Building multi-agent systems in LangGraph begins with defining agent nodes and their communication edges. Each agent encapsulates a distinct behavior—planning, retrieval, critique, summarization—and the edges determine when one agent’s output triggers another’s input. Developers describe these relationships declaratively, and the LangGraph runtime handles execution order, concurrency, and error propagation automatically.
A typical design might include a planner node that decomposes a task, a retriever node that fetches supporting data, and several specialized executors that handle sub-tasks. Because LangGraph tracks the full dependency graph, developers can visualize message flow and analyze where reasoning diverges. This structure encourages modularity: you can replace or retrain individual agents without rebuilding the entire system. Built-in checkpointing allows you to re-run just the affected branches.
Multi-agent workflows often rely on shared knowledge. By integrating a vector database such as Milvus or Zilliz Cloud, agents can store and query a unified embedding space. Each agent contributes new information to the vector store, while others retrieve it as semantic memory. This persistent, searchable layer allows distributed agents to collaborate coherently, scaling from prototypes to enterprise-grade AI systems.