Back to all tools
Open Source
Tool Comparison
Ragas
Evaluate your RAG pipeline with LLM-as-judge metrics — faithfulness, relevance, recall.
VS
At a Glance
| Attribute | Ragas | OpenAI Evals |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.3/5 | 4.2/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 4 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
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
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
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
Ragas
langchainllamaindexopenailangsmithwandb
OpenAI Evals
openailangsmithwandbdeepeval