Ragas

What it does

Ragas is an Apache-2.0 Python library centered on RAG evaluation. It separates retrieval and generation quality with metrics such as context precision, context recall, faithfulness, and answer relevance, while also supporting agent and traditional text metrics.

• Context precision, context recall, faithfulness, answer relevance, and noise-sensitivity metrics
• Agent goal, topic, and tool-call metrics plus exact-match and text-similarity metrics
• Custom metrics, synthetic test-set generation, experiments, and framework integrations

Where it fits

Evaluating retrieval quality, grounded generation, agent tool use, and production-aligned test data for RAG applications.

Use it after defining the behavior that matters, collecting representative cases, and deciding how each case will be graded. A tool can execute and organize an eval, but it cannot decide which failures are costly for your users.

Limitations and cautions

• Many semantic metrics call a judge model, making reproducibility and cost dependent on that model and prompt.
• Ragas is primarily a library, not a complete production observability, red-team, or annotation platform.

How to evaluate the evaluator

Before using any score as a release gate, create a small evaluator-validation set that humans have labeled. Compare false passes, false failures, disagreement by failure type, run-to-run variance, latency, and cost. Keep deterministic checks for verifiable facts, calibrate model judges against domain experts, and review raw traces when aggregate scores move.