LangGraph Technical Architecture: Building Intelligent Agent Collaboration Through Graph Computing

LangGraph Technical Architecture Deep Dive and Implementation Guide Principle Explanation: Intelligent Agent Collaboration Through Graph Computing 1.1 Dynamic Graph Structure LangGraph’s computational model leverages directed graph theory with dynamic topology for agent coordination. The core architecture comprises three computational units: • Execution Nodes: Python function modules handling specific tasks (<200ms average response time) • Routing Edges: Multi-conditional branching system supporting O(n²) complexity expressions • State Containers: JSON Schema-structured storage with 16MB capacity limit （Visualization: Multi-agent communication framework, Source: Unsplash） Typical workflow implementation for customer service systems: class DialogState(TypedDict): user_intent: str context_memory: list service_step: int def intent_analysis(state: DialogState): # Intent recognition …