Choosing an LLM Evaluation Framework

An LLM evaluation framework is the tooling that runs your test cases against a model or pipeline, scores the outputs, and tracks results over time. The options split into four jobs, and most teams need one or two of them, not all four.

This page maps the well-known options by job. Descriptions are a starting point; projects move fast, so check current docs before committing. If you'd rather build a lean suite yourself, the eval-suite-in-a-day recipe shows how.

Benchmark harnesses

• lm-evaluation-harness (EleutherAI): runs many standard benchmarks and custom tasks against models.
• HELM (Stanford): holistic evaluation across many scenarios and metrics.
• Inspect (the UK AI Security Institute, with Meridian Labs): an open-source framework for building and running evaluations, including agent tasks.

Built for comparing models. Less suited to catching regressions in your own app.

Test runners for prompts and apps

• promptfoo: config-driven testing of prompts and models from the command line, CI-friendly.
• DeepEval: unit-test-style assertions for LLM outputs, in a pytest-like workflow.
• OpenAI Evals: OpenAI's open-source evaluation framework and registry.

Built for regression tests: run the suite on every prompt or model change and fail the build on regressions. The practices behind them live in LLM testing.

RAG evaluators

• Ragas: metrics for retrieval-augmented generation, including faithfulness and context precision and recall.

Built for pipelines that answer from documents. The method is covered in RAG evaluation.

Evaluation platforms

Hosted and open-source platforms that combine datasets, runs, human review, and production tracing overlap heavily with observability tools. The observability tools comparison covers them by type.

How to choose, and when to build your own

Four questions decide it:

1. Comparing models, or protecting an app from regressions? Harness for the first, test runner for the second.
2. Does it need to run in CI? Favor config-driven runners.
3. Where must test data live? Hosted platforms may be ruled out.
4. Do people need to review outputs? Then you want a platform's review interface, not a script.

Build your own when your checks are closed questions, your gold set is a few hundred items, and you need versioned judges. A script, a labeled set, and a battery of yes/no questions covers it, and frameworks add less than they cost at that size.

Whatever you pick, two habits carry over: version the judge questions (question versioning) and build the test set from real traffic and real failures (gold set method).

Frequently asked questions

What's the most popular LLM evaluation framework?

Popularity shifts month to month. Choose by the job you need done, not by stars.

Can I use several evaluation frameworks together?

Yes, and many teams do: a test runner in CI plus a platform for human review.

Do evaluation frameworks support LLM-as-judge scoring?

Most do. You still own the judge's calibration, since the framework only runs it. More in the LLM evals guide.

Is building my own evaluation framework a mistake?

Not for small, closed-question suites. It becomes one when you start rebuilding dashboards and review tools that platforms already do well.

How big should my evaluation set be?

Start with 100 to 200 real cases and add every failure you find in production. See what Jev is for the judge side.