# 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
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## Full Details / RAG Data
### Overview
DeepEval is listed in the AI Directory.
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.
### Metadata
| Field        | Value |
|--------------|-------|
| Name         | DeepEval |
| Slug         | deepeval |
| URL          | https://optimly.ai/brand/deepeval |
| BAI Score    | 62/100 |
| Archetype    | Challenger |
| Category     | Software |
| Last Analyzed | April 10, 2026 |
| Last Updated | 2026-05-03T08:41:15.037Z |
### Verified Facts
- Founded: 2023
- Headquarters: San Francisco, CA
### Competitors
| Name | Profile |
|------|---------|
| Arize Phoenix Arize Ai | https://optimly.ai/brand/arize-phoenix-arize-ai |
### Also Referenced By
- Post Hoc Eval Scaling (https://optimly.ai/brand/post-hoc-eval-scaling)
### AI-Suggested Alternatives
- Ad Hoc Scripting (https://optimly.ai/brand/ad-hoc-scripting)
### Buyer Intent Signals
#### Problems this brand solves
- 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.
#### Buyers search for
- 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
### Links
- Canonical page: https://optimly.ai/brand/deepeval
- JSON endpoint: /brand/deepeval.json
- LLMs.txt: /brand/deepeval/llms.txt