langgraph-fundamentals

Model steps as a directed StateGraph using nodes, edges, and START/END markers to build stateful AI agent workflows. This approach defines deterministic, compilable graphs that execute complex conditional routing logic reliably while clarifying node roles and state flow.

LangGraph handles state updates via reducers that support partial state updates during graph execution. Nodes process a structured state and return transformed outputs, ensuring that complex agent workflows maintain clear state flow and data consistency across parallel workers.

Yes, you can run parallel workers in a LangGraph workflow. The StateGraph-centric design supports defining complex execution paths, allowing multiple nodes to process state simultaneously before converging, which is essential for orchestrating advanced AI agent tasks efficiently.

Structure error handling in LangGraph agent orchestration by defining clear node and edge semantics within your StateGraph. This deterministic approach applies compile-before-execution logic to manage complex, stateful agent graphs, ensuring reliable error handling and conditional routing.

Yes, you must compile a LangGraph before invoking it. The workflow requires compile-before-execution to validate the directed graph structure, ensuring that nodes, edges, and state flow are correctly defined before processing input and returning a transformed output.