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. Unlike general-purpose evaluation tools like PromptFoo or BrainTrust that focus broadly on LLM evaluation, RAGAS specializes exclusively in the unique challenges of retrieval-augmented systems.

Where tools like LangSmith provide general conversation evaluation, RAGAS offers four RAG-specific metrics that directly correlate with real-world performance: 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. This specialization provides far more actionable insights than generic quality scores.