LangGraph

What is LangGraph?

LangChain's chains run in a straight line, and they get tangled once your flow gets complicated. LangGraph takes a different approach: it builds apps as a graph. Each processing step is a node, nodes are wired together by edges, and data moves between them like messages. Loops, branches, parallel paths, backtracking — all the things graphs are naturally good at — are what LangGraph does best.

How does it relate to LangChain?

LangChain: a pipeline
Great for simple, fixed-order flows like "fetch → ask → answer".
LangGraph: a loopable network
Built by the same team, it reuses LangChain's components but upgrades control flow from a line to a graph, with state moving between nodes and inspectable at any point.

What's it good at?

State management
The whole flow's state is stored centrally — readable, editable, rollback-able — which makes debugging and auditing easy.
Loops and backtracking
A plain chain can only move forward; LangGraph can loop back. If an agent's check fails, it can return to a previous step and redo it.
Multi-agent collaboration
One graph can hold several agents, each owning a piece, passing messages to coordinate.
Persistence and checkpoints
You can save, pause, and resume a flow from a checkpoint, so long-running tasks survive.

Where does it fit?

Complex agents that need multi-step reasoning, repeated validation or multiple roles collaborating are its home turf: support bots, research assistants, automation workflows, multi-agent systems. For a simple Q&A, LangChain is plenty.

Bottom line: LangGraph draws an agent's flow as a graph — nodes handle steps, edges handle direction — so even complex tasks stay organized.