Ragas.

TL;DR — Ragas is a specialized evaluation toolkit for RAG pipelines, focused on retrieval quality and answer grounding.

What it is

Ragas computes metrics like faithfulness, context precision, answer relevancy, and context recall over RAG datasets.

Why it exists

RAG failures often come from retrieval quality, not only generation quality. Ragas gives retrieval-aware metrics to catch that.

Install

pip install ragas

Basic usage

from ragas import evaluate
# prepare question, context, answer dataset
# run evaluate(dataset, metrics=[...])

When to use, when to skip

Use it when this category is a bottleneck in your agent stack and you want faster delivery with fewer custom components.

Skip it when your workload is tiny, requirements are fixed, or a plain provider SDK plus a few local functions is enough.

Alternatives

Compare with adjacent tools in the same AI Native category and choose based on interface style, deployment model (hosted vs self-hosted), and team familiarity.

Verified against project documentation, June 2026.