langgraph-fundamentals

Build stateful workflows with LangGraph by modeling applications as directed graphs using StateGraph. Define nodes for processing work and edges for execution order, enabling complex agent orchestration with branching and loops.

Manage state in a LangGraph workflow by implementing state management strategies with reducers and partial state updates. Route commands and control execution flow by configuring the edges connecting your processing nodes.

Yes, you need Python or TypeScript programming experience to implement LangGraph workflows. You also need a solid understanding of LangGraph concepts like state management, nodes, and edges to design applications effectively.

LangGraph handles error management by implementing robust error handling with retry policies and tool nodes. This approach ensures complex agent workflows remain stable during execution failures within the graph.

Yes, you can use LangGraph for human-in-the-loop capabilities. By designing directed graphs with state management, nodes, and edges, you can create complex agent workflows that pause for human intervention and resume execution.

Use a graph-based framework like LangGraph when your agent orchestration requires complex workflows with branching, loops, and stateful memory. Directed graphs provide superior control over execution order compared to standard linear chains.