Ragas
Purpose-built RAG evaluation — measures retrieval and generation separately.
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sovereigntyRagas evaluates RAG systems specifically, and its central insight is that retrieval failure and generation failure need separate measurement. Context precision and recall tell you whether the retriever found the right material; faithfulness and answer relevancy tell you whether the model used it honestly. Without that separation you know the answer was wrong but not which half to fix. Apache-2.0, and it composes with LlamaIndex and Haystack directly.
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST
LicenseApache-2.0
PricingFree, Apache-2.0.
Open sourceYes
Self-hostableYes
Local-first dataYes
What it does well
- +Separates retrieval quality from generation quality
- +Can generate a synthetic test set from your own documents
- +Integrates directly with LlamaIndex and Haystack
- +Apache-2.0
Where it falls short
- −RAG-specific — not a general evaluation framework
- −Judge-model calls make large suites cost real money
- −Metric scores need calibration before you trust them
Ragas as an alternative to
Where Ragas shows up in our comparisons, and how it ranked.
Ragas head-to-head
Straight comparisons against the tools people weigh it against.