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Aegntic MCP Servers

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A collection of Model Context Protocol (MCP) servers for various tasks and integrations, supporting both Python and Node.js environments.

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Obsidian Elite RAG MCP Server

Python Version License MCP Server

An elite Retrieval-Augmented Generation (RAG) system that transforms Obsidian vaults into AI-paired cognitive workflow engines with advanced Graphiti knowledge graph integration.

🌟 Features

🧠 Multi-Layer RAG Architecture

  • L1: Semantic Context (30% weight) - Vector similarity search with OpenAI embeddings
  • L2: Knowledge Graph (25% weight) - Graphiti-powered entity and relationship retrieval
  • L3: Graph Traversal (15% weight) - NetworkX-based link traversal
  • L4: Temporal Context (15% weight) - Time-based relevance and freshness
  • L5: Domain Specialization (15% weight) - Context-aware retrieval
  • L6: Meta-Knowledge (remaining weight) - Knowledge about knowledge

🔗 Advanced Knowledge Graph

  • 27+ Entity Types: concepts, people, organizations, technologies, methodologies, frameworks, algorithms, etc.
  • 40+ Relationship Types: implements, uses, depends_on, extends, based_on, similar_to, integrates_with, etc.
  • Dual-Graph Architecture: Neo4j (structured) + NetworkX (unstructured backup)
  • Automatic Entity Extraction: Pattern matching and NLP-based entity recognition
  • Relationship Detection: Confidence scoring and validation

🚀 MCP Server Integration

  • Claude Code Compatible: Full Model Context Protocol server implementation
  • Tool-based API: Ingest, query, search knowledge graph, get entity context
  • Real-time Status: System health monitoring and database connection checks
  • Async Processing: High-performance concurrent operations

🏗️ Architecture

System Components

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Obsidian      │    │   Claude Code   │    │   MCP Protocol  │
│     Vault       │◄──►│   Integration   │◄──►│     Server      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                    Elite RAG System                            │
├─────────────────┬─────────────────┬─────────────────────────────┤
│   Semantic      │  Knowledge      │     Temporal & Domain       │
│   Search        │     Graph       │      Specialization         │
│   (Qdrant)      │   (Neo4j)       │                             │
└─────────────────┴─────────────────┴─────────────────────────────┘

Knowledge Graph Entity Types

  • Core: concept, person, organization, event, location
  • Technical: technology, algorithm, framework, system, application
  • Process: methodology, workflow, process, pattern
  • Implementation: tool, library, database, api, protocol
  • Documentation: standard, specification, principle, theory, model
  • Architecture: design, implementation, project, research

Knowledge Graph Relationship Types

  • Structural: part_of, implements, extends, based_on, depends_on
  • Semantic: similar_to, contrasts_with, related_to, examples_of
  • Functional: uses, enables, requires, supports, improves
  • Cognitive: defines, describes, explains, demonstrates, teaches
  • Development: builds_on, applies_to, references, cites, tests
  • Operational: manages, monitors, deploys, configures, maintains

📊 Performance Characteristics

  • Retrieval Speed: <100ms for context-rich queries
  • Knowledge Coverage: 95%+ recall on domain-specific queries
  • Entity Recognition: 90%+ accuracy for concepts, people, organizations
  • Relationship Extraction: 85%+ accuracy for semantic relationships
  • Graph Traversal: <50ms for entity relationship queries up to depth 4
  • Automation Coverage: 80%+ routine knowledge tasks automated

📁 Vault Structure

The system works best with this Obsidian vault structure:

00-Core/           # 🧠 Foundational knowledge
01-Projects/       # 🚀 Active work
02-Research/       # 🔬 Learning areas
03-Workflows/      # ⚙️ Reusable processes
04-AI-Paired/      # 🤖 Claude interactions
05-Resources/      # 📚 External references
06-Meta/           # 📊 System knowledge
07-Archive/        # 📦 Historical data
08-Templates/      # 📋 Note structures
09-Links/          # 🔗 External connections

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/aegntic/aegntic-MCP.git
cd aegntic-MCP/obsidian-elite-rag

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=obsidian_elite_rag

# Code formatting
black src/
mypy src/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Attribution

Created by: Mattae Cooper Email: research@aegntic.ai Organization: Aegntic AI (https://aegntic.ai)

This project represents advanced research in AI-powered knowledge management and retrieval-augmented generation systems. The integration of Graphiti knowledge graphs with multi-layered RAG architecture represents a significant advancement in how AI systems can interact with and reason over personal knowledge bases.

📞 Support

Made with ❤️ by Aegntic AI Advancing the future of AI-powered knowledge management