Comparison
LangSmith vs Offline Evaluation
LangSmith and Offline Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for LangSmith
LangSmith comes up when the question is fundamentally about infrastructure.
A LangChain app with one line of setup: every chain run shows up in the LangSmith trace UI with input, output, intermediate steps, and per-step costs.
When you would reach for Offline Evaluation
Offline Evaluation comes up when the question is fundamentally about evaluation.
A RAG team's offline eval: 500 (question, gold answer) pairs, scored by LLM-as-judge on faithfulness and relevance, run on every prompt PR.
Frequently asked
What is the difference between LangSmith and Offline Evaluation?
LangSmith: LangSmith is LangChain's commercial LLM observability and evaluation platform. It captures traces (LangChain-native and OTel), runs evaluations, manages prompt versions, and supports dataset curation. Offline Evaluation: Offline evaluation runs a fixed dataset of inputs through a candidate model or prompt, scores each output, and reports aggregate quality — the standard way to compare changes before shipping.
When should I use LangSmith vs Offline Evaluation?
LangSmith is the right concept when you are focused on infrastructure. Offline Evaluation applies when you are focused on evaluation.
Are LangSmith and Offline Evaluation the same thing?
No. LangSmith is infrastructure; Offline Evaluation is evaluation. They are related but address different parts of the AI stack.