MLflow

MLflow is an open-source platform designed to help developers and teams build, manage, and improve AI applications, including those powered by large language models (LLMs), autonomous agents, and traditional machine learning models. Its core purpose is to streamline the iterative process of developing AI products by providing tools for debugging, evaluating, and monitoring these systems in production and development environments. The platform captures complete traces of LLM application executions and agent behaviors, using OpenTelemetry as the underlying instrumentation framework. This allows developers to see detailed logs of every step an agent takes, including tool calls, API requests, and internal reasoning, making it easier to diagnose unexpected outputs, latency issues, or failures. MLflow supports any LLM provider — such as OpenAI, Anthropic, or open-source models — and works with popular agent frameworks like LangChain, CrewAI, and AutoGen. The tracing data is structured to show parent-child relationships, timing, and token usage, giving teams a clear view of how their applications behave in real time. For evaluation, MLflow offers a systematic approach to measuring quality. Users can run batch evaluations using over 50 built-in metrics, including correctness, faithfulness, relevance, and toxicity, or define custom metrics tailored to specific use cases. It also integrates LLM judges — specialized language models that score outputs against criteria like coherence or instruction following. These evaluations can be scheduled or triggered after each deployment, and the results are tracked over time so teams can spot regressions before they reach end users. The platform supports comparing multiple model versions or prompt variations side by side, helping developers