Project Architecture

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This document provides a comprehensive overview of the AICollaborator project architecture, including its structure, components, design patterns, and integration points.

Table of Contents

1. Project Structure
2. Core Components
3. Design Patterns and Architectural Decisions
4. Module Organization
5. Integration Points
6. System Architecture
7. Data Flow
8. Machine-Readable Architecture

Project Structure

The AICollaborator project follows a modular architecture with clear separation of concerns:

AICollaborator/
├── Sources/
│   ├── AICollaboratorApp/        # Main application code
│   │   ├── Core/                 # Core functionality
│   │   │   ├── AICollaborator.swift
│   │   │   ├── AIContext.swift
│   │   │   ├── AITask.swift
│   │   │   └── Protocols/        # Core protocols
│   │   │
│   │   ├── Agents/               # Agent implementations
│   │   │   ├── CodeAgent/
│   │   │   ├── AnalysisAgent/
│   │   │   └── OrchestratorAgent/
│   │   │
│   │   ├── Tasks/                # Task definitions
│   │   │   ├── CodeGenerationTask.swift
│   │   │   ├── AnalysisTask.swift
│   │   │   └── ProblemSolvingTask.swift
│   │   │
│   │   ├── Models/               # Data models
│   │   │   ├── AICapability.swift
│   │   │   ├── AICredentials.swift
│   │   │   └── AITaskResult.swift
│   │   │
│   │   ├── Services/             # Services and utilities
│   │   │   ├── NetworkService.swift
│   │   │   ├── GitHubService.swift
│   │   │   └── LoggingService.swift
│   │   │
│   │   └── Utils/                # Utility functions
│   │       ├── Extensions/
│   │       └── Helpers/
│   │
│   └── Resources/                # Resource files
│       ├── assets/
│       └── configs/
│
├── Tests/                        # Test suite
│   ├── UnitTests/
│   ├── IntegrationTests/
│   └── TestResources/
│
├── Documentation/                # Detailed documentation
├── Examples/                     # Example implementations
├── tools/                        # Development tools
└── .github/                      # GitHub configuration

Core Components

The AICollaborator framework consists of several core components that work together:

1. AICollaborator

The main entry point and orchestrator for the framework. It manages agent registration, task routing, and context tracking.

2. AITask

Represents tasks that can be executed by AI agents. Different task types inherit from this base protocol.

3. AIAgent

Defines the interface for AI agents, including methods for processing tasks and declaring capabilities.

4. AIContext

Manages contextual information and conversation history for AI agent interactions.

5. Services

Provides specialized functionality like network operations, GitHub integration, and logging.

Core Component Relationships

              ┌─────────────┐
              │             │
              │AICollaborator│
              │             │
              └───────┬─────┘
                      │
                      │ manages
                      │
        ┌─────────────┼─────────────┐
        │             │             │
┌───────▼───────┐ ┌───▼───┐  ┌─────▼─────┐
│    AIAgent    │ │AITask │  │ AIContext │
└───────┬───────┘ └───────┘  └───────────┘
        │
        │ uses
        │
┌───────▼───────┐
│   Services    │
└───────────────┘

Design Patterns and Architectural Decisions

AICollaborator leverages several design patterns to ensure maintainability, extensibility, and testability:

Protocol-Oriented Design

The framework extensively uses protocols to define interfaces, enabling loose coupling and facilitating testing.

Key protocols include:

• AICollaboratorAgent: Interface for AI agents
• TaskExecutable: Protocol for task execution
• ContextAware: Protocol for context-aware components
• HumanInteractive: Protocol for human interaction capabilities

Dependency Injection

Services and dependencies are injected rather than created directly, facilitating testing and flexible configuration.

class AICollaborator {
    private let networkService: NetworkServiceProtocol
    private let githubService: GitHubServiceProtocol
    
    init(networkService: NetworkServiceProtocol = NetworkService(),
         githubService: GitHubServiceProtocol = GitHubService()) {
        self.networkService = networkService
        self.githubService = githubService
    }
}

Observer Pattern

The framework uses observers for event notification, such as task status changes or context updates.

protocol TaskObserver: AnyObject {
    func taskDidUpdate(_ task: AITask)
}

Command Pattern

Tasks are implemented as command objects that encapsulate all information needed for execution.

struct CodeGenerationTask: AITask {
    let id: UUID
    let description: String
    let constraints: [String]
    let priority: TaskPriority
    
    func execute(using agent: AICollaboratorAgent) -> AITaskResult {
        // Task execution logic
    }
}

Factory Pattern

Factories are used to create and configure complex objects, particularly for agent creation.

class AIAgentFactory {
    func createAgent(type: AIAgentType, config: AgentConfig) -> AICollaboratorAgent {
        // Agent creation logic
    }
}

Module Organization

The AICollaborator framework is organized into logical modules:

Core Module

Contains the essential components and protocols of the framework.

Responsibilities:

• Define core interfaces
• Manage agent registration
• Route tasks to appropriate agents
• Maintain context

Agents Module

Contains implementations of various AI agents.

Responsibilities:

• Implement agent-specific logic
• Process tasks based on capabilities
• Manage agent state
• Interact with external AI services

Tasks Module

Contains task definitions and execution logic.

Responsibilities:

• Define task interfaces
• Implement task-specific validation
• Handle task results
• Track task status

Services Module

Contains services for external integrations.

