# Ragas

**Canonical:** https://apis.io/providers/ragas-ai/  
**Website:** https://www.ragas.io/  
**APIs profiled:** 1

Ragas is an open-source evaluation toolkit for Large Language Model applications, with particular depth on Retrieval Augmented Generation (RAG) and agentic systems. Originally created under the Exploding Gradients organization on GitHub and now maintained by Vibrant Labs AI, Ragas is a Python library distributed on PyPI under the Apache 2.0 license. It moves teams from informal "vibe checks" to systematic evaluation loops by providing objective LLM-based and traditional metrics, automated test dataset generation, experiment tracking, and integrations with the broader LLM ecosystem including LangChain, LlamaIndex, OpenAI, Anthropic, and popular observability platforms. Ragas exposes a metrics library covering faithfulness, response relevancy, context precision and recall, factual correctness, semantic similarity, agent tool-use accuracy, SQL equivalence, Nvidia-defined RAG metrics, and general-purpose rubric scoring. The project ships a CLI (`ragas`) with quickstart templates such as `rag_eval`, and is consumed primarily as a `pip install ragas` library rather than as a hosted API service. Ragas is widely cited as a default evaluation harness for RAG applications and has grown a substantial community on GitHub and Discord.

## Kin Score — 13.6 / 100 (emerging)

Scored 2026-08-25 under rubric 0.14.0. Trend: flat (+0.0 from 13.6).

| Facet | Score |
|---|---|
| Discoverability | 59.3 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 18.4 |
| Developer Ergonomics | 26.2 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | no |
| Protected Resource Metadata | no |
| Dynamic Client Registration | no |
| Agentic Commerce | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## APIs (1)

- **Ragas Python Library** — The Ragas Python library is the primary surface of the project, installed via `pip install ragas` and imported as `ragas`. It exposes evaluation entry points (`ragas.evaluate`),...

## Security (1)

- **Ragas Ai Domain Security** — TLSv1.3 · HSTS · DMARC

## Use cases (6)

- **RAG Pipeline Evaluation** — Scoring retrieval and generation quality in RAG applications across faithfulness, relevance, and context fidelity.
- **Agent Evaluation** — Measuring tool-call correctness, goal completion, and topic adherence in multi-step LLM agents.
- **Regression Testing in CI** — Running Ragas metrics in CI pipelines to detect quality regressions across prompt, model, and configuration changes.
- **Model and Prompt Selection** — Comparing candidate models and prompt variants on a fixed dataset using Ragas experiments.
- **Synthetic Test Set Generation** — Generating diverse evaluation datasets from a knowledge base for systematic LLM testing.
- **Text-to-SQL Evaluation** — Validating generated SQL against reference queries using execution and structural equivalence metrics.

## Tags

LLM Evaluation, RAG Evaluation, Retrieval Augmented Generation, AI Evaluation, Open-Source, Python, Metrics, Test Data Generation, Agent Evaluation, LLM Tooling

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/ragas-ai/). Scores are computed from the provider's own public artifacts under a published rubric.