MLflow is an open-source AI engineering platform covering the full lifecycle of building, evaluating, monitoring, and optimizing AI applications – agents, LLMs, and traditional ML models alike. It handles experiment tracking, hyperparameter tuning, and model registry for classic ML workflows, plus prompt optimization using state-of-the-art algorithms and AI-powered issue detection across correctness, latency, execution, adherence, relevance, and safety dimensions for modern LLM and agent applications.
Covering both traditional ML lifecycle management and the newer LLM/agent evaluation problem in one platform is what sets MLflow apart from tools built for only one era of AI development – teams running both classic models and newer agentic applications don’t need two separate toolchains. AI-powered issue detection across six distinct dimensions (not just “did it work”) gives a genuinely structured way to diagnose why an LLM application is underperforming, rather than guessing from raw output logs.
MLflow is 100% open source under the Apache 2.0 license, backed by the Linux Foundation with no vendor lock-in, self-hostable locally or on your own infrastructure. It reports over 20,000 GitHub stars, 900-plus contributors, and 30 million-plus package downloads per month, with an AI-powered coding agent for automatic setup added recently. Free with no signup required.
