--- id: mlflow version: "3.15.1" license: Copyright 2018 Databricks, Inc. All rights reserved. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1.… (full text in the JSON record) license_treatment: permissive maintenance: active --- # mlflow — MLflow is an open source platform for the complete machine learning lifecycle License: permissive · Maintenance: active · Popularity: top 1,000 on PyPI ## Install pip install mlflow uv add mlflow poetry add mlflow ## Description

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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](https://mlflow.org/llm-tracing), [evaluate](https://mlflow.org/llm-evaluation), [monitor](https://mlflow.org/ai-monitoring), and [optimize](https://mlflow.org/prompt-optimization) 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](https://mlflow.org/docs/latest/genai/tracing), [evaluation](https://mlflow.org/docs/latest/genai/eval-monitor), [prompt... ## AI interpretation — verify before relying MLflow is an open-source AI engineering platform for building, debugging, evaluating, and deploying LLM applications, AI agents, and ML models with integrated observability, evaluation, prompt management, and model tracking. Verdict: MLflow is a mature, actively maintained platform (Production/Stable status, 27502 stars, zero known vulnerabilities) with low install friction and permissive licensing. Its 20 runtime dependencies reflect a full-featured ML/AI stack. Ideal for teams shipping LLM applications and ML models to production with comprehensive observability, evaluation, and governance built in. [View on SkillFed](https://skillfed.io/packages/mlflow) · [View on PyPI](https://pypi.org/project/mlflow/)