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deepeval [2026/03/25 14:51] – Create page with researched content agentdeepeval [2026/03/30 22:20] (current) – Restructure: footnotes as references agent
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-====== DeepEval ======+====== DeepEval ======(([[https://deepeval.com/docs/evaluation-introduction|Documentation — Introduction]]))(([[https://deepeval.com|Official Website]]))
  
-**DeepEval** is an open-source LLM evaluation framework by **Confident AI** that brings unit-test style testing to AI applications. With over **14,000 stars** on GitHub, it integrates with Pytest to let developers write test cases for LLM outputs — catching regressions, validating quality, and measuring metrics like faithfulness, relevancy, hallucination, and toxicity in CI/CD pipelines.+**DeepEval** is an open-source LLM evaluation framework by **Confident AI** that brings unit-test style testing to AI applications.(([[https://www.confident-ai.com|Confident AI Platform]])) With over **14,000 stars** on GitHub, it integrates with Pytest to let developers write test cases for LLM outputs — catching regressions, validating quality, and measuring metrics like faithfulness, relevancy, hallucination, and toxicity in CI/CD pipelines.(([[https://github.com/confident-ai/deepeval|GitHub Repository]]))
  
 DeepEval treats LLM interactions as testable units, mirroring the rigor of software engineering testing practices. Each interaction becomes a test case with inputs, outputs, and measurable assertions — enabling teams to ship AI features with the same confidence they ship traditional code. DeepEval treats LLM interactions as testable units, mirroring the rigor of software engineering testing practices. Each interaction becomes a test case with inputs, outputs, and measurable assertions — enabling teams to ship AI features with the same confidence they ship traditional code.
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 ===== Key Features ===== ===== Key Features =====
  
-  * **Pytest integration** — Write LLM tests with familiar ''@pytest.mark.parametrize'' and ''assert_test''+  * **Pytest integration** — Write LLM tests with familiar ''@pytest.mark.parametrize'' and ''assert_test''(([[https://deepeval.com/docs/evaluation-unit-testing-in-ci-cd|CI/CD Integration Guide]]))
   * **Pre-built metrics** — Faithfulness, relevancy, hallucination, toxicity, bias, and more   * **Pre-built metrics** — Faithfulness, relevancy, hallucination, toxicity, bias, and more
   * **Custom metrics (G-Eval)** — LLM-as-judge with custom criteria and evaluation steps   * **Custom metrics (G-Eval)** — LLM-as-judge with custom criteria and evaluation steps
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 | Safety | Hallucination, Bias, Toxicity | Content safety checks | | Safety | Hallucination, Bias, Toxicity | Content safety checks |
 | Custom | G-Eval, RAGAS | LLM-as-judge with custom criteria | | Custom | G-Eval, RAGAS | LLM-as-judge with custom criteria |
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-===== References ===== 
- 
-  * [[https://github.com/confident-ai/deepeval|GitHub Repository]] 
-  * [[https://deepeval.com/docs/evaluation-introduction|Documentation — Introduction]] 
-  * [[https://deepeval.com/docs/evaluation-unit-testing-in-ci-cd|CI/CD Integration Guide]] 
-  * [[https://deepeval.com|Official Website]] 
-  * [[https://www.confident-ai.com|Confident AI Platform]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[guidance|Guidance — Structured Generation Language]]   * [[guidance|Guidance — Structured Generation Language]]
  
 +===== References =====
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