LangSmith

LangSmith is LangChain’s platform for tracing, evaluating, and monitoring LLM applications. It captures every run of your chain or agent as a hierarchical trace — each prompt, model response, retriever call, and tool invocation with its inputs, outputs, latency, and token cost — and pairs that with an evaluation suite: datasets of examples, LLM-as-judge and custom evaluators, regression comparisons between versions, and a prompt playground for iterating on any captured run.

Its standout trait among observability platforms is depth of integration with the LangChain ecosystem. If you build with LangChain or LangGraph, tracing is nearly free — set LANGSMITH_TRACING=true and an API key, and every step of a complex agent graph appears fully structured, no instrumentation code required. That’s the crispest reason to choose it over Langfuse (open-source, framework-neutral) or Helicone (proxy-based request logging): LangSmith understands your framework’s internals natively. It works without LangChain too, via its SDK and OpenTelemetry support, and is a hosted SaaS with self-hosting reserved for enterprise plans.

In practice, you’ll enable tracing with environment variables, use the @traceable decorator or wrap_openai for non-LangChain code, and browse traces to debug why an agent looped or a retrieval missed. Then you’ll curate failing traces into datasets, write evaluators, run evaluate() against candidate prompts or models, and compare experiments side by side before shipping. Production monitors and alerts close the loop.