# vibrantlabsai/ragas

Supercharge Your LLM Application Evaluations 🚀

Repository: https://github.com/vibrantlabsai/ragas
Canonical: https://ross.abutalabs.com/products/ragas
Homepage: https://docs.ragas.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: llm, llmops, evaluation
Last push: 2026-02-24T07:47:19+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 69, release rhythm 65, longevity 86
- inputs: {"age_days": 1213, "days_push": 190, "days_rel": 232, "gap_med": 11, "n_releases_24m": 36}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15485, forks 1656 (observed 2026-08-28T04:11:09.918673+00:00)

## What it is
Ragas is a Python library for evaluating LLM applications with objective, LLM-driven metrics and systematic experiment loops. It also generates synthetic test datasets and integrates with frameworks like LangChain, LlamaIndex, and observability tools such as LangSmith and Arize Phoenix.

## Use cases
- evaluate rag pipeline quality with metrics like faithfulness and answer relevance
- generate synthetic test datasets for llm application evaluation
- build continuous evaluation loops for chatbots and ai agents
- benchmark prompt or model changes against a baseline
- integrate llm evals into langchain or llamaindex workflows
- trace and log evaluation results to langsmith or arize phoenix
- evaluate amazon bedrock agents

## When to choose
- you need systematic, repeatable evaluation of rag or agent applications instead of manual vibe checks
- you lack a golden test dataset and want automated test set generation
- you use langchain, llamaindex, or bedrock and want drop-in eval integration
- you want custom llm-based metrics defined with simple decorators

## When to avoid
- you need deterministic unit testing of non-LLM code - use pytest instead
- you want a hosted eval dashboard with no code - this is a developer library
- your evaluation must run fully offline without any LLM judge, since many metrics require an evaluator LLM

## Facets
- artifact type: library
- maturity: active
- function: testing, benchmarking, machine-learning, rag, llm-inference, data-generation, analytics
- domain: large-language-models, machine-learning, developer-tools, artificial-intelligence
- platform: python, cross-platform
- tags: llm-evaluation, evals, rag-evaluation, test-set-generation, llmops, langchain, llamaindex, retrieval-augmented-generation

## Member repositories
- vibrantlabsai/ragas (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:09.918673+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:05:50.040955+00:00, confidence not recorded.
  - readme: https://github.com/vibrantlabsai/ragas (fetched 2026-08-28T04:11:09.918673+00:00, sha 147212e94120)
  - homepage: https://docs.ragas.io (fetched 2026-08-29T08:04:11.250802+00:00, sha 9fbc0dd94657)
  - site_page: https://docs.ragas.io/en/stable/getstarted/install (fetched 2026-08-29T08:04:11.253444+00:00, sha a76ee58b7d57)
  - site_page: https://docs.ragas.io/en/stable/getstarted/quickstart (fetched 2026-08-29T08:04:11.255303+00:00, sha b99bf3251443)
  - site_page: https://docs.ragas.io/en/stable/howtos/integrations (fetched 2026-08-29T08:04:11.257497+00:00, sha ed6c980af8c9)
  - site_page: https://docs.ragas.io/en/stable/howtos/integrations/_arize (fetched 2026-08-29T08:04:11.259601+00:00, sha cac922094751)
  - site_page: https://docs.ragas.io/en/stable/howtos/integrations/langsmith (fetched 2026-08-29T08:04:11.261706+00:00, sha 1df848a7adc0)
  - site_page: https://docs.ragas.io/en/stable/howtos/integrations/amazon_bedrock (fetched 2026-08-29T08:04:11.263480+00:00, sha 6500d2041a4b)
  - site_page: https://docs.ragas.io/en/stable/howtos/integrations/gemini (fetched 2026-08-29T08:04:11.266097+00:00, sha 1ba43e0153d2)
  - site_page: https://docs.ragas.io/en/stable/howtos/integrations/oci_genai (fetched 2026-08-29T08:04:11.267989+00:00, sha f1794d81a8c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.