Comparison
LangSmith vs Tracing
LangSmith and Tracing 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 Tracing
Tracing comes up when the question is fundamentally about infrastructure.
A trace showing: user_query → retrieve(top_k=5) → rerank → completion(gpt-4o) with each step's tokens, latency, and content visible.
Frequently asked
What is the difference between LangSmith and Tracing?
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. Tracing: Tracing captures the full causal tree of an LLM request — the user input, retrieval calls, tool calls, intermediate prompts, and the final response — as a hierarchy of timed spans you can replay and inspect.
When should I use LangSmith vs Tracing?
LangSmith is the right concept when you are focused on infrastructure. Tracing applies when you are focused on infrastructure.
Are LangSmith and Tracing the same thing?
No. LangSmith is infrastructure; Tracing is infrastructure. They are related but address different parts of the AI stack.