Vibe Coding Discovermlflow
View on GitHubThe open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
MLflow is an LLMOps/AI engineering platform for debugging, evaluating, monitoring and optimizing agents and LLM apps, plus classic ML experiment tracking, a model registry and deployment tooling. Use it for tracing, evals, prompt management and AI gateway cost control.
Use Cases
Trace and debug LLM and agent applicationsRun systematic LLM evaluations with built-in judges and metricsMonitor production quality, cost and safety of AI appsVersion, test and optimize prompts with lineage trackingRoute and govern LLM provider access via an OpenAI-compatible AI gatewayTrack ML experiments, parameters and metricsManage ML model lifecycle in a model registryDeploy models to batch and real-time scoring on Docker, Kubernetes, SageMaker and Azure MLAdd one-line autologging/tracing for 60+ agent and LLM frameworksCollect OpenTelemetry traces from any language or provider
Built With
- Language
- Python
- Frameworks
- LangChain · LangGraph · OpenAI Agents · DSPy · PydanticAI · Google ADK · Microsoft Agent Framework · CrewAI · LlamaIndex · AutoGen · Strands · OpenTelemetry · FastAPI · Flask · scikit-learn · Apache Spark
Tags
llmops · observability · tracing · evaluation · prompt-management · model-registry · experiment-tracking · ai-gateway · mlops · monitoring · governance · opentelemetry · agents · llm-evaluation · model-management · cost-control