Overview

Rating

10.0 / 10

Rating

Best For

Teams building agents on LangChain or LangGraph who want tracing, evals, and deployment from one vendor

Best For

Developers building RAG applications who need specialized evaluation metrics

Product Summary

LangSmith is LangChain's platform for tracing, evaluating, and deploying AI agents. Compare its features, pricing, pros and cons, and alternatives.

Product Summary

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.

Starting Price

Free

Starting Price

Open Source

Free Trial

Yes

Free Trial

Free Version

Yes

Free Version

Website

langchain.com

Website

ragas.io

Key features

Core capabilities each platform advertises.

LangSmith

Native tracing for LangChain and LangGraph
OpenTelemetry and multi-language SDKs
Offline and online evaluations
Annotation queues and datasets
Prompt Hub with versioning

Ragas

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.

LangSmith

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

Ragas

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

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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LangSmith vs Ragas (2026)

Overview

Key features

Strengths and tradeoffs

LangSmith or Ragas — which should you choose?

Compare LangSmith and Ragas on your own traffic

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