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Brain Cms

Continuum Memory System (CMS) for OpenClaw agents.

Rating: 3.9 (408 reviews)
Downloads: 16,018 downloads
Version: 1.0.0

Overview

Continuum Memory System (CMS) for OpenClaw agents.

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Brain CMS 🧠

A neuroscience-inspired memory architecture for OpenClaw agents. Replaces flat file injection with sparse, semantic, frequency-gated memory loading.

What This Installs

text

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:

Create memory/.md
Add to INDEX.md with triggers + priority + cross-links
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:

text

[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)

Installation

Terminal bash


openclaw install brain-cms

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💻Code Examples

.venv/bin/python3 query_memory.py "your topic here" --sources-only

venvbinpython3-querymemorypy-your-topic-here---sources-only.txt

## 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:

memory_brain/.venv/bin/python3 memory_brain/query_memory.py "message text" --sources-only

memorybrainvenvbinpython3-memorybrainquerymemorypy-message-text---sources-only.txt

**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:**

cd ~/.openclaw/workspace/memory_brain && .venv/bin/python3 rem.py

cd-openclawworkspacememorybrain--venvbinpython3-rempy.txt

## 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:

example.txt

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)

example.sh

# 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

example.sh

# 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