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LangSmith vs Ragas

A side-by-side comparison of LangSmith and Ragas, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

LangSmith

Observability

LangChain's hosted observability + eval platform.

View LangSmith

Ragas

Eval

Evaluation toolkit for RAG and LLM applications.

View Ragas

At a glance

Feature comparison of LangSmith and Ragas
AttributeLangSmithRagas
Category (differs)ObservabilityEval
Pricing (differs)FREEMIUMFREE
License (differs)ProprietaryOpen source
Deployment (differs)Cloud
Platforms (differs)API, WebCLI, API
Model support (differs)Model-agnosticBYO key / model
Vendor (differs)LangChainExploding Gradients

The honest brief

LangSmith

Deepest native LangChain/LangGraph tracing — but cloud-only, where Langfuse lets you self-host the same.

  • Native LangChain/LangGraph tracing
  • Works standalone via SDKs
  • Datasets + eval orchestration
  • Prompt playground built in
  • Closed source, cloud-only
  • Self-host is Enterprise-only
  • Best value inside LangChain stack

Ragas

Popularized reference-free RAG metrics — faithfulness, context precision — scored by an LLM judge, so you evaluate without gold answers.

  • Faithfulness & relevancy metrics
  • Knowledge-graph synthetic test sets
  • LLM-as-judge scoring
  • Integrates LangChain, LlamaIndex, CI
  • LLM-judge scores add cost/variance
  • Python library, no hosted UI
  • Focused on RAG, narrower scope