langgraph-fundamentals

You build stateful agent workflows in LangGraph by constructing a StateGraph with nodes and edges to declaratively model and execute deterministic processing pipelines. State management with reducers ensures predictable state evolution.

The best way to manage state updates in a LangGraph StateGraph is using reducers and partial updates. This state management approach ensures predictable state evolution as data moves across nodes within your directed graph.

Streaming and routing in LangGraph orchestration work by using Command and Send objects to handle edge routing, parallelism, and real-time feedback. This allows complex orchestration scenarios to execute dynamically across graph nodes.

Yes, you need to compile LangGraph workflows before executing them to validate graphs before run. This compile-before-execute discipline ensures your directed graph is structurally sound and ready to process inputs.

You handle errors when orchestrating complex AI pipelines by designing directed graphs in LangGraph that manage state and handle errors in a structured way, ensuring robust execution of your agent workflows.

Yes, you can use LangGraph for parallel processing and interactive decision pipelines. It supports Command, Send, and streaming to handle routing and parallelism, making it suitable for complex AI orchestrations and modular workflow templates.