Evals

Ragas

Ragas is an open-source evaluation library focused on retrieval-augmented generation, described in its own docs as "a library that helps you move from 'vibe checks' to systematic evaluation loops for your AI applications." It exposes LLM-driven metrics for RAG (faithfulness, context recall, answer relevancy), integrates with LangChain and LlamaIndex, and supports custom metric authoring as a complement to other eval platforms (Weave, LangSmith).

GitHub

Documentation

📖
Documentation
https://docs.ragas.io/en/stable/

SDKs

📦
GitHubRepository
https://github.com/explodinggradients/ragas

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API entry from apis.yml

apis.yml Raw ↑ 
name: Ragas
description: Ragas is an open-source evaluation library focused on retrieval-augmented generation, described
  in its own docs as "a library that helps you move from 'vibe checks' to systematic evaluation loops
  for your AI applications." It exposes LLM-driven metrics for RAG (faithfulness, context recall, answer
  relevancy), integrates with LangChain and LlamaIndex, and supports custom metric authoring as a complement
  to other eval platforms (Weave, LangSmith).
humanURL: https://docs.ragas.io/
baseURL: https://docs.ragas.io
tags:
- Open-Source
- RAG
- Faithfulness
- Context Recall
- Library
tags_raw:
- Open Source
- RAG
- Faithfulness
- Context Recall
- Library
properties:
- type: Documentation
  url: https://docs.ragas.io/en/stable/
- type: GitHubRepository
  url: https://github.com/explodinggradients/ragas