Igor Lins e Silva
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- 22 content pages across Guide, Concepts, and Reference sections - Custom indigo/cyan theme with Lucide icons and Mermaid diagrams - GitHub Actions workflow for GitHub Pages deployment - Live preview: https://mempalace-docs.netlify.app/
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Memory Stack
MemPalace uses a 4-layer memory stack. Each layer loads progressively more data only when needed.
The Layers
| Layer | What | Size | When |
|---|---|---|---|
| L0 | Identity — who is this AI? | ~50-100 tokens | Always loaded |
| L1 | Essential Story — top moments | ~500-800 tokens | Always loaded |
| L2 | Room Recall — filtered retrieval | ~200–500 each | When topic comes up |
| L3 | Deep Search — full semantic query | Variable | When explicitly asked |
In the current implementation, a typical wake-up is roughly ~600-900 tokens for L0 + L1. Searches only fire when needed.
Layer 0: Identity
A plain text file at ~/.mempalace/identity.txt. Always loaded as the AI's self-concept.
I am Atlas, a personal AI assistant for Alice.
Traits: warm, direct, remembers everything.
People: Alice (creator), Bob (Alice's partner).
Project: A journaling app that helps people process emotions.
~50 tokens. Tells the AI who it is and who it works with.
Layer 1: Essential Story
Auto-generated from the highest-importance drawers in the palace. Groups by room, picks the top moments, and keeps the output bounded.
The generation process:
- Reads all drawers from ChromaDB
- Scores each by importance/emotional weight
- Takes the top 15 moments
- Groups by room for readability
- Truncates to fit within 3,200 characters
## L1 — ESSENTIAL STORY
[auth-migration]
- Team decided to migrate from Auth0 to Clerk — pricing + DX (session_2026-01-15.md)
- Kai debugged the OAuth token refresh issue (session_2026-01-20.md)
[deploy-process]
- Switched to blue-green deploys after the January outage (session_2026-02-01.md)
Layer 2: On-Demand Recall
Loaded when a specific topic or wing comes up in conversation. Retrieves drawers filtered by wing and/or room — typically ~200–500 tokens.
stack = MemoryStack()
stack.recall(wing="driftwood", room="auth")
# → returns recent drawers about auth in the driftwood project
Layer 3: Deep Search
Full semantic search against the entire palace. This is what fires when you or the AI explicitly asks a question.
stack.search("why did we switch to GraphQL")
# → returns top-5 matching drawers with similarity scores
Wake-Up Budget
The point of the stack is bounded startup context, not a fixed universal token count. The exact size depends on your identity file and what Layer 1 selects, but the implementation keeps wake-up meaningfully smaller than loading the full corpus into the prompt.
Using the Stack
CLI
# Wake-up context (L0 + L1)
mempalace wake-up
# Project-specific wake-up
mempalace wake-up --wing driftwood
Python API
from mempalace.layers import MemoryStack
stack = MemoryStack()
# L0 + L1: wake-up (~600-900 tokens in typical use)
print(stack.wake_up())
# L2: on-demand recall
print(stack.recall(wing="myapp"))
# L3: deep search
print(stack.search("pricing change"))
# Status
print(stack.status())