langgraph-fundamentals

You can build a LangGraph agent workflow by modeling it as a directed state graph using StateGraph, Nodes, and Edges. This structure enforces clear state management and deterministic execution for branching logic and parallel task orchestration.

Conditional edge routing in LangGraph state machines directs execution flow based on specific state conditions. It enables scalable orchestration by allowing multi-step agents to branch logic dynamically during runtime execution.

You run parallel tasks in a LangGraph workflow by using the Send parallelism feature. This allows the state machine to orchestrate scalable parallel processing and aggregate the results back into the final state.

Yes, LangGraph is designed for multi-step campaign orchestration. It models AI-driven processes as directed graphs, allowing you to process inputs, apply branching logic, and aggregate results reliably across multiple steps.

LangGraph enforces node signatures, edge types, reducers, streaming, and error handling requirements. These constraints ensure reliable execution and state management across complex agent workflows.

The best way to structure a simple two-node LangGraph is to define START and END markers, connect the nodes with edges, compile the graph, and run it with an initial state to observe the final output.