# DeepEval
> DeepEval is an open-source testing framework for LLM applications. It provides a unit-testing-like experience for developers to evaluate model outputs using metrics like faithfulness, relevancy, and hallucination detection. The framework is designed to integrate into CI/CD pipelines to ensure model performance across iterations.
- URL: https://optimly.ai/brand/deepeval
- Slug: deepeval
- BAI Score: 62/100
- Archetype: Challenger
- Category: Software
- Last Analyzed: April 10, 2026
## Competitors
- Arize Phoenix Arize Ai (https://optimly.ai/brand/arize-phoenix-arize-ai)
## AI-Suggested Alternatives
- Ad Hoc Scripting (https://optimly.ai/brand/ad-hoc-scripting)
## Also Referenced By
- Post Hoc Eval Scaling (https://optimly.ai/brand/post-hoc-eval-scaling)
## Buyer Intent Signals
Problems: Manual Human Evaluation: Using human reviewers to manually grade model outputs based on custom rubrics. | Ad-hoc Scripting: Writing custom Python scripts and regex patterns to check for specific keywords or formatting in LLM responses. | Evaluation Agencies: Hiring specialized AI safety or data labeling firms to benchmark model performance. | Public Benchmarks: Relying on generic public benchmarks (MMLU, GSM8K) which do not reflect specific business use cases.
Solutions: open source LLM evaluation framework | how to test RAG pipeline faithfulness | llm unit testing python library | enterprise AI safety monitoring software | best tool for llm hallucination detection