RAGAS
An open-source framework for evaluating retrieval-augmented generation with reference-free RAG metrics.
RAGAS is an open-source framework built specifically to evaluate RAG pipelines. Its metrics — faithfulness, answer relevancy, context precision, and context recall — pinpoint whether failures come from retrieval or generation, many of them reference-free so you can score without gold answers.
RAGAS is an open-source framework purpose-built for evaluating retrieval-augmented generation. Generic LLM metrics tell you an answer was bad; RAGAS tells you why — whether the retrieval half failed (the right context wasn't fetched) or the generation half did (the model ignored or contradicted the context it was given). That split is exactly the diagnosis a RAG team needs.
It is aimed at engineers building RAG who want metrics tuned to the pipeline rather than to generic chat. Many of its metrics are reference-free, meaning they can score outputs without a hand-written gold answer for every case — which makes building an eval set far cheaper.
Highlights
- Faithfulness — is the answer actually supported by the retrieved context (the core hallucination check)?
- Answer relevancy — does the answer address the question?
- Context precision / recall — did retrieval surface the right passages, and rank them well?
- Reference-free options — score many metrics without gold answers, lowering the cost of an eval set.
- Integrations — works with common LLM/orchestration stacks and observability tools.
In an AI-assisted workflow
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision
scores = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_precision])TIP
Read the metrics as a diagnosis: low context precision/recall means fix retrieval (Hybrid Search & Reranking); high context scores but low faithfulness means fix generation (grounding and citations).
Good to know
RAGAS is free and open source (Apache-2.0); its metrics call an LLM as judge, so expect token cost when you run a suite. Use it alongside a general framework like DeepEval if you also need non-RAG metrics, and see How RAG Actually Works for where each metric maps onto the pipeline.
Related
- DeepEvalAn open-source evaluation framework for LLM apps — 'Pytest for LLMs' with ready-made metrics and CI integration.
- Best LLM & RAG Evaluation Tools in 2026: DeepEval vs RAGAS vs LangSmith vs Phoenix vs promptfooA decision guide to the LLM eval landscape — code-first frameworks vs. eval-and-observability platforms, open-source vs. hosted, and which fits your stack.
- How RAG Actually Works: Ingestion, Chunking, Retrieval & RerankingA clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together.
- Rag Pipeline EngineerUse this agent to design, build, and harden a production retrieval-augmented generation (RAG) pipeline end to end — ingestion, chunking, embeddings, indexing, retrieval, reranking, and grounded generation — with evals that prove each stage works. Examples — "stand up RAG over our docs", "our RAG hallucinates and misses obvious answers, fix the pipeline", "take our prototype RAG to production with evals and citations".
- LangfuseAn open-source LLM engineering platform for tracing, evals, prompt management, and metrics.