Responsibilities:

• GitHub repository integration
• Network operations
• AI model communication
• Logging and monitoring

Integration Points

AICollaborator provides several integration points for extensibility:

1. AI Model Integration

The framework can connect to different AI models through adapter interfaces:

protocol AIModelAdapter {
    func connect(credentials: AICredentials) -> Bool
    func processPrompt(_ prompt: String) -> AIResponse
    func supportedCapabilities() -> [AICapability]
}

2. GitHub Integration

Integrate with GitHub repositories for code analysis and generation:

protocol GitHubServiceProtocol {
    func fetchRepository(owner: String, repo: String) -> Result<Repository, Error>
    func createPullRequest(title: String, body: String, changes: [FileChange]) -> Result<PullRequest, Error>
    func fetchIssues(state: IssueState) -> Result<[Issue], Error>
}

3. Plugin System

The framework includes a plugin architecture for extending functionality:

protocol AICollaboratorPlugin {
    var id: String { get }
    var name: String { get }
    var version: String { get }
    
    func initialize(collaborator: AICollaborator)
    func shutdown()
}

4. Custom Agent Integration

Developers can create custom agents by implementing the AICollaboratorAgent protocol:

struct CustomAgent: AICollaboratorAgent {
    func processTask(_ task: AITask) -> AITaskResult {
        // Custom implementation
    }
    
    func provideCapabilities() -> [AICapability] {
        return [.codeGeneration, .textAnalysis]
    }
}

System Architecture

The AICollaborator follows a layered architecture:

1. Presentation Layer

Handles user interaction, including:

• Command-line interface
• GUI (if applicable)
• API endpoints

2. Application Layer

Contains the core logic of the framework:

• Task management
• Agent orchestration
• Context tracking

3. Domain Layer

Defines the core business logic:

• Task execution
• Agent capabilities
• Collaboration protocols

4. Infrastructure Layer

Handles external dependencies:

• AI model integration
• GitHub communication
• Persistence
• Logging

Data Flow

Task Execution Flow

1. User or System → Creates Task
2. AICollaborator → Routes Task to appropriate Agent
3. Agent → Processes Task
   a. Retrieves Context if needed
   b. Communicates with AI Model if needed
   c. Accesses GitHub if needed
4. Agent → Returns Result to AICollaborator
5. AICollaborator → Updates Context with Result
6. AICollaborator → Returns Result to User or System

Agent Registration Flow

1. Agent → Registers with AICollaborator
2. AICollaborator → Records Agent capabilities
3. AICollaborator → Assigns unique identifier
4. Agent → Ready to receive tasks

Context Update Flow

1. Entity → Requests Context update
2. AIContext → Validates update
3. AIContext → Stores information
4. AIContext → Notifies interested observers

Machine-Readable Architecture

{
  "architecture_type": "project_structure",
  "version": "0.1.0",
  "components": [
    {
      "name": "AICollaborator",
      "type": "class",
      "purpose": "Main orchestrator",
      "relationships": [
        {"type": "manages", "target": "AIAgent"},
        {"type": "routes", "target": "AITask"},
        {"type": "maintains", "target": "AIContext"}
      ]
    },
    {
      "name": "AIAgent",
      "type": "protocol",
      "purpose": "Agent interface",
      "implementations": [
        "CodeAgent",
        "AnalysisAgent",
        "OrchestratorAgent"
      ],
      "relationships": [
        {"type": "processes", "target": "AITask"},
        {"type": "uses", "target": "Services"}
      ]
    },
    {
      "name": "AITask",
      "type": "protocol",
      "purpose": "Task representation",
      "implementations": [
        "CodeGenerationTask",
        "AnalysisTask",
        "ProblemSolvingTask"
      ]
    },
    {
      "name": "AIContext",
      "type": "class",
      "purpose": "Context management",
      "methods": [
        "store",
        "retrieve",
        "update",
        "clear"
      ]
    },
    {
      "name": "Services",
      "type": "module",
      "components": [
        "NetworkService",
        "GitHubService",
        "LoggingService"
      ]
    }
  ],
  "design_patterns": [
    {
      "name": "Protocol-Oriented Design",
      "purpose": "Define interfaces and enable loose coupling"
    },
    {
      "name": "Dependency Injection",
      "purpose": "Facilitate testing and flexible configuration"
    },
    {
      "name": "Observer Pattern",
      "purpose": "Event notification for task status and context updates"
    },
    {
      "name": "Command Pattern",
      "purpose": "Encapsulate task information"
    },
    {
      "name": "Factory Pattern",
      "purpose": "Create and configure complex objects"
    }
  ],
  "integration_points": [
    {
      "name": "AI Model Integration",
      "interface": "AIModelAdapter",
      "purpose": "Connect to different AI models"
    },
    {
      "name": "GitHub Integration",
      "interface": "GitHubServiceProtocol",
      "purpose": "Interact with GitHub repositories"
    },
    {
      "name": "Plugin System",
      "interface": "AICollaboratorPlugin",
      "purpose": "Extend functionality"
    },
    {
      "name": "Custom Agent Integration",
      "interface": "AICollaboratorAgent",
      "purpose": "Create specialized agents"
    }
  ],
  "architecture_layers": [
    {
      "name": "Presentation Layer",
      "components": ["CLI", "GUI", "API"]
    },
    {
      "name": "Application Layer",
      "components": ["Task Management", "Agent Orchestration", "Context Tracking"]
    },
    {
      "name": "Domain Layer",
      "components": ["Task Execution", "Agent Capabilities", "Collaboration Protocols"]
    },
    {
      "name": "Infrastructure Layer",
      "components": ["AI Model Integration", "GitHub Communication", "Persistence", "Logging"]
    }
  ]
}

Additional Resources

• API Reference - Detailed API documentation
• Development Guidelines - Coding standards and best practices
• AI Agent Integration Guide - Guide for integrating custom AI agents
• Human-AI Collaboration - Patterns for human-AI teamwork