Mcp Server Rabel

🧠 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 💙

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🚀 Quick Start

Option 1: pip install (recommended)

# 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 -- mcp-server-rabel

# Verify
claude mcp list
# rabel: ✓ Connected

Option 2: Docker

# Build
docker build -t mcp-server-rabel .

# Run (MCP uses stdio)
docker run -i mcp-server-rabel

# With persistent data
docker run -i -v rabel-data:/data mcp-server-rabel

Option 3: From source

git clone https://github.com/jaspertvdm/RABEL.git
cd RABEL
pip install -e .
mcp-server-rabel

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🤔 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.

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🛠️ 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

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📖 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"

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🏗️ 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)

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🌍 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

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🙏 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

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🏢 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

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📞 Contact

HumoticaOS

• Website: humotica.com
• GitHub: github.com/jaspertvdm

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📜 License

AGPL-3.0 License - One love, one fAmIly 💙

This is open source software. If you use it in a network service, you must share your modifications.

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Built with love in Den Dolder, Netherlands By Jasper & Root AI - December 2025

Official Distribution

This package is officially distributed via:

• PyPI: https://pypi.org/project/RABEL/
• GitHub: https://github.com/jaspertvdm/RABEL

Note: Third-party directories may list this package but are not official or verified distribution channels for Humotica software.