RAGAS Review 2026

Open-source framework for evaluating RAG pipelines and AI agents with automated metrics for faithfulness, relevancy, and context quality.

RAGAS (Retrieval Augmented Generation Assessment) is an open-source evaluation framework specifically designed for assessing the quality of RAG (Retrieval Augmented Generation) pipelines and AI agents that rely on retrieved context. As RAG becomes the dominant pattern for building knowledge-grounded agents, RAGAS provides the metrics and methodology to systematically measure whether agents are retrieving the right information and generating faithful, relevant responses.

The framework provides automated metrics that evaluate different aspects of RAG quality: Faithfulness measures whether the generated answer is factually consistent with the retrieved context. Answer Relevancy evaluates whether the response actually addresses the user's question. Context Precision assesses whether the retrieved documents are relevant to the query. Context Recall measures whether all necessary information was retrieved.