# Ragas

Open-source evaluation framework for LLM applications and RAG workflows.

## Summary
Ragas is an Apache-2.0 evaluation framework for LLM applications, especially retrieval-augmented generation workflows that need structured quality checks.


## Guide
Ragas is an Apache-2.0 evaluation framework for LLM applications, especially retrieval-augmented generation workflows that need structured quality checks.

### What it is
Ragas is listed on OpenAgent.bot as a tools resource for open AI builders.

### Why it matters
Many agent products use retrieval or long-context workflows. Ragas gives builders a practical evaluation layer for checking answers, context, retrieval quality, and application behavior.

### How it works
Start from the official source links, then validate the project against your deployment needs, license requirements, and maintenance expectations.


### Getting Started
- Review the repository: https://github.com/vibrantlabsai/ragas
- Homepage: https://docs.ragas.io
## Why It Matters
Many agent products use retrieval or long-context workflows. Ragas gives builders a practical evaluation layer for checking answers, context, retrieval quality, and application behavior.


## Best For
- Teams evaluating RAG and LLM applications
- Developers building repeatable quality checks for retrieval workflows
- Builders comparing evaluation frameworks around agent knowledge systems

## Not For
- Pure browser automation tests
- Teams that need production tracing more than evaluation datasets

## What It Actually Does
- Rag: Ragas surfaces rag as a core capability in its published project metadata and source links.
  - Why it matters: This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.

## Typical Use Cases
- Self hosted ai: Use it as a candidate for self hosted ai when the project facts, license, and official links match your deployment requirements.

## How It Compares
- When to choose Ragas: Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.

## Command Line
### Install or run
```bash
pip install ragas
```

## Facts
- Category: tools
- Resource type: tool
- Open source: yes
- License: Apache-2.0
- Last verified: 2026-06-02
- GitHub repo: vibrantlabsai/ragas
- GitHub stars: 14187

## Capabilities
- rag

## Structured Use Case Tags
- self-hosted-ai

## Getting Started
- Review the repository: https://github.com/vibrantlabsai/ragas
- Homepage: https://docs.ragas.io

## Links
- GitHub: https://github.com/vibrantlabsai/ragas
- Homepage: https://docs.ragas.io

## Structured Outputs
- JSON: https://www.openagent.bot/tools/ragas.json
- Markdown: https://www.openagent.bot/tools/ragas.md
- Canonical: https://www.openagent.bot/tools/ragas