Summary
The framework and observability platform most teams use to build, debug, and ship LLM agents.
Description
LangChain is the most widely used toolkit for building language-model applications, and it has grown into three distinct things that are worth separating.
LangChain
The open-source framework, in Python and JavaScript, providing standard interfaces over models, tools, retrievers, and memory. Its value is portability: swapping a model or a vector store is a configuration change rather than a rewrite.
LangGraph
A lower-level library for building stateful, multi-step agents as explicit graphs. Where a simple chain is a straight line, LangGraph handles loops, branching, human approval steps, and persistence — which is what production agents actually need. It is used well beyond LangChain-based codebases.
LangSmith
The observability and evaluation platform, and for many teams the main reason they engage with LangChain at all. It traces every step of an agent run, so you can see the exact prompt, tool call, and response that produced a bad answer. Datasets, evaluations, and prompt versioning turn "it feels worse" into a measurable regression.
Frameworks are free and open source; LangSmith has a free tier with usage-based paid plans, plus self-hosted enterprise deployment.
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