State and memory are first-class citizens in LangGraph. Each node can read or write shared variables stored in a graph-level state dictionary. This allows agents to reference prior outputs, track intermediate goals, or reuse retrieved data. Developers can checkpoint the state to disk, replay a workflow from any step, or compare different execution branches. This explicit management of memory eliminates the “black box” behavior typical of long LLM sessions.
Internally, LangGraph serializes node states and message histories so they persist between runs. Memory can be short-term (for intra-graph context) or long-term (persisted to external storage). Developers can combine these approaches—keeping transient variables in memory while writing durable artifacts like embeddings to a database. When an agent restarts, it can reload past states and continue reasoning seamlessly.
For large-scale systems, a vector database such as Milvus provides efficient long-term memory. Each interaction, document, or observation can be embedded and indexed. When a new query arrives, LangGraph retrieves the most relevant vectors and injects them back into the reasoning node. This setup gives agents the ability to “remember” across sessions without consuming the limited token window of the model itself.