DeepEval
Evals as pytest tests, with the research metrics already implemented.
92
sovereigntyDeepEval brings LLM evaluation into pytest, so an eval is a test function and your existing test runner, CI integration and reporting all work unchanged. It ships implementations of the metrics people actually cite — answer relevancy, faithfulness, contextual precision and recall, hallucination, bias, toxicity — including several LLM-as-judge metrics done carefully. Apache-2.0, from Confident AI, who sell an optional hosted platform.
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST
LicenseApache-2.0
PricingFree and Apache-2.0; optional paid cloud dashboard.
Open sourceYes
Self-hostableYes
Local-first dataYes
What it does well
- +Pytest-native — your existing CI and reporting just work
- +Large library of implemented, research-backed metrics
- +Synthetic test-case generation for cold-start coverage
- +Apache-2.0 with no seat cost
Where it falls short
- −LLM-as-judge metrics cost tokens on every run
- −Assumes a Python codebase
- −Best dashboard experience is the paid hosted one
DeepEval as an alternative to
Where DeepEval shows up in our comparisons, and how it ranked.
DeepEval head-to-head
Straight comparisons against the tools people weigh it against.