Sponsored by Deepsite.site

Mcp Server Rabel

Created By
jaspertvdm3 months ago
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
Content

🧠 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

ToolDescription
rabel_helloTest if RABEL is working
rabel_add_memoryAdd a memory (fact, experience, knowledge)
rabel_searchSemantic search through memories
rabel_add_relationAdd graph relation (A --rel--> B)
rabel_get_relationsQuery the knowledge graph
rabel_get_guidanceGet soft pipeline hints (EN/NL)
rabel_next_stepWhat should I do next?
rabel_statsMemory 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:

FeatureWithout extrasWith [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:

PackagePurposeStatus
mcp-server-tibetTrust & Provenance✅ Available
mcp-server-rabelMemory & Knowledge✅ Available
mcp-server-bettiComplexity Management🔜 Coming

📞 Contact

HumoticaOS


📜 License

MIT License - One love, one fAmIly 💙


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

Server Config

{
  "mcpServers": {
    "rabel": {
      "command": "python",
      "args": [
        "-m",
        "mcp_server_rabel"
      ],
      "env": {}
    }
  }
}
Recommend Servers
TraeBuild with Free GPT-4.1 & Claude 3.7. Fully MCP-Ready.
Tavily Mcp
Serper MCP ServerA Serper MCP Server
Howtocook Mcp基于Anduin2017 / HowToCook (程序员在家做饭指南)的mcp server,帮你推荐菜谱、规划膳食,解决“今天吃什么“的世纪难题; Based on Anduin2017/HowToCook (Programmer's Guide to Cooking at Home), MCP Server helps you recommend recipes, plan meals, and solve the century old problem of "what to eat today"
ChatWiseThe second fastest AI chatbot™
CursorThe AI Code Editor
BlenderBlenderMCP connects Blender to Claude AI through the Model Context Protocol (MCP), allowing Claude to directly interact with and control Blender. This integration enables prompt assisted 3D modeling, scene creation, and manipulation.
Jina AI MCP ToolsA Model Context Protocol (MCP) server that integrates with Jina AI Search Foundation APIs.
MCP AdvisorMCP Advisor & Installation - Use the right MCP server for your needs
Zhipu Web SearchZhipu Web Search MCP Server is a search engine specifically designed for large models. It integrates four search engines, allowing users to flexibly compare and switch between them. Building upon the web crawling and ranking capabilities of traditional search engines, it enhances intent recognition capabilities, returning results more suitable for large model processing (such as webpage titles, URLs, summaries, site names, site icons, etc.). This helps AI applications achieve "dynamic knowledge acquisition" and "precise scenario adaptation" capabilities.
Y GuiA web-based graphical interface for AI chat interactions with support for multiple AI models and MCP (Model Context Protocol) servers.
AiimagemultistyleA Model Context Protocol (MCP) server for image generation and manipulation using fal.ai's Stable Diffusion model.
WindsurfThe new purpose-built IDE to harness magic
MiniMax MCPOfficial MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.
Playwright McpPlaywright MCP server
RedisA Model Context Protocol server that provides access to Redis databases. This server enables LLMs to interact with Redis key-value stores through a set of standardized tools.
Amap Maps高德地图官方 MCP Server
Visual Studio Code - Open Source ("Code - OSS")Visual Studio Code
DeepChatYour AI Partner on Desktop
EdgeOne Pages MCPAn MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.
Baidu Map百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。