Ragas
Widely adopted open-source framework for evaluating RAG pipelines with metrics like faithfulness and context precision.
Overview
Ragas is an open-source (Apache-2.0) evaluation framework for retrieval-augmented generation, built by Exploding Gradients. It pioneered a four-metric pattern — faithfulness, answer relevancy, context precision, and context recall — that has become a standard starting point for testing RAG applications, and it also supports synthetic test-data generation. With roughly 15K GitHub stars, it integrates with LangChain, LlamaIndex, and Haystack. A hosted platform is available for teams that want dashboards and collaboration on top of the free library.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 24 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
ASEAN Perspective
Ragas in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
What this is for: Measuring the quality of RAG pipelines with faithfulness, relevancy, and context metrics, plus synthetic test-set generation.
Who this is for: Developers and ML teams who need to quantify and regression-test retrieval and generation quality.
Availability: Free open-source library (Apache-2.0) with an optional paid hosted platform; integrates with LangChain, LlamaIndex, and Haystack.
What people say
Ragas became the default first stop for RAG evaluation almost by convention — its four-metric pattern (faithfulness, answer relevancy, context precision, context recall) is now the reflexive starting point people reach for, with ~15K GitHub stars and integrations into LangChain, LlamaIndex, and Haystack. Its synthetic test-set generation is a frequently cited convenience.
The sharpest caveats, and they matter, come from the research literature. One methodological study found correlation between Ragas metrics and human evaluation with a harmonic mean of only ~0.55 — well below what reliable automated scoring would require. Its heuristic scoring prompts often fail to adapt to new domains or corpora; it generates test queries directly from documents rather than from realistic user behavior, so test sets measure what the corpus can say, not what users actually ask; and it won't flag irrelevant, duplicate, or out-of-scope documents, letting a noisy index still score well. All of it inherits well-documented LLM-judge biases (position, verbosity, self-enhancement).
Its popularity is deserved for its utility in fast, directional, regression-style checks, but the scores themselves should not be mistaken for ground truth. Calibrate against human judgment before trusting them for high-stakes decisions.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Ragas's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Ragas unless explicitly stated.
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