRouter	Portkey
Helicone	Kong AI Gateway	PydanticAI Gateway	TrueFoundry

Instructor	Claude Code	Opencode	Langfuse	Arize / Phoenix	Goose
Langflow

MLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is vendor-neutral — whether you're building AI agents, LLM applications, or ML models, you have access to MLflow's core capabilities.

Databricks

Amazon SageMaker

Azure ML

Nebius

Self-Hosted

• For help or questions about MLflow usage (e.g. "how do I do X?") visit the documentation.
• In the documentation, you can ask the question to our AI-powered chat bot. Click on the "Ask AI" button at the right bottom.
• Join the virtual events like office hours and meetups.
• To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.
• For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com) or join us on Slack.

We happily welcome contributions to MLflow!

• Submit bug reports and feature requests
• Contribute for good-first-issues and help-wanted
• Writing about MLflow and sharing your experience

Please see our contribution guide to learn more about contributing to MLflow.

If you use MLflow in your research, please cite it using the "Cite this repository" button at the top of the GitHub repository page, which will provide you with citation formats including APA and BibTeX.

MLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.

• Ben Wilson
• Corey Zumar
• Daniel Lok
• Gabriel Fu
• Harutaka Kawamura
• Joel Robin P
• Matt Prahl
• Pat Sukprasert
• Serena Ruan
• Tomu Hirata
• Weichen Xu
• Yuki Watanabe