Ragas vs OpenAI Evals
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as OpenAI Evals vs Ragas — same comparison, one verdict.
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.
OpenAI Evals
The original benchmark harness. Simple, standard, still useful.
OpenAI Evals is the framework and registry that popularised systematic LLM evaluation. It provides a standard harness for running benchmark-style evaluations and a public registry of existing ones, so comparing against a known suite is a command rather than a project. It is less actively developed than the others here and less suited to application-level regression testing, but for running or extending a standard benchmark it remains the shortest path. MIT licensed.
Side by side
| Ragas | OpenAI Evals | |
|---|---|---|
| Sovereignty Score | 92 | 85 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | MIT |
| Pricing | Free, Apache-2.0. | Free, MIT. You pay for whatever model API the evals call. |
Ragas edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
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
OpenAI Evals
Strengths
- +Large registry of existing benchmark evaluations
- +Simple, well-understood format that many teams already know
- +Straightforward to extend with your own cases
- +MIT licensed
Trade-offs
- −Development pace has slowed relative to the alternatives
- −Oriented to benchmarks, not application regression testing
- −Weaker RAG and agent evaluation support than the others
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.