# 🐝 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**
```python
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**
```sql
-- 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**
```typescript
// 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**
```yaml
# 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**
```bash
# 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**
```bash
# 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

---

**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."*