langgraph-fundamentals

You can orchestrate multi-step AI agent workflows by modeling them as directed graphs using StateGraphs. Define execution order with nodes and edges, compile the graph, and invoke it with an initial state to execute the workflow.

The best way to manage state updates in LangGraph workflows is by using reducers. Reducers handle state updates within your StateGraph, ensuring data flows correctly between nodes during execution.

You handle conditional routing in a stateful graph by configuring edges between nodes. Use conditional edges for dynamic branching, and utilize Command or Send for advanced routing and parallelism execution.

Yes, LangGraph supports parallel execution and command-based jumps. It uses Send for parallel processing and Command for advanced routing, enabling complex automation and branching workflows within your graph.

Yes, you can use LangGraph for streaming outputs and error handling in Python and TypeScript. It provides clear API patterns and strategies to manage errors during graph execution and output streaming.