hive/PROJECT_PLAN.md

🐝 Hive: Unified Distributed AI Orchestration Platform

Project Overview

Hive is a comprehensive distributed AI orchestration platform that consolidates the best components from our distributed AI development ecosystem into a single, powerful system for coordinating AI agents, managing workflows, and monitoring cluster performance.

🎯 Vision Statement

Create a unified platform that combines:

• Distributed AI Development coordination and monitoring
• Visual Workflow Orchestration with n8n compatibility
• Multi-Agent Task Distribution across specialized AI agents
• Real-time Performance Monitoring and alerting
• MCP Integration for standardized AI tool protocols

🏗️ System Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        HIVE ORCHESTRATOR                        │
├─────────────────────────────────────────────────────────────────┤
│  Frontend Dashboard (React + TypeScript)                       │
│  ├── 🎛️  Agent Management & Monitoring                         │
│  ├── 🎨  Visual Workflow Editor (n8n-compatible)               │
│  ├── 📊  Real-time Performance Dashboard                       │
│  ├── 📋  Task Queue & Project Management                       │
│  └── ⚙️   System Configuration & Settings                      │
├─────────────────────────────────────────────────────────────────┤
│  Backend Services (FastAPI + Python)                           │
│  ├── 🧠  Hive Coordinator (unified orchestration)              │
│  ├── 🔄  Workflow Engine (n8n + MCP bridge)                    │
│  ├── 📡  Agent Communication (compressed protocols)            │
│  ├── 📈  Performance Monitor (metrics & alerts)                │
│  ├── 🔒  Authentication & Authorization                        │
│  └── 💾  Data Storage (workflows, configs, metrics)           │
├─────────────────────────────────────────────────────────────────┤
│  Agent Network (Ollama + Specialized Models)                   │
│  ├── 🏗️  ACACIA (Infrastructure & DevOps)                     │
│  ├── 🌐  WALNUT (Full-Stack Development)                       │
│  ├── ⚙️   IRONWOOD (Backend & Optimization)                    │
│  └── 🔌  [Expandable Agent Pool]                               │
└─────────────────────────────────────────────────────────────────┘

📦 Component Integration Plan

🔧 Core Components from Existing Projects

1. From distributed-ai-dev

• AIDevCoordinator: Task orchestration and agent management
• Agent Configuration: YAML-based agent profiles and capabilities
• Performance Monitoring: Real-time metrics and GPU monitoring
• Claudette Compression: Efficient agent communication protocols
• Quality Control: Multi-agent code review and validation

2. From McPlan

• Visual Workflow Editor: React Flow-based n8n-compatible designer
• Execution Engine: Real-time workflow execution with progress tracking
• WebSocket Infrastructure: Live updates and monitoring
• MCP Bridge: n8n workflow → MCP tool conversion
• Database Models: Workflow storage and execution history

3. From Cluster Monitoring

• Hardware Abstraction: Multi-GPU support and hardware profiling
• Alert System: Configurable alerts with severity levels
• Dashboard Components: React-based monitoring interfaces
• Time-series Storage: Performance data retention and analysis

4. From n8n-integration

• Workflow Patterns: Proven n8n integration examples
• Model Registry: 28+ available models across cluster endpoints
• Protocol Standards: Established communication patterns

🚀 Unified Architecture Components

1. Hive Coordinator Service

class HiveCoordinator:
    """
    Unified orchestration engine combining:
    - Agent coordination and task distribution
    - Workflow execution management  
    - Real-time monitoring and alerting
    - MCP server integration
    """
    
    # Core Services
    agent_manager: AgentManager
    workflow_engine: WorkflowEngine
    performance_monitor: PerformanceMonitor
    mcp_bridge: MCPBridge
    
    # API Interfaces
    rest_api: FastAPI
    websocket_manager: WebSocketManager
    
    # Configuration
    config: HiveConfig
    database: HiveDatabase

2. Database Schema Integration

-- Agent Management (enhanced from distributed-ai-dev)
agents (id, name, endpoint, specialization, capabilities, hardware_config)
agent_metrics (agent_id, timestamp, performance_data, gpu_metrics)
agent_capabilities (agent_id, capability, proficiency_score)

