LangSmith

Most teams start debugging LLM apps the same way they debug everything else: console logs, print statements and a general-purpose APM tool bolted on after something breaks. That works fine for a single prompt and a single response. It falls apart the moment you're running a multi-step chain or an agent that calls tools, loops and branches. A traditional log tells you a request was slow or failed. LangSmith tells you which node in the pipeline did it, what prompt went in, what came back and how long that one step took. That's the real difference, and it's why LangSmith exists as its own category instead of being a plugin for your existing monitoring stack.

LangSmith comes from the LangChain team, and it's built around three jobs: tracing, testing and production monitoring. Tracing follows a request through your entire LLM workflow so you can inspect inputs and outputs at every step. The evaluation tooling lets you build datasets of example inputs and expected outputs, then run automated assessments against them. That's the part that pays off when you swap models or rewrite a prompt and want proof you didn't break something. On the monitoring side, you get latency, token usage, error rates and cost tracked over time, so you can see how your app actually behaves once real traffic hits it.

The obvious question is whether you need to be on LangChain to use it. You don't. LangSmith supports non-LangChain apps through its SDK and API, though the integration is tightest if you're already using the framework. There's a free Developer tier for individual use with limited trace volume, paid plans starting at $39/mo that add trace storage, team collaboration and higher limits, and Enterprise options for teams with compliance or data residency requirements. If you're building agents or long chains and you're tired of guessing where things went wrong, this is the tool built specifically for that problem.