MLflow is an open-source AI engineering platform for managing agents, LLM applications, and traditional machine-learning models across development and production. It gives teams a common environment for tracing, debugging, evaluating, monitoring, and optimizing AI systems while also helping control model access, prompts, costs, and operational behavior.

For LLM and agent workloads, MLflow includes OpenTelemetry-based tracing, systematic evaluations, built-in metrics and LLM judges, prompt management and optimization, and an AI Gateway that can route requests across model providers. It integrates with multiple programming languages, model providers, agent frameworks, MCP, and existing ML workflows.

MLflow is aimed at teams that need more than a model API or experimentation notebook. It provides the observability, evaluation, lineage, governance, and deployment-oriented tooling required to move AI applications from prototypes into repeatable production systems and to measure how those systems behave after release.