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.
## Why Structure Matters
Tested on 22,000+ real conversation memories:
| Search scope | R@10 | Improvement |
|-------------|------|-------------|
| All closets | 60.9% | baseline |
| Within wing | 73.1% | +12% |
| Wing + hall | 84.8% | +24% |
| Wing + room | 94.8% | +34% |
The practical point is that structure improves retrieval. In the project benchmarks, narrowing the search scope by wing and room outperformed searching the entire corpus at once.
## Navigation
The palace supports graph traversal across wings:
```text
MCP tool: mempalace_traverse
arguments: { "start_room": "auth-migration" }
→ discovers rooms in wing_kai, wing_driftwood, wing_priya