lcatlett
8472d553a3
When the user removes ~/.mempalace/ (a strong "do not auto-capture"
signal), the next hook fire would silently recreate the entire dir
hierarchy and ingest existing transcripts:
1. _log() at hooks_cli.py:148 unconditionally calls
STATE_DIR.mkdir(parents=True, exist_ok=True), so the act of
writing the hook log line recreated ~/.mempalace/hook_state/
2. With no config file present, hook_stop_auto_save and
hook_precompact_auto_save defaulted to True (no override to read)
3. The full save path then ran, materializing palace/, wal/,
knowledge_graph.sqlite3, and N drawers from existing transcripts
in ~/.claude/projects/*.jsonl
All four entry points (hook_stop, hook_precompact, hook_session_start,
and _log itself) now check a new PALACE_ROOT = Path.home() / ".mempalace"
constant first and short-circuit (returning {} on stdout, never logging)
when the dir is absent. The user-removable directory is now a kill-switch.
Five unit tests in tests/test_hooks_cli.py cover: hook_stop /
hook_precompact / hook_session_start do not create the dir when absent;
_log() does not create it when absent; existing dir proceeds normally
(regression).
Caught in the wild on a downstream fork: ~146 drawers materialized in
under a second after a deliberate `rm -rf ~/.mempalace/`, into a planning
session that was explicitly not meant to be captured.
mempalace/ — Core Package
The Python package that powers MemPalace. All modules, all logic.
Modules
| Module | What it does |
|---|---|
cli.py |
CLI entry point — routes to mine, search, init, compress, wake-up |
config.py |
Configuration loading — ~/.mempalace/config.json, env vars, defaults |
normalize.py |
Converts 5 chat formats (Claude Code JSONL, Claude.ai JSON, ChatGPT JSON, Slack JSON, plain text) to standard transcript format |
miner.py |
Project file ingest — scans directories, chunks by paragraph, stores to ChromaDB |
convo_miner.py |
Conversation ingest — chunks by exchange pair (Q+A), detects rooms from content |
searcher.py |
Semantic search via ChromaDB vectors — filters by wing/room, returns verbatim + scores |
layers.py |
4-layer memory stack: L0 (identity), L1 (critical facts), L2 (room recall), L3 (deep search) |
dialect.py |
AAAK compression — entity codes, emotion markers, 30x lossless ratio |
knowledge_graph.py |
Temporal entity-relationship graph — SQLite, time-filtered queries, fact invalidation |
palace_graph.py |
Room-based navigation graph — BFS traversal, tunnel detection across wings |
mcp_server.py |
MCP server — 19 tools, AAAK auto-teach, Palace Protocol, agent diary |
onboarding.py |
Guided first-run setup — asks about people/projects, generates AAAK bootstrap + wing config |
entity_registry.py |
Entity code registry — maps names to AAAK codes, handles ambiguous names |
entity_detector.py |
Auto-detect people and projects from file content |
general_extractor.py |
Classifies text into 5 memory types (decision, preference, milestone, problem, emotional) |
room_detector_local.py |
Maps folders to room names using 70+ patterns — no API |
spellcheck.py |
Name-aware spellcheck — won't "correct" proper nouns in your entity registry |
split_mega_files.py |
Splits concatenated transcript files into per-session files |
Architecture
User → CLI → miner/convo_miner → ChromaDB (palace)
↕
knowledge_graph (SQLite)
↕
User → MCP Server → searcher → results
→ kg_query → entity facts
→ diary → agent journal
The palace (ChromaDB) stores verbatim content. The knowledge graph (SQLite) stores structured relationships. The MCP server exposes both to any AI tool.