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Ragas
Evaluate your RAG pipeline with LLM-as-judge metrics — faithfulness, relevance, recall.
VS
At a Glance
| Attribute | Ragas | DeepEval |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.3/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 6 listed |
| Categories | RAG FrameworksAI Observability | LLM Evaluation |
Key Features
Ragas
- Faithfulness — measures if the answer is grounded in retrieved context
- Answer Relevance — checks if the answer addresses the question
- Context Precision and Recall — evaluates retriever quality
- LLM-as-judge evaluation — no labeled ground truth needed
- Integrates with LangChain, LlamaIndex, and any RAG pipeline
- Testset generation — automatically create evaluation datasets
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
Real-World Use Cases
Ragas
Benchmark RAG pipeline before going to production
Generate a test set from your documents with Ragas TestsetGenerator
CI quality gate for RAG changes
Add a Ragas evaluation step to your GitHub Actions pipeline
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
Integrations
Ragas
langchainllamaindexopenailangsmithwandb
DeepEval
openaianthropiclangchainllamaindexragaslangfuse