langgraph

Create a StateGraph with a typed State schema, define node functions that process state, and connect them with edges. Compile the graph and invoke it with initial state to orchestrate multi-step agent workflows with explicit control over execution flow.

Persistence uses checkpointers to save graph state at each step, enabling long-running workflows to survive interruptions and resume from checkpoint. This is essential for durable agent applications that require human-in-the-loop interaction or recovery from failures.

Yes. LangGraph supports multi-agent patterns through subgraphs and composition, allowing you to model multiple actors in cyclic graphs with state management and coordinated transitions between agents.

LangGraph enables interrupts through nodes that pause execution and expose state for human review or modification. Resume execution after interrupt handling by reinvoking the graph from the checkpoint with updated state.

Use conditional edges to branch execution based on node output, and fan-out/in patterns to parallelize work across multiple nodes. These patterns enable complex decision trees and aggregation logic in stateful workflows.

No. LangGraph abstracts graph concepts into nodes (functions) and edges (transitions). You define state schemas and node logic in familiar code; the framework handles graph orchestration, routing, and persistence.