langgraph-fundamentals

State management in LangGraph involves defining state schemas and using reducer patterns for updates. This approach ensures data persists correctly across nodes, allowing complex AI agents to maintain context throughout directed graph execution.

Build a multi-agent system in LangGraph by defining nodes for each agent and using edges for conditional routing. This allows each agent's output to feed into the next, with branching based on intermediate results.

Error handling in LangGraph requires implementing robust retry logic and tool error management within your graph nodes. This ensures orchestrated workflows recover gracefully from failures during execution.

Use the Command and Send API in LangGraph for complex routing and parallelization tasks. These advanced concepts enable fine-grained control over execution flow when building highly orchestrated AI systems.

Define a basic StateGraph in LangGraph by setting up a state schema, creating at least two nodes, and connecting them with linear edges. This structure forms the foundation for directed graph execution workflows.