LangSmith is an LLM application observability and evaluation platform from the LangChain team. It provides tracing, monitoring, and testing infrastructure for AI applications, capturing the full input/output chain, intermediate steps, token counts, latency, and costs for every LLM call in production or development. When you instrument an application with LangSmith, every request becomes inspectable. The tracing view makes debugging AI agents and chains significantly faster: you can see exactly which step in a multi-step agent produced an incorrect output, what the model received as input at that point, and how long each step took. LangSmith also includes an evaluation framework for running automated tests against LLM outputs and comparing performance across model versions, which is essential for teams iterating quickly on prompts and model choices. LangSmith works with LangChain, LangGraph, and any other LLM framework via a universal Python and TypeScript SDK. The free tier covers a meaningful number of monthly traces, making it practical for individual developers and small teams. Enterprise plans add user access controls, data retention policies, SSO, and higher trace volume.

What the community says

Widely praised by AI developers building production applications as a must-have tool once you move beyond simple prototypes. Reddit and Hacker News discussions consistently rank it highly for debugging complex agent chains. The evaluation framework gets particular praise for enabling A/B testing of prompts. Criticism focuses on pricing escalating quickly at higher trace volumes and the UI being optimized for LangChain users even when using other frameworks. Generally considered the standard for LLM observability in the open-source ecosystem.

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