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RAGAS measures four key aspects of RAG quality: Faithfulness (factual consistency), Answer Relevancy (addressing the question), Context Precision (retrieval relevance), and Context Recall (retrieval completeness).
Yes. RAGAS works with any RAG implementation. You just need to provide the question, answer, contexts, and ground truth in the expected format.
RAGAS itself is free, but metrics use LLM calls for evaluation. Costs depend on your evaluator model and dataset size — typically a few dollars for hundreds of test cases.
RAGAS primarily evaluates single-turn RAG quality. For multi-turn agent evaluation, combine RAGAS with conversation-level metrics or use complementary tools like DeepEval.
Compare features, test the interface, and see if it fits your workflow.