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Applied AI26 October 20265 min read

LangChain and LangGraph: what practitioners need to know

LangChain and LangGraph are open-source libraries for building applications around language models. They are tools, not requirements; knowing what they do helps you decide whether you need them.

In this post
  1. What they do
  2. What they save you
  3. What they cost you
  4. A sensible rule
  5. Sources and further reading

What they do

  • LangChain provides building blocks: model wrappers, prompt templates, retrievers and tool interfaces.
  • LangGraph models an application as a graph of steps with shared state, which suits agents that loop, branch and wait for people.

What they save you

Common plumbing: connecting to many model providers, loading documents, calling tools and managing state between steps.

What they cost you

  • Another layer to learn and to keep up with as it changes.
  • Abstractions can hide what is actually sent to the model, which makes debugging harder.

A sensible rule

Start with the model provider’s own client and plain code. Adopt a framework when you are repeating the same plumbing, and make sure you can still see every prompt and tool call. The skill that transfers is the design: tools, state, limits and evaluation.

Sources and further reading

  1. LangChain documentation. python.langchain.com.
  2. LangGraph documentation. langchain-ai.github.io/langgraph.

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