# 🐝 WHOOSH: Unified Distributed AI Orchestration Platform ## Project Overview **WHOOSH** 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 ``` ┌─────────────────────────────────────────────────────────────────┐ │ WHOOSH 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) │ │ ├── 🧠 WHOOSH 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. WHOOSH Coordinator Service** ```python class WHOOSHCoordinator: """ 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: WHOOSHConfig database: WHOOSHDatabase ``` #### **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/ │ ├── whooshStore.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 # whoosh.yaml - Unified Configuration whoosh: 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 ``` whoosh/ ├── 📋 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 │ │ │ ├── whoosh_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 │ ├── whoosh.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 WHOOSH-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 + GITEA - **CI/CD**: GITEA Actions + Docker Registry - **Testing**: pytest + Jest + Playwright - **Code Quality**: Black + ESLint + TypeScript ## 🚀 Quick Start Guide ### **Development Setup** ```bash # Clone and setup git clone cd whoosh # 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://whoosh.home.deepblack.cloud # API: https://whoosh.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 --- **WHOOSH 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 WHOOSH is unstoppable."*