Graph Memory for LLM Agents: The Relational Blind Spots That Flat Vectors Miss
A customer service agent knows that the user prefers morning delivery. It also knows the user's primary address is in Seattle. What it cannot figure out is that the Seattle address is a work address used only on weekdays, and the morning delivery window does not apply there on Mondays because of a building restriction the user mentioned three months ago. Each fact is retrievable in isolation. The relationship between them is not.
This is the failure mode that bites production agents working from flat vector stores. Each piece of information exists as an embedding floating in high-dimensional space. Similarity search retrieves facts that match a query. It does not recover the structural connections between facts — the edges that give them meaning in combination.
Most agent memory architectures are built around vector databases because they are fast, simple to set up, and work well for the majority of retrieval tasks. The failure cases are subtle enough that they often survive into production before anyone notices the pattern.