-- Workflow Management (from McPlan)
workflows (id, name, n8n_data, mcp_tools, created_by, version)
executions (id, workflow_id, status, input_data, output_data, logs)
execution_steps (execution_id, step_index, node_id, status, timing)

-- Task Coordination (enhanced)
tasks (id, title, description, priority, assigned_agent, status)
task_dependencies (task_id, depends_on_task_id)
projects (id, name, description, task_template, agent_assignments)

-- System Management
users (id, email, role, preferences, api_keys)
alerts (id, type, severity, message, resolved, timestamp)
system_config (key, value, category, description)

3. Frontend Component Architecture

// Unified Dashboard Structure
src/
├── components/
│   ├── dashboard/
│   │   ├── AgentMonitor.tsx        // Real-time agent status
│   │   ├── PerformanceDashboard.tsx // System metrics
│   │   └── SystemAlerts.tsx        // Alert management
│   ├── workflows/
│   │   ├── WorkflowEditor.tsx      // Visual n8n editor
│   │   ├── ExecutionMonitor.tsx    // Real-time execution
│   │   └── WorkflowLibrary.tsx     // Workflow management
│   ├── agents/
│   │   ├── AgentManager.tsx        // Agent configuration
│   │   ├── TaskQueue.tsx           // Task assignment
│   │   └── CapabilityMatrix.tsx    // Skills management
│   └── projects/
│       ├── ProjectDashboard.tsx    // Project overview
│       ├── TaskManagement.tsx      // Task coordination
│       └── QualityControl.tsx      // Code review
├── stores/
│   ├── hiveStore.ts               // Global state management
│   ├── agentStore.ts              // Agent-specific state
│   ├── workflowStore.ts           // Workflow state
│   └── performanceStore.ts        // Metrics state
└── services/
    ├── api.ts                     // REST API client
    ├── websocket.ts               // Real-time updates
    └── config.ts                  // Configuration management

4. Configuration System

# hive.yaml - Unified Configuration
hive:
  cluster:
    name: "Development Cluster"
    region: "home.deepblack.cloud"
    
  agents:
    acacia:
      name: "ACACIA Infrastructure Specialist"
      endpoint: "http://192.168.1.72:11434"
      model: "deepseek-r1:7b"
      specialization: "infrastructure"
      capabilities: ["devops", "architecture", "deployment"]
      hardware:
        gpu_type: "AMD Radeon RX 7900 XTX"
        vram_gb: 24
        cpu_cores: 16
      performance_targets:
        min_tps: 15
        max_response_time: 30
        
    walnut:
      name: "WALNUT Full-Stack Developer"
      endpoint: "http://192.168.1.27:11434"
      model: "starcoder2:15b"
      specialization: "full-stack"
      capabilities: ["frontend", "backend", "ui-design"]
      hardware:
        gpu_type: "NVIDIA RTX 4090"
        vram_gb: 24
        cpu_cores: 12
      performance_targets:
        min_tps: 20
        max_response_time: 25
        
    ironwood:
      name: "IRONWOOD Backend Specialist"
      endpoint: "http://192.168.1.113:11434"
      model: "deepseek-coder-v2"
      specialization: "backend"
      capabilities: ["optimization", "databases", "apis"]
      hardware:
        gpu_type: "NVIDIA RTX 4080"
        vram_gb: 16
        cpu_cores: 8
      performance_targets:
        min_tps: 18
        max_response_time: 35

  workflows:
    templates:
      web_development:
        agents: ["walnut", "ironwood"]
        stages: ["planning", "frontend", "backend", "integration", "testing"]
      infrastructure:
        agents: ["acacia", "ironwood"]
        stages: ["design", "provisioning", "deployment", "monitoring"]
        
  monitoring:
    metrics_retention_days: 30
    alert_thresholds:
      cpu_usage: 85
      memory_usage: 90
      gpu_usage: 95
      response_time: 60
    health_check_interval: 30
    
  mcp_servers:
    registry:
      comfyui: "ws://localhost:8188/api/mcp"
      code_review: "http://localhost:8000/mcp"
      
  security:
    require_approval: true
    api_rate_limit: 100
    session_timeout: 3600

