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Initial commit: Complete Hive distributed AI orchestration platform

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This comprehensive implementation includes:
- FastAPI backend with MCP server integration
- React/TypeScript frontend with Vite
- PostgreSQL database with Redis caching
- Grafana/Prometheus monitoring stack
- Docker Compose orchestration
- Full MCP protocol support for Claude Code integration

Features:
- Agent discovery and management across network
- Visual workflow editor and execution engine
- Real-time task coordination and monitoring
- Multi-model support with specialized agents
- Distributed development task allocation

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
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# 🐝 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."*