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RABEL - Recidive Active Brain Environment Layer. Local-first AI memory with semantic search, graph relations, and soft pipelines. Now with Remote Mode for cross-machine AI communication!

Project description

๐Ÿง  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 ๐Ÿ’™


๐Ÿš€ 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: โœ“ Connected

๐Ÿค” What is RABEL?

RABEL gives AI assistants persistent memory that works 100% locally.

Before RABEL:
  AI: "Who is Storm?" โ†’ "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* โ†’ "Storm is Jasper's 7-year-old son!"

No cloud. No API keys. No data leaving your machine.


๐Ÿ› ๏ธ 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

๐Ÿ“– 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?")
# โ†’ Returns: "Jasper is the founder of HumoticaOS"

rabel_search(query="What handles trust?")
# โ†’ 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")
# โ†’ Jasper --father_of--> Storm

rabel_get_relations(predicate="part_of")
# โ†’ TIBET --part_of--> HumoticaOS
# โ†’ RABEL --part_of--> HumoticaOS

Soft Pipelines (Bilingual!)

# Get guidance in English
rabel_get_guidance(intent="solve_puzzle", lang="en")
# โ†’ "Puzzle: Read โ†’ Analyze โ†’ Attempt โ†’ Verify โ†’ Document"

# Get guidance in Dutch
rabel_get_guidance(intent="solve_puzzle", lang="nl")
# โ†’ "Puzzel: Lezen โ†’ Analyseren โ†’ Proberen โ†’ Verifiรซren โ†’ Documenteren"

# What's next?
rabel_next_step(intent="solve_puzzle", completed=["read", "analyze"])
# โ†’ Suggested next step: "attempt"

๐Ÿ—๏ธ Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                         RABEL                                โ”‚
โ”‚       Recidive Active Brain Environment Layer               โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                             โ”‚
โ”‚   Memory Layer     โ†’ Semantic facts with embeddings         โ”‚
โ”‚   Graph Layer      โ†’ Relations between entities             โ”‚
โ”‚   Soft Pipelines   โ†’ Guidance without enforcement (EN/NL)   โ”‚
โ”‚                                                             โ”‚
โ”‚   Storage: SQLite + sqlite-vec (optional)                   โ”‚
โ”‚   Embeddings: Ollama nomic-embed-text (optional)            โ”‚
โ”‚                                                             โ”‚
โ”‚   100% LOCAL - Zero cloud dependencies                      โ”‚
โ”‚                                                             โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Graceful Degradation

RABEL works with minimal dependencies:

Feature Without extras With [full]
Text memories โœ… โœ…
Text search โœ… (LIKE query) โœ… (semantic)
Graph relations โœ… โœ…
Soft pipelines โœ… โœ…
Vector search โŒ โœ…
Embeddings โŒ โœ… (Ollama)

๐ŸŒ 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

๐Ÿ™ 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

๐Ÿข Part of HumoticaOS

RABEL is part of a larger ecosystem:

Package Purpose Status
mcp-server-tibet Trust & Provenance โœ… Available
mcp-server-rabel Memory & Knowledge โœ… Available
mcp-server-betti Complexity Management ๐Ÿ”œ Coming

๐Ÿ“ž Contact

HumoticaOS


๐Ÿ“œ License

MIT License - One love, one fAmIly ๐Ÿ’™


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

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