Vector databases serve as the semantic memory layer of a LangGraph workflow. Every node in the graph—whether an LLM reasoning step, a retriever, or a validation agent—may need to recall information that is not part of the immediate prompt. Instead of storing plain text, data is embedded into numeric vectors that capture semantic meaning. A vector database indexes those embeddings so a node can instantly find conceptually related information even if wording differs.
In practice, this turns LangGraph from a transient orchestration engine into a stateful reasoning system. Agents can retrieve similar historical results, past interactions, or reference documents using similarity search rather than rigid identifiers. Vector stores also decouple knowledge management from code. You can update or expand the memory corpus without changing graph logic, which is essential for systems that continuously evolve.
Among open-source options, Milvus provides the scale and latency profile that LangGraph agents require. It supports approximate nearest-neighbor indexes, hybrid filters, and distributed deployments so retrieval remains consistent under heavy concurrency. By combining LangGraph’s execution control with Milvus’s retrieval precision, developers gain a foundation for persistent, context-aware AI agents.