Ragas is an open-source toolkit designed to evaluate and optimize LLM applications. It provides objective metrics, automated test-data generation, and production-aligned feedback loops to help teams measure and improve model behavior in real-world scenarios.
Key features
- Objective metrics: combine LLM-driven and traditional metrics for fine-grained evaluation.
- Test data generation: automatically create diverse, production-aligned test sets.
- Integrations: works with popular LLM frameworks (e.g. LangChain) and observability tools for easy production adoption.
Use cases
- Evaluation & regression testing: automate checks for model changes and regressions.
- Quality engineering: generate test datasets to surface real-world issues early.
- Continuous improvement: close the loop using production data to refine models.
Technical notes
- Implementation: primarily Python, with examples and extension points.
- Extensible metrics: supports pluggable evaluators and LLM-based scorers (AspectCritic).
- Deployment: provides CLI and library APIs suitable for local installs and CI integration.