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DeepEval
Open SourceModel Evaluation & BenchmarkingOpen-source LLM evaluation framework with pytest-style unit testing for AI applications.
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overview
DeepEval brings unit testing discipline to LLM outputs: research-backed metrics (G-Eval, hallucination, answer relevancy, RAG faithfulness) exposed as pytest-style assertions that run locally or in CI. It has become the default open-source harness for teams that want regression gates on model quality.
- 30+ research-backed evaluation metrics - Pytest integration for CI/CD gates - RAG-specific metrics (faithfulness, contextual recall) - Synthetic dataset generation
core features
- 30+ research-backed evaluation metrics
- Pytest integration for CI/CD gates
- RAG-specific metrics (faithfulness, contextual recall)
- Synthetic dataset generation
faq
DeepEval — frequently asked questions
- What is DeepEval?
- DeepEval brings unit testing discipline to LLM outputs: research-backed metrics (G-Eval, hallucination, answer relevancy, RAG faithfulness) exposed as pytest-style assertions that run locally or in CI. It has become the default open-source harness for teams that want regression gates on model quality.
- Does DeepEval have compliance certifications?
- DeepEval does not publicly list specific compliance frameworks in our directory. Confirm the latest posture directly with the vendor at https://docs.confident-ai.com.
- How much does DeepEval cost?
- DeepEval is open source, so you can self-host it for free. Managed/cloud tiers may be offered separately.
- Who is DeepEval for?
- Teams working on model evaluation & benchmarking — particularly those that need to evidence AI compliance and security controls.
- Is DeepEval open source?
- Yes — the source code is available on GitHub at https://github.com/confident-ai/deepeval.