MLflow

MLflow is the largest open-source AI engineering platform, trusted by thousands of organizations with over 30 million monthly downloads. It covers the full lifecycle of AI development — from LLM and agent observability to classical ML experiment tracking — under a single Apache 2.0-licensed platform. Built on OpenTelemetry and supporting 100+ integrations, MLflow works with any cloud, framework, or LLM provider without vendor lock-in.

• Observability & Tracing: Capture complete traces of LLM applications and agents using OpenTelemetry-compatible instrumentation; monitor production quality, costs, and safety in real time.
• LLM Evaluation: Run systematic evaluations with 50+ built-in metrics and LLM judges, track quality over time, and catch regressions before they reach production.
• Automatic Issue Detection: Use AI-powered analysis to automatically detect issues across correctness, latency, execution, adherence, relevance, and safety dimensions in your traces.
• Prompt Registry & Optimization: Version, test, and deploy prompts with full lineage tracking; automatically optimize prompts using state-of-the-art algorithms.
• AI Gateway: Unified OpenAI-compatible API gateway for all LLM providers — route requests, manage rate limits, handle fallbacks, and control costs.
• Agent Server: Deploy agents to production with a single command using a FastAPI-based hosting solution with streaming support and built-in tracing.
• Experiment Tracking: Log parameters, metrics, and artifacts for ML experiments; compare runs and reproduce results with ease.
• Model Registry & Deployment: Manage the full ML model lifecycle from staging to production with a centralized model registry and deployment tools.
• Broad Framework Support: Integrates natively with OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, AutoGen, PyTorch, HuggingFace, and 100+ more frameworks.
• Multi-language Support: Supports Python, TypeScript/JavaScript, Java, and R, making it accessible across diverse engineering teams.

To get started, install MLflow via pip, launch the tracking server with uvx mlflow server, add a single mlflow.openai.autolog() call to your code, and explore traces and metrics in the MLflow UI.