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System Components

Detailed breakdown of KGraph-MCP's system components, their responsibilities, and how they interact to create an intelligent MCP orchestration platform.

🏗️ Component Architecture

KGraph-MCP is built with a layered architecture where each component has specific responsibilities:

graph TB
    subgraph "Presentation Layer"
        UI[Gradio Web UI]
        API[FastAPI REST API]
        WS[WebSocket Interface]
    end

    subgraph "Agent Layer"
        PA[Planner Agent]
        SA[Selector Agent]
        EA[Executor Agent]
        SV[Supervisor Agent]
    end

    subgraph "Knowledge Layer"
        KG[Knowledge Graph]
        ES[Embedding Service]
        RE[Reasoning Engine]
        QE[Query Engine]
    end

    subgraph "Integration Layer"
        MC[MCP Connectors]
        TR[Tool Registry]
        TM[Tool Manager]
    end

    subgraph "Data Layer"
        VDB[Vector Database]
        GDB[Graph Database]
        FS[File Storage]
    end

    UI --> API
    API --> PA
    PA --> SA
    SA --> EA
    EA --> SV

    PA --> KG
    SA --> ES
    EA --> TR
    SV --> RE

    KG --> GDB
    ES --> VDB
    TR --> MC
    QE --> KG

🔧 Core Components

1. Presentation Layer

Gradio Web UI

  • Purpose: Interactive web interface for user interactions
  • Responsibilities:
  • Collect user input and requirements
  • Display tool selection and execution results
  • Provide real-time feedback and status updates
  • Handle dynamic form generation based on tool requirements

FastAPI REST API

  • Purpose: RESTful API for programmatic access
  • Responsibilities:
  • Expose HTTP endpoints for all system functionality
  • Handle authentication and authorization
  • Provide OpenAPI documentation
  • Support webhook integrations

WebSocket Interface

  • Purpose: Real-time bidirectional communication
  • Responsibilities:
  • Stream execution progress updates
  • Handle real-time notifications
  • Support live collaboration features

2. Agent Layer

Planner Agent

  • Purpose: High-level task planning and decomposition
  • Responsibilities:
  • Analyze user requirements and goals
  • Break down complex tasks into manageable steps
  • Create execution plans with dependencies
  • Optimize task sequences for efficiency

Selector Agent

  • Purpose: Intelligent tool selection and routing
  • Responsibilities:
  • Query knowledge graph for available tools
  • Match requirements to tool capabilities
  • Rank tools based on suitability scores
  • Handle tool substitution and fallbacks

Executor Agent

  • Purpose: Tool execution and orchestration
  • Responsibilities:
  • Execute selected tools with proper parameters
  • Handle tool invocation protocols
  • Manage execution contexts and state
  • Coordinate multi-tool workflows

Supervisor Agent

  • Purpose: Quality assurance and monitoring
  • Responsibilities:
  • Monitor execution progress and health
  • Validate results and detect anomalies
  • Handle error recovery and retries
  • Ensure safety and compliance constraints

3. Knowledge Layer

Knowledge Graph

  • Purpose: Semantic representation of MCP ecosystem
  • Responsibilities:
  • Store tool metadata and relationships
  • Represent capabilities and requirements
  • Maintain ontology and schema definitions
  • Support complex semantic queries

Embedding Service

  • Purpose: Semantic similarity and search capabilities
  • Responsibilities:
  • Generate embeddings for tools and descriptions
  • Perform semantic similarity matching
  • Enable fuzzy search and discovery
  • Support multi-modal embedding types

Reasoning Engine

  • Purpose: Logical inference and decision making
  • Responsibilities:
  • Perform logical reasoning over knowledge graph
  • Infer implicit relationships and capabilities
  • Support rule-based decision making
  • Handle uncertainty and confidence scoring

Query Engine

  • Purpose: Efficient graph querying and traversal
  • Responsibilities:
  • Execute SPARQL and Cypher queries
  • Optimize query performance
  • Handle complex graph traversals
  • Support both synchronous and streaming queries

4. Integration Layer

MCP Connectors

  • Purpose: Protocol adapters for MCP servers
  • Responsibilities:
  • Implement MCP protocol communication
  • Handle protocol version compatibility
  • Manage connection lifecycle and health
  • Support various transport mechanisms

Tool Registry

  • Purpose: Central catalog of available tools
  • Responsibilities:
  • Discover and register MCP tools
  • Maintain tool metadata and documentation
  • Handle tool versioning and updates
  • Support plugin and extension mechanisms

Tool Manager

  • Purpose: Tool lifecycle and resource management
  • Responsibilities:
  • Manage tool instantiation and cleanup
  • Handle resource allocation and limits
  • Coordinate concurrent tool usage
  • Monitor tool performance and health

5. Data Layer

Vector Database

  • Purpose: High-performance similarity search
  • Responsibilities:
  • Store and index vector embeddings
  • Perform fast similarity searches
  • Support various distance metrics
  • Handle large-scale vector operations

Graph Database

  • Purpose: Native graph storage and querying
  • Responsibilities:
  • Store knowledge graph structure
  • Support complex graph queries
  • Maintain ACID properties
  • Handle graph updates and mutations

File Storage

  • Purpose: Persistent storage for artifacts
  • Responsibilities:
  • Store configuration files and schemas
  • Handle large binary assets
  • Support versioning and backup
  • Provide secure access controls

🔄 Component Interactions

Request Flow

  1. User Input → Gradio UI or FastAPI
  2. Planning → Planner Agent analyzes requirements
  3. Tool Selection → Selector Agent queries knowledge graph
  4. Execution → Executor Agent invokes selected tools
  5. Monitoring → Supervisor Agent validates results
  6. Response → Results returned to user interface

Data Flow

  1. Knowledge Ingestion → Tools registered in knowledge graph
  2. Embedding Generation → Semantic vectors created and indexed
  3. Query Processing → Graph queries retrieve relevant tools
  4. Execution Context → Tool parameters and state managed
  5. Result Processing → Outputs validated and formatted

📊 Component Metrics

Performance Characteristics

  • Planner Agent: ~100ms average planning time
  • Selector Agent: ~50ms tool selection latency
  • Executor Agent: Variable based on tool complexity
  • Knowledge Graph: Sub-second query response times
  • Embedding Service: ~10ms similarity search

Scalability Factors

  • Horizontal Scaling: Agent layer and API layer
  • Vertical Scaling: Database and storage layers
  • Load Balancing: Request distribution across instances
  • Caching: Multi-level caching for performance