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DeepEval
Pytest for LLMs — unit test your AI outputs with 20+ built-in evaluation metrics.
VS
At a Glance
| Attribute | DeepEval | OpenAI Evals |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.2/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 6 listed | 4 listed |
| Categories | LLM Evaluation | LLM Evaluation |
Key Features
DeepEval
- Pytest-compatible — run with deepeval test run or pytest
- 20+ metrics: correctness, hallucination, faithfulness, bias, toxicity
- RAG-specific metrics: context relevancy, contextual recall, RAGAS
- LLM-as-judge using GPT-4o or a custom evaluator model
- Confident AI platform for evaluation result dashboards
- Red teaming module for safety and jailbreak testing
OpenAI Evals
- Built-in eval types: match, includes, fuzzy match, model-graded
- Model-graded evals for open-ended responses
- Custom eval definition via YAML
- Eval registry with hundreds of community-contributed benchmarks
- Compare performance across model versions
- Integration with OpenAI API for automated scoring
Real-World Use Cases
DeepEval
Write unit tests for your LLM application
Define test cases with input, actual_output, and expected_output
Red team your LLM for safety issues
Use DeepEval's red teaming module to generate adversarial prompts
OpenAI Evals
Measure quality before upgrading model versions
Define eval tasks from your real production use cases
Build a domain-specific benchmark
Collect 50–100 representative queries from your application logs
Integrations
DeepEval
openaianthropiclangchainllamaindexragaslangfuse
OpenAI Evals
openailangsmithwandbdeepeval
🏆 Which should you choose?
Choose DeepEval if…
- → you're already in the LLM Evaluation ecosystem and prefer DeepEval's workflow
Choose OpenAI Evals if…
- → you're already in the LLM Evaluation ecosystem and prefer OpenAI Evals's workflow