🗂️ Project Structure

hive/
├── 📋 PROJECT_PLAN.md              # This document
├── 🚀 DEPLOYMENT.md                # Infrastructure deployment guide
├── 🔧 DEVELOPMENT.md               # Development setup and guidelines
├── 📊 ARCHITECTURE.md              # Detailed technical architecture
│
├── backend/                        # Python FastAPI backend
│   ├── app/
│   │   ├── core/                   # Core services
│   │   │   ├── hive_coordinator.py # Main orchestration engine
│   │   │   ├── agent_manager.py    # Agent lifecycle management
│   │   │   ├── workflow_engine.py  # n8n workflow execution
│   │   │   ├── mcp_bridge.py       # MCP protocol integration
│   │   │   └── performance_monitor.py # Metrics and alerting
│   │   ├── api/                    # REST API endpoints
│   │   │   ├── agents.py           # Agent management API
│   │   │   ├── workflows.py        # Workflow API
│   │   │   ├── executions.py       # Execution API
│   │   │   ├── monitoring.py       # Metrics API
│   │   │   └── projects.py         # Project management API
│   │   ├── models/                 # Database models
│   │   │   ├── agent.py
│   │   │   ├── workflow.py
│   │   │   ├── execution.py
│   │   │   ├── task.py
│   │   │   └── user.py
│   │   ├── services/               # Business logic
│   │   └── utils/                  # Helper functions
│   ├── migrations/                 # Database migrations
│   ├── tests/                      # Backend tests
│   └── requirements.txt
│
├── frontend/                       # React TypeScript frontend
│   ├── src/
│   │   ├── components/             # React components
│   │   ├── stores/                 # State management
│   │   ├── services/               # API clients
│   │   ├── types/                  # TypeScript definitions
│   │   ├── hooks/                  # Custom React hooks
│   │   └── utils/                  # Helper functions
│   ├── public/
│   ├── package.json
│   └── vite.config.ts
│
├── config/                         # Configuration files
│   ├── hive.yaml                   # Main configuration
│   ├── agents/                     # Agent-specific configs
│   ├── workflows/                  # Workflow templates
│   └── monitoring/                 # Monitoring configs
│
├── scripts/                        # Utility scripts
│   ├── setup.sh                    # Initial setup
│   ├── deploy.sh                   # Deployment automation
│   ├── migrate.py                  # Data migration from existing projects
│   └── health_check.py             # System health validation
│
├── docker/                         # Container configuration
│   ├── docker-compose.yml          # Development environment
│   ├── docker-compose.prod.yml     # Production deployment
│   ├── Dockerfile.backend
│   ├── Dockerfile.frontend
│   └── nginx.conf                  # Reverse proxy config
│
├── docs/                           # Documentation
│   ├── api/                        # API documentation
│   ├── user-guide/                 # User documentation
│   ├── admin-guide/                # Administration guide
│   └── developer-guide/            # Development documentation
│
└── tests/                          # Integration tests
    ├── e2e/                        # End-to-end tests
    ├── integration/                # Integration tests
    └── performance/                # Performance tests

🔄 Migration Strategy

Phase 1: Foundation (Week 1-2)

1. Project Setup

   • Create unified project structure
   • Set up development environment
   • Initialize database schema
   • Configure CI/CD pipeline

2. Core Integration

   • Merge AIDevCoordinator and McPlan execution engine
   • Unify configuration systems (YAML + database)
   • Integrate authentication systems
   • Set up basic API endpoints

Phase 2: Backend Services (Week 3-4)

1. Agent Management

   • Implement unified agent registration and discovery
   • Migrate agent hardware profiling and monitoring
   • Add capability-based task assignment
   • Integrate performance metrics collection

2. Workflow Engine

   • Port n8n workflow parsing and execution
   • Implement MCP bridge functionality
   • Add real-time execution monitoring
   • Create workflow template system

Phase 3: Frontend Development (Week 5-6)

1. Dashboard Integration

   • Merge monitoring dashboards from both projects
   • Create unified navigation and layout
   • Implement real-time WebSocket updates
   • Add responsive design for mobile access

2. Workflow Editor

   • Port React Flow visual editor
   • Enhance with Hive-specific features
   • Add template library and sharing
   • Implement collaborative editing

Phase 4: Advanced Features (Week 7-8)

1. Quality Control

   • Implement multi-agent code review
   • Add automated testing coordination
   • Create approval workflow system
   • Integrate security scanning

