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Brain CMS
by
Harreynish Gowtham
· GitHub ↗
· v1.0.0
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Install in OpenClaw
/install brain-cms
Description
Neuroscience-based multi-layer memory system for OpenClaw agents that improves context efficiency using semantic schemas, vector stores, and sleep cycle cons...
README (SKILL.md)
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)
# 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:
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/\x3Ctopic>.md - Add to INDEX.md with triggers + priority + cross-links
- Re-index:
memory_brain/.venv/bin/python3 memory_brain/index_memory.py
Sleep cycles:
# 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)
Usage Guidance
This skill appears to implement what it claims: a local, neuroscience-inspired memory system. Before installing, consider these practical steps: 1) Review the included install.py and the four brain_scripts (they're bundled in the skill) to confirm you understand the file changes. 2) Backup ~/.openclaw/workspace (or at least AGENTS.md and any existing memory/ directory) — the installer will create/copy files and append to AGENTS.md. 3) Ensure you want to run local Ollama: the code calls http://localhost:11434 for embeddings and generation and SKILL.md recommends pulling the nomic-embed-text and llama3.2:3b models; those model downloads happen via Ollama and pip package installs happen from PyPI. 4) If you do not run Ollama locally, embeddings/generation will fail but no secrets are leaked. 5) The installer uses shell commands (venv creation and pip install) and subprocess.run(shell=True); run only if you trust the source or after manually inspecting the scripts. The only notable metadata inconsistency is that registry 'Requirements' showed none while SKILL.md declares required binaries (python3, ollama) — prefer the SKILL.md requirements when preparing your environment.
Capability Analysis
Type: OpenClaw Skill
Name: brain-cms
Version: 1.0.0
The skill is classified as suspicious primarily due to the use of `subprocess.run(..., shell=True)` in `install.py`. While the commands executed are hardcoded and appear benign (creating a Python virtual environment and installing dependencies), the `shell=True` argument introduces a shell injection vulnerability. If an attacker could manipulate environment variables (e.g., PATH) or control the interpretation of the command string, it could lead to arbitrary code execution. All other scripts and the SKILL.md instructions appear to align with the stated purpose of a memory system, making network calls only to a local Ollama instance and operating within the expected OpenClaw workspace.
Capability Assessment
Purpose & Capability
Overall coherent: the code, installer, and SKILL.md all implement a local multi-layer memory system that indexes .md schema files, runs semantic search, and performs NREM/REM consolidation using local Ollama and a LanceDB vectorstore. One minor inconsistency: the registry-level 'Requirements' block shown to the scanner lists no required binaries, whereas the SKILL.md metadata requires 'python3' and 'ollama' — the latter is necessary for embeddings/LLM work and is consistent with the scripts.
Instruction Scope
Instructions and scripts stay within the claimed scope: they read/write files under ~/.openclaw/workspace (memory/, memory_brain/), create INDEX.md and ANCHORS.md, index schemas, run semantic queries, compress logs, and append REM updates to schema files. They do modify AGENTS.md (append CMS instructions) and write to workspace files — expected for this installer but worth noting because it changes workspace state.
Install Mechanism
No high-risk remote downloads or obscure URLs. The installer creates a venv and pip-installs lancedb, numpy, pyarrow, requests (PyPI), and SKILL.md suggests using 'ollama pull' to fetch models (standard Ollama behavior). All installs are local and traceable; the installer uses subprocess.run(shell=True) for convenience, which is normal for local installers but means you should inspect the script before running.
Credentials
No environment variables or external credentials are requested. Runtime network usage is limited to a local Ollama HTTP endpoint (http://localhost:11434) for embeddings and generation, and to standard package downloads (pip/ollama). The scripts do not attempt to exfiltrate data to remote endpoints.
Persistence & Privilege
The skill is not force-included (always:false) and runs only when invoked. It persists files under ~/.openclaw/workspace (creates memory/ and memory_brain/), copies scripts there, and appends to AGENTS.md — normal for an installer but it does alter workspace files and should be allowed only if you accept those changes.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install brain-cms - After installation, invoke the skill by name or use
/brain-cms - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release — neuroscience-inspired CMS for OpenClaw
Metadata
Frequently Asked Questions
What is Brain CMS?
Neuroscience-based multi-layer memory system for OpenClaw agents that improves context efficiency using semantic schemas, vector stores, and sleep cycle cons... It is an AI Agent Skill for Claude Code / OpenClaw, with 637 downloads so far.
How do I install Brain CMS?
Run "/install brain-cms" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Brain CMS free?
Yes, Brain CMS is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Brain CMS support?
Brain CMS is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Brain CMS?
It is built and maintained by Harreynish Gowtham (@harrey401); the current version is v1.0.0.
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