langgraph-fundamentals

You orchestrate stateful multi-agent workflows with LangGraph by modeling agent logic as a StateGraph with clearly defined nodes and edges. This structure manages state updates, conditional routing, and parallel Send edges across complex investigations.

The best way to manage conditional routing and retries in an AI agent graph is using a LangGraph StateGraph. It enforces proper state updates and provides deterministic edge routing to handle transitions and error recovery during runtime invocation.

To compile and run a LangGraph StateGraph with streaming support, you add your nodes, wire the edges, compile the graph, and execute it using invoke or stream methods. This provides runtime execution and error handling for production-like scenarios.

Yes, you can use parallel Send edges in LangGraph to handle complex investigations. The framework allows you to model directed graphs that execute parallel branches while enforcing proper state accumulation through reducers across each step.

Yes, LangGraph supports reducers for accumulating state across graph transitions. Reducers work alongside deterministic and conditional routing within the StateGraph to ensure proper state updates are enforced as data moves between nodes.

You should model AI agent orchestration as a directed graph when you need stateful execution, conditional routing, retries, or parallel branching. LangGraph's StateGraph makes it easier to reason about complex state transitions than a simple linear chain.