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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 | Evidently AI |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.3/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 6 listed | 5 listed |
| Categories | LLM Evaluation | AI Observability |
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
Evidently AI
- Data drift detection across tabular, text, and embeddings
- LLM evaluation metrics: faithfulness, relevance, toxicity
- Interactive HTML reports for sharing with stakeholders
- Test suites for automated data quality CI checks
- Evidently Cloud for production monitoring dashboards
- Supports sklearn, pandas, and any ML framework
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
Evidently AI
Add ML model monitoring to CI/CD
Load reference (training) and current (production) data
Integrations
DeepEval
openaianthropiclangchainllamaindexragaslangfuse
Evidently AI
mlflowwandbairflowkubeflowlangchain