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KGraph-MCP: The Self-Orchestrating Tool Network¶

  • Knowledge Graph Powered

    Semantic representation of MCP primitives (Tools, Prompts, Resources, Roots, Sampling) enabling intelligent tool discovery and orchestration.

    Learn about our Architecture

  • AI Agent Framework

    Autonomous agents (Planner, Selector, Executor, Supervisor) that understand goals, discover tools, and execute complex workflows safely.

    Explore Agent Framework

  • Quick Start

    Get KGraph-MCP running in minutes with our comprehensive setup guide and development environment automation.

    Get Started

  • MCP Integration

    Native integration with Model Context Protocol servers for seamless tool orchestration and dynamic reasoning capabilities.

    View MCP Integration

Project Vision¶

The Challenge: As AI agents become more capable, they need access to a vast and diverse array of tools (MCP Servers) to interact with the world and perform complex tasks. However, managing, discovering, and orchestrating thousands of such tools is a significant challenge.

Our Solution: KGraph-MCP is an intelligent orchestration layer that uses a Knowledge Graph to semantically represent and manage MCP primitives, combined with AI Agents that can understand user goals and autonomously execute complex workflows.

Innovation with MCP

Our project innovates by treating MCP primitives as first-class citizens within a queryable, semantic knowledge graph. This allows agents to move beyond simple tool invocation to sophisticated understanding of what tools exist, how to use them, what data they need, and how they can reason.

Current Development Status¶

  • Enterprise-Grade Development Environment: Python 3.11.8 with uv, comprehensive tooling
  • Autonomous Project Management: Claude 4.0 PM with TaskMaster system
  • Quality Assurance Pipeline: Black, Ruff, MyPy, comprehensive testing
  • Application Framework: FastAPI + Gradio integrated platform
  • Knowledge Graph Core: Semantic MCP primitive representation
  • Agent Framework: Planner, Selector, Executor, Supervisor agents
  • Tool Discovery: Dynamic MCP server discovery and registration
  • Workflow Orchestration: Complex multi-tool workflow execution
  • Dynamic Provisioning: Automatic MCP server deployment
  • Cost & Compliance: Resource optimization and policy enforcement
  • Self-Improvement: Learning from feedback and new data
  • Production Deployment: Enterprise-ready orchestration platform

Architecture Overview¶

graph TB
    subgraph "User Interface"
        UI[Gradio UI]
        API[FastAPI Backend]
    end

    subgraph "AI Agent Framework"
        PA[Planner Agent]
        SA[Selector Agent]
        EA[Executor Agent]
        SUA[Supervisor Agent]
    end

    subgraph "Knowledge Graph Core"
        KG[(Knowledge Graph)]
        Tools[Tools]
        Prompts[Prompts]
        Resources[Resources]
        Roots[Roots]
    end

    subgraph "MCP Ecosystem"
        MCP1[MCP Server 1]
        MCP2[MCP Server 2]
        MCP3[MCP Server N...]
    end

    UI --> API
    API --> PA
    PA --> SA
    SA --> KG
    SA --> EA
    EA --> SUA
    EA --> MCP1
    EA --> MCP2
    EA --> MCP3

    KG --> Tools
    KG --> Prompts
    KG --> Resources
    KG --> Roots

Key Features¶

🧠 Intelligent Tool Discovery¶

  • Semantic search across MCP tools and capabilities
  • Context-aware tool recommendation
  • Dependency resolution and workflow planning

🤖 Autonomous Execution¶

  • Natural language goal interpretation
  • Multi-step workflow orchestration
  • Error handling and recovery mechanisms

🔒 Safe & Secure¶

  • Sandboxed execution environments
  • Resource boundary enforcement (Roots)
  • Comprehensive audit logging

📊 Development Excellence¶

  • 100% Type Safety: Full MyPy strict mode compliance
  • Comprehensive Testing: 80%+ code coverage requirements
  • AI-Assisted Development: Claude 4.0 autonomous project management
  • Quality Automation: 30+ justfile commands for workflow automation

Technology Stack¶

  • Core Platform

    • Python 3.11.8 with uv package management
    • FastAPI for high-performance API backend
    • Gradio for interactive web interfaces
    • SQLAlchemy 2.1+ for data persistence

  • AI & Knowledge

    • Neo4j for Knowledge Graph storage
    • Qdrant for vector embeddings
    • OpenAI/Azure OpenAI for LLM inference
    • Transformers for local model support

  • Development & Quality

    • Black 25.1 + Ruff for code quality
    • MyPy strict mode for type safety
    • Pytest with comprehensive coverage
    • Pre-commit hooks for quality gates

  • Orchestration & Deployment

    • Docker for containerization
    • GitHub Actions for CI/CD
    • Kubernetes for production deployment
    • Modal Labs for serverless compute

Quick Navigation¶

Getting Started¶

Ready to explore KGraph-MCP? Here's how to get started:

  1. Installation Guide - Set up your development environment
  2. Quick Start Tutorial - Build your first workflow
  3. Architecture Overview - Understand the system design
  4. Developer Guide - Contributing to the project

Enterprise Ready

KGraph-MCP is built with enterprise-grade standards from day one, featuring comprehensive testing, security scanning, type safety, and automated quality assurance.

Interested in contributing? Check out our Contributing Guide or explore our Active Tasks.