LLM Evaluation Frameworks: HELM vs lm-eval-harness vs OpenAI Evals vs Braintrust vs Ragas
You cannot ship production GenAI without an evaluation harness. The frameworks vary widely — from research-scale multi-task suites (HELM) to production-focused hosted platforms (Braintrust) to RAG-specific tooling (Ragas). Most enterprises end up combining two: an OSS backbone plus a hosted platform for team workflow.
Last reviewed:
What we're comparing
- HELM
Stanford CRFM's Holistic Evaluation of Language Models — broad multi-task benchmark suite (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency).
- lm-eval-harness
EleutherAI's open-source LLM evaluation library — the reference implementation for many published benchmarks. Widely used in research.
- OpenAI Evals
OpenAI-authored open-source framework for defining and running LLM evals. Structured around templates for common eval patterns.
- Braintrust
Hosted LLM evaluation platform with dataset management, LLM-as-judge, and team workflow. Popular in enterprise production ops.
- Ragas
Open-source RAG-specific evaluation library — faithfulness, answer relevance, context recall/precision, and more.
Side-by-side
| Attribute | HELM | lm-eval-harness | OpenAI Evals | Braintrust | Ragas |
|---|---|---|---|---|---|
| Coverage | Broad multi-task | Broad; extensive benchmark library | Templated common patterns | Custom evals (yours + LLM-as-judge) | RAG-specific metrics |
| Deployment | OSS self-run | OSS self-run | OSS self-run | SaaS + self-host tier | OSS self-run |
| Team workflow | Research-scale batch runs | Batch runs, minimal team features | Basic | Best-in-class — dataset versioning, review workflow, prod trace linking | Basic |
| LLM-as-judge support | Native | Extensible | Native | Native + rubric templating | Native (RAG-focused rubrics) |
| CI integration | Custom | Custom | Custom | Native CI hooks | Custom |
| Best-fit use case | Research; comparing base models across many capabilities | Reproducing published benchmark results; broad model evaluation | Fast start on common eval patterns; team without deep eval infra | Production ops for team-scale eval workflow; CI-gated evals | RAG-specific quality tracking |
When to use which
- Use HELM or lm-eval-harness
You are evaluating base models against a wide capability set for a selection decision, or you need to reproduce published benchmark numbers.
- Use OpenAI Evals
You are getting started on custom evals and want a lightweight OSS scaffold with templates.
- Use Braintrust
You are running LLM evaluation as a team discipline with CI gates, dataset governance, and production-trace linkage. Pay for the workflow.
- Use Ragas
You are evaluating a RAG system specifically and want out-of-the-box faithfulness / groundedness / context metrics.
FAQs
Should we use one framework or combine them?
Combine. Typical mature setup: lm-eval-harness or HELM for base-model comparison at selection time, Ragas for RAG-specific metrics, Braintrust or OpenAI Evals for production eval workflow and CI gates. Each addresses a different layer.
What is CI-gated evaluation?
A change to model version, prompt scaffold, or tool set cannot ship to production without passing a defined threshold on the evaluation harness. Analogous to CI-gated tests for code. Braintrust supports this natively; other frameworks need custom CI plumbing.
Do we need to build our own golden dataset?
Yes for production ops. Public benchmarks tell you about general capability; only a domain-specific golden dataset tells you about performance on your workload. Grow the set from real production incidents — every P1/P2 postmortem should add at least one entry.