Ragas

Evaluating RAG pipelines without hand-labeled ground truth is the problem Ragas set out to solve, introduced through an EACL 2024 demo paper, and it has since become the default open-source evaluation library for LLM applications. Metrics such as faithfulness, answer relevancy, context precision, and context recall score systems using LLM-as-judge techniques alongside traditional non-LLM measures, while automated test set generation synthesizes diverse question and answer pairs from a document corpus so teams can bootstrap evaluation before production traffic exists. Scores feed CI pipelines and observability feedback loops, and integrations cover LangChain and LlamaIndex, with AWS courseware teaching it as standard practice. Installation is pip install ragas on plain Python, with any supported LLM acting as judge, including local models. Originally published under the explodinggradients organization and now maintained by VibrantLabs, the Apache-2.0 project holds about 15,000 GitHub stars alongside an active Discord community.