mlflow
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-
What is mlflow?
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.
Overview of mlflow
What it is
MLflow is an open source AI engineering platform for agents, LLMs, and ML models. It enables teams to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data. According to the README, MLflow has over 60 million monthly downloads and thousands of organizations use it daily. It supports Python, TypeScript/JavaScript, Java, and other languages, and integrates with OpenTelemetry and MCP.
Key features
- Observability: Capture complete traces of LLM applications and agents, built on OpenTelemetry, supporting any LLM provider and agent framework.
- Evaluation: Run systematic evaluations with 50+ built-in metrics and LLM judges, or define custom metrics.
- Prompts & Optimization: Version, test, and deploy prompts with lineage tracking; automatically optimize prompts with state-of-the-art algorithms.
- AI Gateway: Unified API gateway for all LLM providers with rate limiting, fallbacks, cost control, credential management, guardrails, and traffic splitting.
- Experiment Tracking: Track models, parameters, metrics, and evaluation results across experiments.
- 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.
Use cases
Building, debugging, evaluating, and deploying production-quality LLM applications and AI agents. Managing the full machine learning lifecycle from experiment tracking to model deployment. Controlling costs and managing access to models and data across teams.
Getting started
Run the setup wizard from your project's Git repository: bash curl -LsSf https://mlflow.org/wizard/setup.sh | sh Start the MLflow server: bash uvx mlflow server Enable logging in your Python code: python import mlflow mlflow.set_tracking_uri('http://localhost:5000') mlflow.openai.autolog() Then run your code and explore traces at http://localhost:5000.
License and pricing
MLflow is open source under the Apache 2.0 license. It is free to use.
Based on the project's public README and repository metadata.
