Ragas

Ragas is an open-source framework for evaluating retrieval-augmented generation systems, it provides a suite of metrics that measure the quality of every component in a RAG pipeline without requiring human-labeled ground truth, making systematic RAG evaluation practical for teams that can't afford large-scale human annotation. The core metrics assess Faithfulness (does the generated answer contradict information in the retrieved context?), Answer Relevancy (does the response directly address the question?), Context Precision (are the retrieved chunks relevant to the question?), and Context Recall (was all the relevant information retrieved?). These reference-free metrics use LLMs as judges rather than human annotators, enabling automated evaluation at scale during development and production monitoring. The evaluation pipeline accepts a dataset of questions, generated answers, and retrieved contexts, then computes each metric for the full dataset and returns aggregate scores and per-sample breakdowns.

This enables systematic comparison of RAG configurations, different chunking strategies, embedding models, retrieval parameters, or LLMs, with objective quality metrics rather than subjective impressions. Ragas integrates natively with LangChain and LlamaIndex to evaluate their built-in RAG chains. The framework is available as a Python package. A hosted Ragas Cloud platform provides dashboards for tracking evaluation results across experiments and production monitoring over time.

Commonly used by teams iteratively improving RAG systems who need a repeatable quality signal to guide optimization decisions.