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)
Agent Frameworks (TypeScript)
Agent Frameworks (Java)
Model Providers
Gateways
Tools & No-Code
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.
💭 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 bottom right.
- 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 ([email protected])
or join us on Slack.
🤝 Contributing
We happily welcome contributions to MLflow!
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.
