langgraph-fundamentals

Stateful AI agent workflows are built by defining a StateGraph with discrete processing nodes and conditional edges, enabling fine-grained control over execution flow and complex branching logic. This approach manages state schemas across multiple steps.

Parallel task execution in LangGraph is handled using the Send object to dispatch multiple tasks simultaneously. This allows complex agent workflows to execute concurrent operations while maintaining overall state management across the graph.

Command in LangGraph combines state updates and routing into a single operation within graph execution. This mechanism lets developers simultaneously modify the state schema and direct the flow to specific nodes, streamlining conditional routing logic.

LangGraph is needed for agent orchestration when workflows require complex branching, looping logic, or fine-grained control over state management. Simpler linear chains lack the StateGraph structure needed for persistent state across multiple conditional steps.

External API errors during graph execution are handled through LangGraph's built-in error handling mechanisms. This ensures agent workflows can gracefully manage failures from external calls during complex phases like research synthesis or report drafting.

Streaming outputs from a StateGraph is supported natively in LangGraph to provide real-time visibility into graph execution. This allows developers to monitor state updates and node processing as they occur throughout the agent workflow.