LangChain chains flow in one direction. Input enters, output exits, done.
Real agent workflows are not linear. A plan might need revision. A search might return empty results and require a different approach. A code review might fail and send the work back for fixes. A task might need to branch differently depending on what kind of input it receives.
LangGraph models agent workflows as directed graphs. Nodes are actions. Edges are conditional transitions. The agent's state flows through the graph, taking different paths based on what it finds at each step.
The result: agent workflows that are inspectable, debuggable, resumable from any node, and capable of complex conditional logic without becoming spaghetti code.
What LangGraph Adds to LangChain
