LangChain vs LangGraph: When to Use Code Instead of a Visual Builder

LangChain is a code library for building AI agents. If you are choosing between it and a visual tool like Dify or Flowise, the question is how much control you need.

What is LangChain, and what is LangGraph?

LangChain's docs describe an agent as a model calling tools in a loop until the task is done. The harness is everything around that loop: the prompt, the tools and any middleware that shapes behavior. LangChain gives you create_agent, a configurable harness, so you can assemble exactly the agent you want from a model, tools and a prompt.

LangGraph is the lower-level layer. The docs call it an orchestration framework and runtime for long-running, stateful agents. You model the workflow as a graph: shared state, nodes that do the work, and edges that decide what runs next. It lets you mix fixed, hand-written steps with steps the model decides.

LangChain's own docs say the packages serve different purposes, so read their comparison page before choosing.

Getting started with LangChain

Install the package and a provider, for example pip install -qU langchain "langchain[openai]". Then import create_agent from langchain.agents, give it a model and a list of tools. The overview page has a short working example.

When to use LangChain and LangGraph

When a visual builder is enough instead of LangChain

The cost of building with LangChain code

Someone has to maintain it. Libraries change, so pin versions and read release notes. You also need logging, so you can see what the agent did when something goes wrong.

A reasonable path: prototype in a visual builder, then move to code only when you hit a limit you can name.

Want help picking or setting up a workflow? Send the task and the tools you already use through the contact page.

Sources: LangChain overview, Agents, LangGraph overview, Graph API, Frameworks, runtimes, and harnesses.