Inspect AI vs Ragas
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as Ragas vs Inspect AI — same comparison, one verdict.
Inspect AI
The rigorous one, from the UK AI Safety Institute.
Inspect is the evaluation framework built by the UK AI Safety Institute for evaluating frontier models, released MIT. It is the most methodologically serious option here: first-class support for multi-turn agent evaluations, tool use, sandboxed execution and human grading, with a design that takes statistical validity seriously rather than producing a number that feels reassuring. If your evaluations need to withstand scrutiny — regulatory, academic or internal — this is the one.
Ragas
Purpose-built RAG evaluation — measures retrieval and generation separately.
Ragas 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.
Side by side
| Inspect AI | Ragas | |
|---|---|---|
| Sovereignty Score | 93 | 92 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free, MIT, publicly funded. | Free, Apache-2.0. |
Inspect AI edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Inspect AI
Strengths
- +Built for evaluations that have to survive real scrutiny
- +Strong agent, tool-use and sandboxed-execution support
- +Excellent log viewer for inspecting individual samples
- +MIT, from a public institute with no commercial upsell
Trade-offs
- −Aimed at model evaluation more than application regression testing
- −Steeper learning curve than Promptfoo for simple cases
- −Less oriented toward CI gating out of the box
Ragas
Strengths
- +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
Trade-offs
- −RAG-specific — not a general evaluation framework
- −Judge-model calls make large suites cost real money
- −Metric scores need calibration before you trust them
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.