Keywords AI

LangSmith vs Ragas

Compare LangSmith and Ragas side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

	LangSmith	Ragas
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Open Source
Best For	LangChain developers who need integrated tracing, evaluation, and prompt management	Developers building RAG applications who need specialized evaluation metrics
Website	smith.langchain.com	ragas.io
Key Features	Trace visualization for LLM chains Prompt versioning and management Evaluation and testing suite Dataset management Tight LangChain integration	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library
Use Cases	Debugging LangChain and LangGraph applications Prompt iteration and A/B testing LLM output evaluation and scoring Team collaboration on prompt engineering Regression testing for LLM apps	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

When to Choose LangSmith vs Ragas

Choose LangSmith if you need

Debugging LangChain and LangGraph applications
Prompt iteration and A/B testing
LLM output evaluation and scoring

Pricing: Freemium

Choose Ragas if you need

Evaluating RAG pipeline quality end-to-end
Measuring retrieval precision and recall
Testing faithfulness and answer relevance

Pricing: Open Source

About LangSmith

LangSmith is LangChain's observability and evaluation platform for LLM applications. It provides detailed tracing of every LLM call, chain execution, and agent step—showing inputs, outputs, latency, token usage, and cost. LangSmith includes annotation queues for human feedback, dataset management for evaluation, and regression testing for prompt changes. It's the most comprehensive debugging tool for LangChain-based applications.

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About Ragas

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.

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What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

Browse all Observability, Prompts & Evals tools →