Ragas is the leading open-source framework for evaluating large language model applications, especially retrieval-augmented generation (RAG) systems. It has collected more than 16,000 stars on GitHub and was actively maintained in 2026.
Instead of eyeballing chatbot answers, Ragas gives teams objective, repeatable measurements of both halves of a RAG pipeline: whether the retriever finds the right context, and whether the generator uses that context faithfully. Its reference-free metrics work without hand-labeled ground truth, and its synthetic test-data generation can bootstrap an evaluation set from your own documents, so you can start measuring quality from day one and keep regression-testing every prompt or model change.
Ragas covers the modern LLM evaluation toolbox beyond classic RAG metrics as well: factual correctness and answer-correctness checks, semantic similarity, aspect-based criticism and rubric-driven scoring, plus newer agent-oriented evaluations. Its test-set generation builds a knowledge graph over your documents and evolves questions of increasing difficulty โ simple, reasoning and multi-context โ so the evaluation set grows with your corpus instead of going stale. Results plug into experiment tracking and observability workflows, which is why Ragas shows up in the CI pipelines of teams that ship LLM features continuously and cannot afford silent quality regressions.
Ragas is free and open source under the Apache-2.0 license. Evaluation runs call the LLM you configure for judging, so your only cost is that model usage.
RAG developers, LLM application teams and evaluation engineers who need measurable, repeatable quality checks instead of vibes-based testing.