Ragas
Verified Oct 2, 2026 ActiveOpen-source Python toolkit that scores RAG and agent apps with LLM-based metrics and generates synthetic test sets.
Listed on EffectHub ·
Pricing
- Pricing model
- 100% Free
The library is free under Apache-2.0 and installs with pip. You pay your own LLM provider for the judge calls the metrics make. The company mentions enterprise features by contact only, with no price published.
Use tool ↗About Ragas
Ragas is a Python library for evaluating applications built on retrieval-augmented generation and agents. It ships metrics such as faithfulness, answer relevancy, context precision and context recall, computed by an LLM judge or by classic measures, and can generate test datasets from your own documents when you have none. It plugs into LangChain, LlamaIndex and common observability tools, so scores can follow a pipeline from development into production. The project is Apache-2.0 licensed and maintained by Vibrant Labs, which also offers enterprise features on request.
- API
- No
- Last verified
- Oct 2, 2026
Verified sources
- Vendor site: open-source RAG evaluation with metrics, synthetic data and online monitoring; enterprise by contact; founders and companyragas.io ↗
- GitHub repository: Apache-2.0 licence, created May 2023, homepage docs.ragas.iogithub.com ↗
- Vibrant Labs is a Y Combinator W24 company based in San Franciscoycombinator.com ↗
Is Ragas legit?
We don’t label tools safe or unsafe. Below are the records we checked ourselves, each with the date we read it.
- Last checked
- Oct 2, 2026The site answered
- On the market since
- 2023The vendor's own material, read by us
- Operated by
- Vibrant Labs, United StatesOur research, card verified Oct 2, 2026
Pros & cons of Ragas
Ragas is a common first choice for putting numbers on a RAG pipeline: it is free, has the usual metric names everyone recognises, and drops into existing Python code. It fits engineers who want repeatable scores in CI or notebooks. Expect to spend on judge-model calls, and treat LLM-judged scores as indicators, not ground truth.
Pros
- Free Apache-2.0 library with a large community
- Metrics cover faithfulness, relevancy and context precision and recall
- Synthetic test-set generation helps when no labelled data exists
- Integrates with LangChain, LlamaIndex and observability tools
Cons
- Metric scores come from an LLM judge, so they cost tokens and vary between judge models
- No published price for the commercial online monitoring side
- Library APIs have changed across minor versions, so pin the version you tested with
Best for
- Engineers measuring retrieval and answer quality of a RAG pipeline before release
- Teams that need synthetic test questions built from their own documents
Not for
- Non-developers who want a point-and-click evaluation dashboard
- Teams that need a fully hosted tracing and monitoring service with a published price
Watch out: Each metric run calls an LLM, so scoring a large test set can cost real money on your provider bill, and the GitHub repository now lives under the vibrantlabsai organization rather than the older explodinggradients one.