Memory Palace

A file-based long-term memory system for AI agents, designed to be zero-infrastructure (no Docker, no external services).

Architecture

palace/
├── grand_hall/          # Global navigation, room map, logs
├── chambers/            # Agent-specific knowledge (one per agent)
├── project_rooms/       # Long-running project storage
├── reflection_wing/     # Compiled insights: principles, kernels, patterns
├── dispatch_corridor/   # Task routing and status tracking
├── conflict_room/       # Conflict resolution records
└── archive_basement/    # Cold storage (excluded from default search)

Quick Start

# 1. Initialize palace structure
mkdir -p palace/{grand_hall,chambers,project_rooms,reflection_wing,dispatch_corridor,conflict_room,archive_basement}

# 2. Install BGE-m3
pip install FlagEmbedding

# 3. Build index
python3 scripts/build_index_bge.py --force

# 4. Query
python3 scripts/query_bge.py "your search query"
python3 scripts/query_bge.py --type palace --priority high "query"
python3 scripts/query_bge.py --details "query"   # show sub-scores
python3 scripts/query_bge.py --raw "query"       # pure cosine (v2 compatibility)

Search Scoring

Compound score = 0.5 × Semantic + 0.25 × Recency + 0.25 × Importance

• Semantic: BGE-m3 cosine similarity (clamped to [0,1])
• Recency: 30-day half-life decay based on file mtime
• Importance: priority field mapping (high=1.0, medium=0.6, low=0.3)

Key Scripts

Index Manifest Format

Each file in the manifest (watchdog_manifest_v1.jsonl) requires:

{"path": "palace/chambers/01_agent/accumulated_knowledge.md", "type": "palace", "priority": "high"}

Fields: path (required), type (for metadata filtering), priority (for importance scoring).

Memory Metabolism

See references/memory_metabolism.md for the full protocol:

• 3-tier upgrade path: Project Room → Reflection Wing → Constitution
• 5 output types: principle card, prompt kernel, failure pattern, thinking path, constitution candidate
• Reverse elimination rules for low-value content