MCP ServersMcp Server Rabel
Created at a month ago
by jaspertvdm
Project description ๐ง 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 ๐ ๐ 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: โ Connected ๐ค 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. ๐ ๏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 ๐ 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" ๐๏ธ Architecture โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ RABEL โ โ Recidive Active Brain Environment Layer โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค 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 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Graceful Degradation RABEL works with minimal dependencies: Feature Without extras With [full] Text memories โ โ Text search โ (LIKE query) โ (semantic) Graph relations โ โ Soft pipelines โ โ Vector search โ โ Embeddings โ โ (Ollama) ๐ 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 ๐ 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 ๐ข Part of HumoticaOS RABEL is part of a larger ecosystem: Package Purpose Status mcp-server-tibet Trust & Provenance โ Available mcp-server-rabel Memory & Knowledge โ Available mcp-server-betti Complexity Management ๐ Coming ๐ Contact HumoticaOS Website: humotica.com GitHub: github.com/jaspertvdm ๐ License MIT License - One love, one fAmIly ๐ Built with love in Den Dolder, Netherlands By Jasper & Root AI - December 2025
Categories
Tags
mcp-server
ai-memory
local-first
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.
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