Openclaw Fcma
6-tier AI memory system with 28 MCP tools — episodic memory, knowledge graphs, document storage, session ledger, auth & GDP 6-tier AI memory system with 28 MCP tools — episodic memory, knowledge graphs, document storage, session ledger, auth & GDPR complianceR compliance
Overview
🧠 OpenClaw FCMA
The Memory Layer That Makes AI Actually Remember
Quick Start • Architecture • Tools • API • Contributing
The Problem
Every time you start a new conversation with an AI, it forgets everything. Your name, your project, your preferences — all gone. You repeat yourself endlessly.
OpenClaw FCMA fixes this. It gives AI a persistent, structured memory — not just a bigger notepad, but a brain.
Architecture
┌─────────────────────────────────────────────────┐
│ L1 · Scratchpad · Working Memory (this turn) │
├─────────────────────────────────────────────────┤
│ L2 · Journal · Episodic Memory (FAISS) │
├─────────────────────────────────────────────────┤
│ L3 · Mind · Knowledge Graph (Neo4j) │
├─────────────────────────────────────────────────┤
│ L4 · Library · Your Documents (FAISS+SQL) │
├─────────────────────────────────────────────────┤
│ L5 · World State · Live Facts (Key-Value) │
├─────────────────────────────────────────────────┤
│ L6 · Ledger · Session Notes (.md files) │
└─────────────────────────────────────────────────┘
vs. Traditional RAG:
| Feature | RAG | OpenClaw FCMA |
|---|---|---|
| Structure | Flat vector search | 6-tier hierarchy |
| Relationships | None | Knowledge graph |
| Learning | Static | Surprise-driven |
| Documents | Vector chaos at 25+ PDFs | Parent-child indexing |
| User isolation | DIY | Built-in |
| GDPR | DIY | Built-in export + delete |
Quick Start
1. Install
git clone https://github.com/MohitDhawane/openclaw-fcma.git
cd openclaw-fcma
pip install -e ".[dev]"
2. Connect to Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"openclaw-fcma": {
"command": "python",
"args": ["-m", "openclaw.mcp_server"],
"cwd": "/path/to/openclaw-fcma"
}
}
}
3. Restart Claude Desktop
That's it. Claude now has persistent memory with 28 tools.
4. Try It
You: "Remember that my project uses Next.js 14 with PostgreSQL"
Claude: ✅ Stored in Journal + SQL. Total episodes: 1
--- [New conversation, days later] ---
You: "What stack am I using?"
Claude: 🔍 Your project uses Next.js 14 with PostgreSQL.
28 MCP Tools
L2 Journal — Episodic Memory
| Tool | Description |
|---|---|
memory_store | Store a memory episode |
memory_recall | Recall relevant memories by query |
L3 Mind — Knowledge Graph
| Tool | Description |
|---|---|
mind_add_node | Add concept/entity/action nodes |
mind_add_edge | Create relationships between nodes |
mind_query | Traverse the knowledge graph |
mind_decay | Apply forgetting curve (neural decay) |
mind_prune | Remove low-weight forgotten nodes |
L4 Library — Document Storage
| Tool | Description |
|---|---|
library_upload | Upload text documents |
library_search | Vector search across documents |
library_ingest | Full pipeline: chunk → embed → index |
library_smart_search | Two-stage retrieval with re-ranking |
L5 World State — Live Facts
| Tool | Description |
|---|---|
world_state_set | Set key-value pairs |
world_state_get | Get values by key |
L6 Ledger — Session Summaries
| Tool | Description |
|---|---|
ledger_save | Save session summary as .md file |
ledger_list | List saved session notes |
ledger_read | Read a specific session file |
ledger_context | Get recent context for session recovery |
Discovery
| Tool | Description |
|---|---|
discovery_investigate | Analyze hollow-focus tokens |
Knowledge Graph Viewer
| Tool | Description |
|---|---|
graph_view | View graph as markdown/mermaid/json/text |
graph_export | Export graph to file on disk |
User Management & Security
| Tool | Description |
|---|---|
user_register | Create account with API key |
user_data_export | Export all user data (GDPR) |
user_data_delete | Delete all user data (GDPR) |
db_stats | Database statistics |
audit_log | View data sovereignty audit trail |
auth_login | Exchange API key for JWT token |
auth_token_info | Verify and decode JWT token |
auth_rate_status | Check rate limit status |
API Usage
from openclaw.memory.controller import MemoryController
from openclaw.memory.journal import Journal
from openclaw.memory.library import Library
from openclaw.memory.vector_store import FAISSVectorStore
# Initialize
journal = Journal(backend=FAISSVectorStore())
mc = MemoryController(l2_journal=journal)
# Store memory
mc.update_memory(
query="User asked about Kubernetes",
response="Kubernetes orchestrates containers...",
surprise_score=0.85,
)
# Recall memory
context = mc.synthesize_context("Tell me about containers")
Data Storage
data/
├── openclaw.db ← SQLite (users, audit, document index)
├── journal/ ← FAISS vectors (episodic memory)
├── library/ ← FAISS vectors (document search)
├── ledger/ ← Session summaries (.md files)
│ └── {user_id}/
│ ├── 2026-02-14_Database_Upgrade.md
│ └── 2026-02-15_API_Design.md
└── graph_exports/ ← Knowledge graph exports
└── {user_id}/
└── graph_2026-02-15.md
Security
- JWT Authentication — 24-hour tokens with refresh and revocation
- API Keys — Auto-generated per user (
fcma_xxxx...) - Rate Limiting — Sliding window (60 req/min, configurable)
- GDPR Compliance — Full data export + cascade deletion + audit trail
- Password Hashing — Salted SHA-256
Environment Variables
| Variable | Default | Description |
|---|---|---|
DATABASE_URL | sqlite:///data/openclaw.db | Database connection string |
FCMA_JWT_SECRET | Random (dev) | JWT signing secret |
FCMA_API_KEY | None | API key for server auth |
FCMA_RATE_LIMIT | 60 | Requests per minute per user |
Development
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest tests/ -v
# Run MCP server (stdio)
python -m openclaw.mcp_server
# Run MCP server (HTTP)
python -m openclaw.mcp_server --transport http
# MCP Inspector (debug)
mcp dev openclaw/mcp_server.py
Project Structure
openclaw/
├── mcp_server.py ← MCP server (28 tools, 4 resources)
├── database.py ← SQL layer (SQLAlchemy)
├── auth.py ← JWT + rate limiting
├── memory/
│ ├── controller.py ← Memory orchestrator
│ ├── journal.py ← L2 episodic memory
│ ├── mind.py ← L3 knowledge graph
│ ├── library.py ← L4 document storage
│ ├── world_state.py ← L5 key-value state
│ ├── context_ledger.py ← L6 session summaries
│ ├── vector_store.py ← FAISS backend
│ ├── graph_viewer.py ← Graph export (4 formats)
│ ├── discovery.py ← Curiosity-driven exploration
│ ├── salience.py ← Surprise scoring
│ └── ingestion.py ← Document chunking pipeline
└── tests/
└── ...
Contributing
- Fork the repo
- Create a feature branch (
git checkout -b feature/amazing-feature) - Run tests (
pytest tests/ -v) - Commit your changes (
git commit -m 'Add amazing feature') - Push and open a Pull Request
License
MIT License — see LICENSE for details.
OpenClaw — Because AI should remember you.
Project Info
Created At
2 months agoUpdated At
2 months agoAuthor Name
MohitDhawaneStar
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