---
title: System Components
description: Detailed breakdown of KGraph-MCP system components and their responsibilities
---

# 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:

```mermaid
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

## 🔗 **Related Documentation**

- [Architecture Overview](overview.md) - High-level system architecture
- [Knowledge Graph Design](knowledge-graph.md) - Graph schema and operations
- [Agent Framework](agents.md) - Agent coordination and communication
- [API Reference](../api/index.md) - Component APIs and interfaces