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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 · Try Demo · Docs · News · 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 →
Fastest start — set up tracing with our CLI
uvx mlflow@latest agent setup
One command installs the MLflow skills and launches your coding agent of choice 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.
Observability Evaluation
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. 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 → Getting Started →
Try Demo → Try Demo →
Prompts & Optimization AI Gateway
Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts with state-of-the-art algorithms to improve performance. 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 → Getting Started →
Try Demo →
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.
* Ben Wilson
* Corey Zumar
* Daniel Lok
* Gabriel Fu
* Harutaka Kawamura
* Humair Khan
* Joel Robin P
* Matthew Prahl
* Pat Sukprasert
* Serena Ruan
* Tomu Hirata
* Weichen Xu
* Yuki Watanabe
About
The 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.
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