Key features
Core capabilities each platform advertises.
- RAG-specific evaluation framework
- Component-wise metrics for RAG
- Synthetic test data generation
- LLM-as-judge evaluators
- Open-source Python library
Data not available
Updated March 10, 2026
Ragas is an open-source evaluation framework specifically designed for RAG (Retrieval-Augmented Generation) pipelines. It provides metrics for context precision, context recall, faithfulness, and answer relevancy, helping teams measure and improve the quality of their RAG systems. Ragas has become the standard evaluation toolkit for teams building production RAG applications.
Sentry provides runtime error monitoring and performance observability for AI applications. Its LLM monitoring capabilities track model calls, token usage, and latency alongside traditional error tracking. Sentry helps teams catch and debug issues in production AI pipelines with detailed stack traces and context.
Core capabilities each platform advertises.
What each tool does well, and the limitations to keep in mind.
Pros
Cons
Pros
Cons
Choose Ragas if you wantChoose if you want
Choose Sentry if you wantChoose if you want
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.
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