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Tool Comparison
Ragas
Evaluate your RAG pipeline with LLM-as-judge metrics — faithfulness, relevance, recall.
VS
At a Glance
| Attribute | Ragas | LangSmith |
|---|---|---|
| License / Pricing | Open Source | Free-Limited |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.3/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | RAG FrameworksAI Observability | AI Observability |
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
LangSmith
- Full trace visualization for every LLM call and chain step
- Dataset management for evaluation benchmarks
- Automated evaluators with LLM-as-judge
- Prompt versioning and A/B testing
- Production monitoring with latency and error tracking
- Human annotation workflows for labeling
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
LangSmith
Debug a hallucinating RAG pipeline
Add LANGSMITH_API_KEY to your environment
Integrations
Ragas
langchainllamaindexopenailangsmithwandb
LangSmith
langchainllamaindexopenaianthropicpinecone
🏆 Which should you choose?
Choose Ragas if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in
Choose LangSmith if…
- → you want a managed or commercial offering with enterprise support and SLAs