What it is
RAGAS is an open-source framework for evaluating RAG (retrieval-augmented generation) pipelines. The defining contribution is reference-free evaluation: instead of needing a hand-written "right answer" for every test case, RAGAS uses LLM-as-judge to assess faithfulness (is the answer grounded in retrieved context?), context precision/recall (is retrieval working?), and answer relevancy.
Free, open source, Apache 2.0.
Where it shines
- Standardization. "Faithfulness," "context precision," "context recall" — these are now the words the entire RAG eval industry uses. RAGAS is the reason.
- Reference-free. Skipping the hand-annotation step is a real productivity win, especially in early RAG development.
- Composability. Drops into Braintrust, Langfuse, and other platforms as a metric source.
Where it falls short
- Not a platform. You're getting a Python library, not a product. UI, storage, dashboards — that's all on you (or your platform of choice).
- NaN failure mode. When the LLM judge returns malformed JSON, you get NaN scores with no graceful fallback. Real annoyance at scale.
- Coverage scope. Excellent for RAG. Doesn't try to do agents or non-retrieval use cases.
Bottom line
If your evaluation problem is a RAG pipeline, RAGAS is the default — use it directly or use it via the platform that wraps it. For non-RAG work, look elsewhere; this isn't designed for that.