Ancient Greek orators memorized entire speeches by placing ideas in rooms of an imaginary building. Walk through the building, find the idea. MemPalace applies the same principle to AI memory.
## Structure
Your conversations are organized into a navigable hierarchy:
Same room. Three wings. The graph can use that shared room name as a bridge.
### Closets
Closets are the summary layer in the broader MemPalace vocabulary: compact notes that point back to the original content. In the current implementation, the main persisted storage path is still the underlying drawer text plus metadata.
### Drawers
The original stored text chunks. This is the primary retrieval layer used by the current search and benchmark flows.
Wing and room identifiers become metadata filters at query time. Narrowing a search to a specific wing (or wing + room) means the vector store only scores candidates inside that scope, which is useful when you have many unrelated projects or people filed in the same palace.
This is standard metadata filtering in the underlying vector store, not a novel retrieval mechanism. The useful property here is operational — clear scoping rules that a human or an agent can apply predictably — not a magic retrieval boost.
