Comparison
Vector Memory vs Structured Memory Architecture
Vector memory and structured memory are two architectures for long-term AI memory. Vector memory stores content as high-dimensional embeddings and retrieves by similarity search. Structured memory stores typed entities, facts, and relationships — often as a graph — and retrieves by deterministic traversal or keyed lookup.
Side-by-side comparison
| Capability | Vector Memory | Structured Memory |
|---|---|---|
| Retrieval model | Approximate nearest neighbor | Deterministic traversal / lookup |
| Reproducibility | Drifts with index updates | Stable across updates |
| Provenance | Implicit, embedding-bound | Explicit on every node and edge |
| Schema | Schema-less | Typed, queryable |
| Best for | Fuzzy recall, semantic search | Facts, relationships, agent state |
| Auditability | Hard — outputs depend on index state | Verifiable end-to-end |
Summary
Vector memory excels at fuzzy semantic recall. Structured memory excels at reproducible, auditable agent state. Production-grade deterministic AI systems typically combine both, using structured memory as the system of record and vector memory as a similarity index over it.