How to compare LLM evaluation frameworks without getting sold to
Every evaluation framework claims to measure quality. The differences show up in what they let you measure, who can write the test, and what happens when a score drops.
Evaluation is the part of building with language models that everyone agrees is important and most teams do badly. The tooling has matured, but comparing frameworks is hard because their marketing converges on the same words: observability, quality, guardrails. This guide lays out the criteria that actually separate them, so you can score candidates yourself.
Start with what you are evaluating
There are three distinct jobs, and a framework can be good at one and poor at the others.
- Offline evaluation of prompts and models against a fixed dataset, before anything ships.
- Online evaluation of production traffic, sampling real requests and scoring them after the fact.
- Agent evaluation, where the unit is a multi-step trajectory with tool calls rather than a single response.
Decide which of these you need in the next six months. A framework built for single-turn offline tests will not stretch to agents without a rewrite.
The seven criteria
| Criterion | Why it matters | Question to ask |
|---|---|---|
| Evaluator types | Exact match, model-graded, human, and code-based checks each catch different failures. | Can I write a custom evaluator in my own language? |
| Dataset management | Test sets drift and grow; versioning them is how you compare runs. | Can I diff two dataset versions? |
| Trace depth | Agent failures live in the middle of a trajectory, not the final answer. | Does a trace show every tool call and its inputs? |
| Human review | Some judgments need a person; the workflow determines whether anyone does it. | Can a non-engineer score a queue of outputs? |
| CI integration | An evaluation that does not block a bad deploy is a dashboard. | Can a score regression fail a pull request? |
| Production sampling | Offline sets miss the long tail of real usage. | Can I sample live traffic into a dataset? |
| Cost and latency reporting | Quality is one axis; the other two decide what ships. | Are tokens and latency recorded next to scores? |
How to run the comparison
Take one workflow you actually run, ideally one that has broken before. Build the same evaluation in each candidate. Time how long it took, count how many features you had to work around, and note who on the team could operate it without help. This takes a day per tool and is worth more than any demo.
A framework you can run in CI beats a prettier one you check by hand.
Red flags
Be cautious of frameworks that score only with a model grader and offer no way to calibrate it against human labels. Be cautious of anything that stores traces but cannot turn a trace into a test case. And be cautious of a vendor who cannot show a score going down; the whole point is catching regressions, and a tool that has never displayed one has not been used seriously.
If you need a wider view of the space, our surveys desk maintains a map of the current landscape.