LangSmith vs Ragas (2026)
Updated March 9, 2026
Overview
LangSmith is LangChain's platform for tracing, evaluating, and deploying AI agents. Compare its features, pricing, pros and cons, and alternatives.
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.
Key features
Core capabilities each platform advertises.
- Native tracing for LangChain and LangGraph
- OpenTelemetry and multi-language SDKs
- Offline and online evaluations
- Annotation queues and datasets
- Prompt Hub with versioning
- RAG-specific evaluation framework
- Component-wise metrics for RAG
- Synthetic test data generation
- LLM-as-judge evaluators
- Open-source Python library
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Native LangChain and LangGraph tracing with no extra instrumentation
- Evals, datasets, and annotation queues sit in the same workspace as traces
- Tracing, deployment, and sandboxes from one vendor
- Framework-agnostic SDKs and OpenTelemetry support
Cons
- Per-seat pricing on Plus, and the free plan allows one seat
- 14-day base retention, with longer retention billed at a higher rate
- LLM Gateway is still in beta
- Self-hosting and hybrid deployment are Enterprise only
Pros
- Specialized focus on RAG evaluation with metrics specifically designed for retrieval systems
- Open-source framework providing transparency and community-driven development
- Strong industry endorsement from OpenAI, LangChain, and LlamaIndex
- Y Combinator backing providing credibility and potential for growth
- Easy integration with popular frameworks like LangChain and LlamaIndex
Cons
- Very small team of 3 employees may limit support and feature development
- Limited to RAG evaluation, not a comprehensive observability solution
- Minimal funding ($500K) compared to competitors may impact long-term sustainability
- Lack of clear commercial pricing model for enterprise features
LangSmith or Ragas — which should you choose?
Choose LangSmith if you wantChoose if you want
- Debugging LangChain and LangGraph agents
- Evaluating agent output against datasets
- Human review through annotation queues
- Deploying long-running agents
Choose Ragas if you wantChoose if you want
- Evaluating RAG pipeline quality end-to-end
- Measuring retrieval precision and recall
- Testing faithfulness and answer relevance
- Generating synthetic evaluation datasets
- Benchmarking RAG across configurations
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