- Mcp Server Rabel
Mcp Server Rabel
Project description
🧠 RABEL MCP Server
Recidive Active Brain Environment Layer
Local-first AI memory with semantic search, graph relations, and soft pipelines. Mem0 inspired, HumoticaOS evolved.
By Jasper & Root AI from HumoticaOS 💙
🚀 Quick Start
# Install
pip install mcp-server-rabel
# For full features (vector search)
pip install mcp-server-rabel[full]
# Add to Claude CLI
claude mcp add rabel -- python -m mcp_server_rabel
# Verify
claude mcp list
# rabel: ✓ Connected
🤔 What is RABEL?
RABEL gives AI assistants persistent memory that works 100% locally.
Before RABEL:
AI: "Who is Storm?" → "I don't know, you haven't told me"
After RABEL:
You: "Remember: Storm is Jasper's 7-year-old son"
AI: *saves to RABEL*
Later...
You: "Who is Storm?"
AI: *searches RABEL* → "Storm is Jasper's 7-year-old son!"
No cloud. No API keys. No data leaving your machine.
🛠️ Available Tools
Tool Description
rabel_hello Test if RABEL is working
rabel_add_memory Add a memory (fact, experience, knowledge)
rabel_search Semantic search through memories
rabel_add_relation Add graph relation (A --rel--> B)
rabel_get_relations Query the knowledge graph
rabel_get_guidance Get soft pipeline hints (EN/NL)
rabel_next_step What should I do next?
rabel_stats Memory statistics
📖 Examples
Adding Memories
# Remember facts
rabel_add_memory(content="Jasper is the founder of HumoticaOS", scope="user")
rabel_add_memory(content="TIBET handles trust and provenance", scope="team")
rabel_add_memory(content="Always validate input before processing", scope="agent")
Searching Memories
# Semantic search - ask questions naturally
rabel_search(query="Who founded HumoticaOS?")
# → Returns: "Jasper is the founder of HumoticaOS"
rabel_search(query="What handles trust?")
# → Returns: "TIBET handles trust and provenance"
Knowledge Graph
# Add relations
rabel_add_relation(subject="Jasper", predicate="father_of", object="Storm")
rabel_add_relation(subject="TIBET", predicate="part_of", object="HumoticaOS")
rabel_add_relation(subject="RABEL", predicate="part_of", object="HumoticaOS")
# Query relations
rabel_get_relations(subject="Jasper")
# → Jasper --father_of--> Storm
rabel_get_relations(predicate="part_of")
# → TIBET --part_of--> HumoticaOS
# → RABEL --part_of--> HumoticaOS
Soft Pipelines (Bilingual!)
# Get guidance in English
rabel_get_guidance(intent="solve_puzzle", lang="en")
# → "Puzzle: Read → Analyze → Attempt → Verify → Document"
# Get guidance in Dutch
rabel_get_guidance(intent="solve_puzzle", lang="nl")
# → "Puzzel: Lezen → Analyseren → Proberen → Verifiëren → Documenteren"
# What's next?
rabel_next_step(intent="solve_puzzle", completed=["read", "analyze"])
# → Suggested next step: "attempt"
🏗️ Architecture
┌─────────────────────────────────────────────────────────────┐
│ RABEL │
│ Recidive Active Brain Environment Layer │
├─────────────────────────────────────────────────────────────┤
│ │
│ Memory Layer → Semantic facts with embeddings │
│ Graph Layer → Relations between entities │
│ Soft Pipelines → Guidance without enforcement (EN/NL) │
│ │
│ Storage: SQLite + sqlite-vec (optional) │
│ Embeddings: Ollama nomic-embed-text (optional) │
│ │
│ 100% LOCAL - Zero cloud dependencies │
│ │
└─────────────────────────────────────────────────────────────┘
Graceful Degradation
RABEL works with minimal dependencies:
Feature Without extras With [full]
Text memories ✅ ✅
Text search ✅ (LIKE query) ✅ (semantic)
Graph relations ✅ ✅
Soft pipelines ✅ ✅
Vector search ❌ ✅
Embeddings ❌ ✅ (Ollama)
🌍 Philosophy
"LOKAAL EERST - het systeem MOET werken zonder internet"
(LOCAL FIRST - the system MUST work without internet)
RABEL is built on the belief that:
Your data stays yours - No cloud, no tracking, no API keys
Soft guidance beats hard rules - Pipelines suggest, not enforce
Bilingual by default - Dutch & English, more coming
Graceful degradation - Works with minimal deps, better with more
🙏 Credits
Inspired by: Mem0 - Thank you for the architecture insights!
We took their ideas and made them:
100% local-first
Bilingual (EN/NL)
With soft pipelines
With graph relations
🏢 Part of HumoticaOS
RABEL is part of a larger ecosystem:
Package Purpose Status
mcp-server-tibet Trust & Provenance ✅ Available
mcp-server-rabel Memory & Knowledge ✅ Available
mcp-server-betti Complexity Management 🔜 Coming
📞 Contact
HumoticaOS
Website: humotica.com
GitHub: github.com/jaspertvdm
📜 License
MIT License - One love, one fAmIly 💙
Built with love in Den Dolder, Netherlands By Jasper & Root AI - December 2025
Overview
What is RABEL?
RABEL is a local-first AI memory system that provides persistent memory for AI assistants, allowing them to remember facts and relationships without relying on cloud services.
How to use RABEL?
To use RABEL, install it via pip with pip install mcp-server-rabel, and for full features, use pip install mcp-server-rabel[full]. You can then add it to the Claude CLI and verify the connection.
Key features of RABEL?
- 100% local memory storage with no cloud dependencies.
- Semantic search capabilities for querying memories.
- Ability to add and manage graph relations between entities.
- Bilingual guidance in English and Dutch.
Use cases of RABEL?
- Storing personal information for AI assistants to recall later.
- Managing relationships and facts in a knowledge graph.
- Providing soft guidance for tasks in multiple languages.
FAQ from RABEL?
- Can RABEL work without internet?
Yes! RABEL is designed to function entirely offline.
- Is my data safe with RABEL?
Absolutely! RABEL keeps all data local and does not send any information to the cloud.
- What programming languages does RABEL support?
RABEL is primarily designed for Python.
Server Config
{
"mcpServers": {
"rabel": {
"command": "python",
"args": [
"-m",
"mcp_server_rabel"
],
"env": {}
}
}
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