mlflow · Python Simple Repository Browser

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Meta

Maintainer: Databricks

Requires Python: >=3.10

Classifiers

Development Status

5 - Production/Stable

Intended Audience

Developers
End Users/Desktop
Science/Research
Information Technology

Topic

Scientific/Engineering :: Artificial Intelligence
Software Development :: Libraries :: Python Modules

License

OSI Approved :: Apache Software License

Operating System

OS Independent

Programming Language

Python :: 3.10

The Open Source AI Engineering Platform for Agents, LLMs & Models

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.

Website · Docs · Feature Request · News · YouTube · Events

Get Started in 3 Simple Steps

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.

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.

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 →

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

_{OpenTelemetry}

Agent Frameworks (Python)

_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

Agent Frameworks (TypeScript)

_LangChain

_LangGraph

_{Vercel AI SDK}

_Mastra

_VoltAgent

Agent Frameworks (Java)

_{Spring AI}

_{Quarkus LangChain4j}

Model Providers

_OpenAI	_Anthropic	_Databricks	_Gemini	_{Amazon Bedrock}	_LiteLLM
_Mistral	_{xAI / Grok}	_Ollama	_Groq	_DeepSeek	_Qwen
_{Moonshot AI}	_Cohere	_BytePlus	_{Novita AI}	_FireworksAI	_{Together AI}

Gateways

_Databricks	_{LiteLLM Proxy}	_{Vercel AI Gateway}	_OpenRouter	_Portkey
_Helicone	_{Kong AI Gateway}	_{PydanticAI Gateway}	_TrueFoundry

Tools & No-Code

_Instructor	_{Claude Code}	_Opencode	_Langfuse	_{Arize / Phoenix}	_Goose
_Langflow

Hosting MLflow

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

💭 Support

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.

🤝 Contributing

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.

⭐️ Star History

✏️ Citation

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.

👥 Core Members

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