LangSmith
By LangChain
LangSmith is an observability and evaluation platform from the makers of LangChain, used to debug, test, monitor, and improve applications built with large language models.
Definition
LangSmith is an observability and evaluation platform from the makers of LangChain, used to debug, test, monitor, and improve applications built with large language models.
Overview
LangSmith gives developers visibility into what's actually happening inside an LLM application — logging every prompt, model call, tool invocation, and intermediate step in a chain or agent so issues like hallucinations, slow responses, or unexpected tool use can be diagnosed. This is especially valuable for applications built with LangChain, though LangSmith can be used with LLM applications more broadly. Beyond tracing and debugging, LangSmith supports systematic evaluation: teams can define test datasets and automated or human-graded evaluators to measure whether prompt or model changes actually improve output quality, rather than relying on manual spot-checking. LangSmith is part of the emerging "LLMOps" toolkit alongside platforms like Vellum and traditional MLOps tools like Weights & Biases, reflecting the growing need for observability and evaluation infrastructure specifically tailored to LLM-based applications rather than classical machine-learning models.
Key Features
- Detailed tracing of LLM calls, tools, and chain execution
- Debugging tools for diagnosing hallucinations and unexpected behavior
- Dataset-based evaluation with automated and human-graded scoring
- Prompt and model version comparison over time
- Production monitoring for latency, cost, and error rates
- Deep integration with the LangChain framework