description LangSmith Overview
While not an agent builder itself, LangSmith is critical infrastructure for *building* and *improving* agents. It provides end-to-end observability, allowing developers to trace every step, input, and output of a complex agent run. This capability is invaluable for debugging hallucinations, optimizing prompt chains, and rigorously evaluating agent performance against golden datasets before deployment.
help LangSmith FAQ
Is LangSmith an agent builder like Google Vertex AI Agent Builder?
LangSmith is not primarily an agent builder; it is an observability, evaluation, and debugging platform from the LangChain team. Developers usually pair it with LangChain or LangGraph to inspect traces, tool calls, prompts, and model outputs.
What does a LangSmith trace show in an agent run?
A trace can show each step of an LLM workflow, including prompt inputs, retrieved documents, tool calls, intermediate outputs, latency, and errors. That is especially useful when a LangGraph agent makes several tool calls before producing one final answer.
How does LangSmith help with hallucination debugging?
LangSmith lets developers compare what the model answered against the inputs, retrieved context, and evaluation results for that run. If an answer ignored a source document or used the wrong tool output, the trace gives a concrete place to inspect.
Can LangSmith run evaluations, or is it only logging?
LangSmith supports datasets and evaluations, so teams can run the same test cases across prompt, model, or retrieval changes. That matters for production LLM apps because a new prompt can improve one task while breaking another.
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