WHOOSH - Autonomous AI Development Teams

Orchestration platform for self-organizing AI development teams with democratic consensus and P2P collaboration.

🎯 Overview

WHOOSH has evolved from a simple project template tool into a sophisticated Autonomous AI Development Teams Architecture that enables AI agents to form optimal development teams, collaborate through P2P channels, and deliver high-quality solutions through democratic consensus processes.

🏗️ Architecture

Core Components

• 🧠 Team Composer: LLM-powered task analysis and optimal team formation
• 🤖 Agent Self-Organization: CHORUS agents autonomously discover and apply to teams
• 🔗 P2P Collaboration: UCXL addressing with structured reasoning (HMMM)
• 🗳️ Democratic Consensus: Voting systems with quality gates and institutional compliance
• 📦 Knowledge Preservation: Complete context capture for SLURP with provenance tracking

Integration Ecosystem

WHOOSH Team Composer → GITEA Team Issues → CHORUS Agent Discovery → P2P Team Channels → SLURP Artifact Submission

📋 Development Status

Current Phase: Foundation & Planning

• ✅ Comprehensive architecture specifications
• ✅ Database schema design
• ✅ API specification
• ✅ Team Composer design
• ✅ CHORUS integration specification
• 🚧 Implementation in progress

🚀 Quick Start

Prerequisites

• Python 3.11+
• PostgreSQL 15+
• Redis 7+
• Docker & Docker Compose
• Access to Ollama models or cloud LLM APIs

Development Setup

# Clone repository
git clone https://gitea.chorus.services/tony/WHOOSH.git
cd WHOOSH

# Setup Python environment
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt

# Setup database
docker-compose up -d postgres redis
python scripts/setup_database.py

# Run development server
python -m whoosh.main

📚 Documentation

Architecture & Design

• 📋 Development Plan - Complete 24-week roadmap
• 🗄️ Database Schema - Comprehensive data architecture
• 🌐 API Specification - Complete REST & WebSocket APIs

Core Systems

• 🧠 Team Composer - LLM-powered team formation engine
• 🤖 CHORUS Integration - Agent self-organization & P2P collaboration
• 📖 Original Vision - Autonomous AI development teams concept

🔧 Key Features

Team Formation

• Intelligent Analysis: LLM-powered task complexity and skill requirement analysis
• Optimal Composition: Dynamic team sizing with role-based agent matching
• Risk Assessment: Comprehensive project risk evaluation and mitigation
• Timeline Planning: Automated formation scheduling with contingencies

Agent Coordination

• Self-Assessment: Agents evaluate their own capabilities and availability
• Opportunity Discovery: Automated scanning of team formation opportunities
• Autonomous Applications: Intelligent team application with value propositions
• Performance Tracking: Continuous learning from team outcomes

Collaboration Systems

• P2P Channels: UCXL-addressed team communication channels
• HMMM Reasoning: Structured thought processes with evidence and consensus
• Democratic Voting: Multiple consensus mechanisms (majority, supermajority, unanimous)
• Quality Gates: Institutional compliance with provenance and security validation

Knowledge Management

• Context Preservation: Complete capture of team processes and decisions
• SLURP Integration: Automated artifact bundling and submission
• Decision Rationale: Comprehensive reasoning chains and consensus records
• Learning Loop: Continuous improvement from team performance feedback

🛠️ Technology Stack

Backend

• Language: Python 3.11+ with FastAPI
• Database: PostgreSQL 15+ with async support
• Cache: Redis 7+ for sessions and real-time data
• LLM Integration: Ollama + Cloud APIs (OpenAI, Anthropic)
• P2P: libp2p for peer-to-peer networking

Frontend

• Framework: React 18 with TypeScript
• State: Zustand for complex state management
• UI: Tailwind CSS with Headless UI components
• Real-time: WebSocket with auto-reconnect
• Charts: D3.js for advanced visualizations

Infrastructure

• Containers: Docker with multi-stage builds
• Orchestration: Docker Swarm (cluster deployment)
• Proxy: Traefik with SSL termination
• Monitoring: Prometheus + Grafana
• CI/CD: GITEA Actions with automated testing

🎯 Roadmap

Phase 1: Foundation (Weeks 1-4)

• Core infrastructure and Team Composer service
• Database schema implementation
• Basic API endpoints and WebSocket infrastructure

Phase 2: CHORUS Integration (Weeks 5-8)

• Agent self-organization capabilities
• GITEA team issue integration
• P2P communication infrastructure

Phase 3: Collaboration Systems (Weeks 9-12)

• Democratic consensus mechanisms
• HMMM reasoning integration
• Team lifecycle management

Phase 4: SLURP Integration (Weeks 13-16)

• Artifact packaging and submission
• Knowledge preservation systems
• Quality validation pipelines

Phase 5: Frontend & UX (Weeks 17-20)

• Complete user interface
• Real-time dashboards
• Administrative controls

Phase 6: Advanced Features (Weeks 21-24)

• Machine learning optimization
• Cloud LLM integration
• Advanced analytics and reporting

🤝 Contributing

1. Fork the repository on GITEA
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is part of the CHORUS ecosystem and follows the same licensing terms.

🔗 Related Projects

• CHORUS - Distributed AI agent coordination
• KACHING - License management and billing
• SLURP - Knowledge artifact management
• BZZZ - Original task coordination (legacy)

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WHOOSH - Where AI agents become autonomous development teams 🚀