What this skill does

--- name: brain-cms description: Continuum Memory System (CMS) for OpenClaw agents. Replaces flat MEMORY.md with a brain-inspired multi-layer memory architecture — semantic schemas, a hippocampal router (INDEX.md), vector store (LanceDB + nomic-embed-text), and automated NREM/REM sleep cycles for consolidation. Based on neuroscience research (LTP, spreading activation, CMS theory). Use when setting up persistent agent memory, improving context efficiency, or reducing token cost on long-running agents. Triggers: brain, memory system, CMS, long-term memory, vector store, sleep cycle, NREM, REM, memory architecture, semantic memory, context efficiency. metadata: openclaw: emoji: 🧠 requires: bins: ["python3", "ollama"] install: - id: python-deps kind: shell label: "Install Python dependencies" command: "cd ~/.openclaw/workspace/memory_brain && python3 -m venv .venv && .venv/bin/pip install lancedb numpy pyarrow requests --quiet" - id: ollama-models kind: shell label: "Pull Ollama models (nomic-embed-text + llama3.2:3b)" command: "ollama pull nomic-embed-text && ollama pull llama3.2:3b" --- # Brain CMS 🧠 A neuroscience-inspired memory architecture for OpenClaw agents. Replaces flat file injection with sparse, semantic, frequency-gated memory loading. ## What This Installs ``` memory/ ├── INDEX.md ← Hippocampus: topic router + cross-links ├── ANCHORS.md ← Permanent high-significance event store └── schemas/ ← Domain-specific semantic schemas (you create these) memory_brain/ ├── index_memory.py ← Embeds schemas into LanceDB vector store ├── query_memory.py ← Semantic similarity search ├── nrem.py ← NREM sleep cycle (compression + anchor promotion) ├── rem.py ← REM sleep cycle (LLM consolidation via Ollama) └── vectorstore/ ← LanceDB database (auto-created) ``` ## Setup (one-time) ```bash # 1. Run the installer python3 ~/.openclaw/workspace/skills/brain-cms/install.py # 2. Index your schemas cd ~/.openclaw/workspace/memory_brain .venv/bin/python3 index_memory.py # 3. Test retrieval .venv/bin/python3 query_memory.py "your topic here" --sources-only ``` ## How It Works **Boot sequence:** Load MEMORY.md (lean core) + today's daily log. Nothing else. **When a topic appears:** Read `memory/INDEX.md` → load only the relevant schemas (spreading activation). Check `memory/ANCHORS.md` for high-significance events. **For ambiguous topics:** Run semantic search: ```bash memory_brain/.venv/bin/python3 memory_brain/query_memory.py "message text" --sources-only ``` **Auto-schema creation:** When a new significant project or domain appears: 1. Create `memory/<topic>.md` 2. Add to INDEX.md with triggers + priority + cross-links 3. Re-index: `memory_brain/.venv/bin/python3 memory_brain/index_memory.py` **Sleep cycles:** ```bash # NREM — run on shutdown (~30s, no LLM) cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 nrem.py # REM — run weekly (2-5 min, uses local llama3.2:3b, free) cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 rem.py ``` ## Memory Layers (CMS) | Layer | Files | When loaded | Purpose | |-------|-------|-------------|---------| | Working | `MEMORY.md` + today log | Every session | Core context | | Episodic | `memory/YYYY-MM-DD.md` | Session boot | Recent events | | Semantic | `memory/*.md` schemas | On trigger | Domain knowledge | | Anchors | `memory/ANCHORS.md` | On CRITICAL topics | Permanent ground truth | | Vector | `memory_brain/vectorstore/` | On demand | Semantic search | ## Tagging Anchors In any daily log, tag high-significance events: ``` [ANCHOR] Major demo success — full pipeline working end-to-end ``` NREM auto-promotes these to ANCHORS.md on next shutdown. ## Token Savings Typical MEMORY.md: 150-300 lines injected every session. With Brain CMS: ~50-line core + schemas loaded only when relevant. Estimated savings: 40-60% reduction in context tokens per session. ## Requirements - Python 3.10+ - Ollama (for embeddings + REM consolidation) - 500MB+ storage for vector store and models - `lancedb`, `numpy`, `pyarrow`, `requests` (auto-installed)