langgraph-fundamentals

To build a stateful agent workflow with LangGraph, you model the process as a StateGraph using nodes, edges, and START/END markers. You then call compile() and invoke() to execute the graph for predictable state management.

Reducers in LangGraph state management accumulate updates within a StateGraph. They allow nodes to return partial dictionaries, preventing the need to mutate the full state directly during complex agent workflow orchestration.

Yes, you can route and parallelize tasks in a LangGraph StateGraph using Command and Send execution semantics. These features enable conditional routing and task parallelization across Python and TypeScript implementations.

LangGraph supports streaming and invoke execution semantics for real-time feedback in agent workflows. This allows you to capture runtime flow control and handle errors predictably during graph execution.

Using a directed graph approach for agent orchestration enables deterministic execution and modular node design. Unlike standard chains, LangGraph state graphs provide controlled execution paths with conditional routing based on state.

You do not need to rewrite your entire state object when returning data from a node. LangGraph supports partial dict returns alongside state reducers, allowing you to apply targeted updates without mutating the full state.