LangSmith is LangChain's answer to a problem that becomes obvious the moment an LLM application leaves a notebook and goes into production: once a chain or agent has several steps, figuring out why a specific output was wrong, which step introduced the error, what the retrieved context actually was, becomes genuinely hard without dedicated tracing. LangSmith traces every step of a run, showing exactly what was sent to the model and what came back at each point.
It also handles evaluation, letting teams run a set of test cases against a dataset and score outputs automatically or with human review, which turns whether the latest prompt change made things better or worse from a vibe into something measurable. While it's built by the LangChain team, it works with applications that don't use LangChain itself, tracing raw LLM API calls just as well as a LangChain-specific chain.
Pricing is freemium, with a free tier for smaller usage and paid plans for teams running serious production traffic, and it has become something close to a default observability layer for teams already in the LangChain ecosystem, while competing with Langfuse and other dedicated observability tools for teams that aren't.