2. Performance Optimization

   • Add intelligent load balancing
   • Implement caching strategies
   • Optimize database queries
   • Add performance analytics

Phase 5: Production Deployment (Week 9-10)

1. Infrastructure

   • Set up Docker Swarm deployment
   • Configure SSL/TLS and domain routing
   • Implement backup and recovery
   • Add monitoring and alerting

2. Documentation & Training

   • Complete user documentation
   • Create admin guides
   • Record demo videos
   • Conduct user training

🎯 Success Metrics

Technical Metrics

• Agent Utilization: >80% average utilization across cluster
• Response Time: <30 seconds average for workflow execution
• Throughput: >50 concurrent task executions
• Uptime: 99.9% system availability
• Performance: <2 second UI response time

User Experience Metrics

• Workflow Creation: <5 minutes to create and deploy simple workflow
• Agent Discovery: Automatic agent health detection within 30 seconds
• Error Recovery: <1 minute mean time to recovery
• Learning Curve: <2 hours for new user onboarding

Business Metrics

• Development Velocity: 50% reduction in multi-agent coordination time
• Code Quality: 90% automated test coverage
• Scalability: Support for 10+ concurrent projects
• Maintainability: <24 hours for feature additions

🔧 Technology Stack

Backend

• Framework: FastAPI + Python 3.11+
• Database: PostgreSQL + Redis (caching)
• Message Queue: Redis + Celery
• Monitoring: Prometheus + Grafana
• Documentation: OpenAPI/Swagger

Frontend

• Framework: React 18 + TypeScript
• UI Library: Tailwind CSS + Headless UI
• State Management: Zustand + React Query
• Visualization: React Flow + D3.js
• Build Tool: Vite

Infrastructure

• Containers: Docker + Docker Swarm
• Reverse Proxy: Traefik v3
• SSL/TLS: Let's Encrypt
• Storage: NFS + PostgreSQL
• Monitoring: Grafana + Prometheus

Development

• Version Control: Git + GitLab
• CI/CD: GitLab CI + Docker Registry
• Testing: pytest + Jest + Playwright
• Code Quality: Black + ESLint + TypeScript

🚀 Quick Start Guide

Development Setup

# Clone and setup
git clone <hive-repo>
cd hive

# Start development environment
./scripts/setup.sh
docker-compose up -d

# Access services
# Frontend: http://localhost:3000
# Backend API: http://localhost:8000
# Documentation: http://localhost:8000/docs

Production Deployment

# Deploy to Docker Swarm
./scripts/deploy.sh production

# Access production services
# Web Interface: https://hive.home.deepblack.cloud
# API: https://hive.home.deepblack.cloud/api
# Monitoring: https://grafana.home.deepblack.cloud

🔮 Future Enhancements

Phase 6: Advanced AI Integration (Month 3-4)

• Multi-modal AI: Image, audio, and video processing
• Fine-tuning Pipeline: Custom model training coordination
• Model Registry: Centralized model management and versioning
• A/B Testing: Automated model comparison and selection

Phase 7: Enterprise Features (Month 5-6)

• Multi-tenancy: Organization and team isolation
• RBAC: Role-based access control with LDAP integration
• Audit Logging: Comprehensive activity tracking
• Compliance: SOC2, GDPR compliance features

Phase 8: Ecosystem Integration (Month 7-8)

• Cloud Providers: AWS, GCP, Azure integration
• CI/CD Integration: GitHub Actions, Jenkins plugins
• API Gateway: External API management and rate limiting
• Marketplace: Community workflow and agent sharing

📞 Support and Community

Documentation

• User Guide: Step-by-step tutorials and examples
• API Reference: Complete API documentation with examples
• Admin Guide: Deployment, configuration, and maintenance
• Developer Guide: Contributing, architecture, and extensions

Community

• Discord: Real-time support and discussions
• GitHub: Issue tracking and feature requests
• Wiki: Community-contributed documentation
• Newsletter: Monthly updates and best practices

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Hive represents the culmination of our distributed AI development efforts, providing a unified, scalable, and user-friendly platform for coordinating AI agents, managing workflows, and monitoring performance across our entire infrastructure.

🐝 "Individual agents are strong, but the Hive is unstoppable."