Nightjar vs.
DeepEval
LLM output evaluation vs. code contract proof
DeepEval evaluates LLM outputs against quality metrics: correctness, faithfulness, relevance. It operates on natural-language outputs. Nightjar verifies the code that runs LLM applications — the parsers, validators, routers, and API handlers that sit around the LLM. These are different layers.
DeepEval and Nightjar address different layers. DeepEval: is the LLM output good? Nightjar: is the code handling the LLM output correct? Both are needed in a production LLM application.
Nightjar strengths
- ·Verifies the application code around LLM calls
- ·Catches logic errors in parsers, routers, validators
- ·Found real bugs in litellm and hermes-agent
- ·Formal proof of correctness — not probabilistic metrics
- ·Works on non-LLM code too
DeepEval strengths
- ·Evaluates LLM output quality end-to-end
- ·Hallucination detection and faithfulness metrics
- ·Regression tracking across model versions
- ·RAG pipeline evaluation
- ·Human-in-the-loop evaluation workflows
Feature Comparison
See what Nightjar finds in your code
Free to try. AGPL open source.