Explain LangGraph state machines (ANSWERED)
LangGraph has emerged as the go-to framework for stateful agent workflows, replacing brittle LangChain chains in production. Interviewers want graph thinking — nodes, edges, conditional routing, and checkpointing — not just API familiarity. Essential for any LangChain shop hiring GenAI engineers.
TL;DR — Quick Answer
LangGraph represents workflows as directed graphs where nodes are functions, edges define transitions, and a shared state object flows through the graph.
The Interview Question
How does LangGraph model agent workflows as graphs? Explain nodes, edges, and state.
Deep Explanation
LangGraph builds on LangChain with cyclic graphs for agent loops. State is a typed dict/schema updated by each node. Conditional edges route based on state (e.g., 'needs more info' → search node).
Supports human-in-the-loop, checkpointing, and parallel node execution. Ideal for complex, stateful agent workflows vs linear chains.
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