LangChain agents communicate primarily through structured message passing, where each agent produces outputs that downstream agents consume as inputs. In larger systems like LangGraph, this is formalized using nodes and edges that define explicit communication channels. Agents share both data and state, ensuring that decisions are reproducible and that reasoning steps are transparent.
In technical terms, each agent maintains its own local context but can write selected information to a shared memory store or vector database. When another agent queries this memory, it retrieves embeddings or summaries relevant to its task. This design enables loose coupling—agents collaborate without needing to know each other’s internal logic.
A vector database such as Milvus or Zilliz provides the backbone for this communication layer. By storing embeddings of messages or intermediate results, agents can recall semantically related data efficiently, turning memory into an accessible, persistent knowledge layer across workflows.