Major Features:
✅ JWT Bearer Token authentication system with secure token management
✅ API key generation and management with scoped permissions
✅ Complete user management (registration, login, logout, password change)
✅ Frontend authentication components and context integration
Backend Architecture Improvements:
✅ CORS configuration via environment variables (CORS_ORIGINS)
✅ Dependency injection pattern for unified coordinator
✅ Database schema fixes with UUID support and SQLAlchemy compliance
✅ Task persistence replaced in-memory storage with database-backed system
✅ Service separation following Single Responsibility Principle
✅ Fixed SQLAlchemy metadata column naming conflicts
Infrastructure & Testing:
✅ Comprehensive Jest unit testing and Playwright e2e testing infrastructure
✅ GitHub Actions CI/CD pipeline integration
✅ Enhanced API clients matching PROJECT_PLAN.md specifications
✅ Docker Swarm deployment with proper networking and service connectivity
Database & Security:
✅ UUID-based user models with proper validation
✅ Unified database schema with authentication tables
✅ Token blacklisting and refresh token management
✅ Secure password hashing with bcrypt
✅ API key scoping and permissions system
API Enhancements:
✅ Authentication endpoints (/api/auth/*)
✅ Task management with database persistence
✅ Enhanced monitoring and health check endpoints
✅ Comprehensive error handling and validation
Deployment:
✅ Successfully deployed to Docker Swarm at https://hive.home.deepblack.cloud✅ All services operational with proper networking
✅ Environment-based configuration support
🛠️ Technical Debt Resolved:
- Fixed global coordinator instances with proper dependency injection
- Replaced hardcoded CORS origins with environment variables
- Unified User model schema conflicts across authentication system
- Implemented database persistence for critical task storage
- Created comprehensive testing infrastructure
This release transforms Hive from a development prototype into a production-ready
distributed AI orchestration platform with enterprise-grade authentication,
proper architectural patterns, and robust deployment infrastructure.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Create dedicated service classes for separated concerns:
* AgentService: Agent management and health monitoring
* WorkflowService: Workflow parsing and execution tracking
* PerformanceService: Metrics and load balancing
* BackgroundService: Background processes and cleanup
* TaskService: Database persistence (already existed)
- Refactor UnifiedCoordinator into UnifiedCoordinatorRefactored
* Clean separation of responsibilities
* Improved maintainability and testability
* Dependency injection pattern for services
* Clear service boundaries and interfaces
- Maintain backward compatibility through re-exports
- Update main.py to use refactored coordinator
🚀 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major architectural improvement to replace in-memory task storage with
database-backed persistence while maintaining backward compatibility.
Changes:
- Created Task SQLAlchemy model matching database schema
- Added Workflow and Execution SQLAlchemy models
- Created TaskService for database CRUD operations
- Updated UnifiedCoordinator to use database persistence
- Modified task APIs to leverage database storage
- Added task loading from database on coordinator initialization
- Implemented status change persistence during task execution
- Enhanced task cleanup with database support
- Added comprehensive task statistics from database
Benefits:
- Tasks persist across application restarts
- Better scalability and reliability
- Historical task data retention
- Comprehensive task filtering and querying
- Maintains in-memory cache for performance
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Frontend Enhancements:
- Complete React TypeScript frontend with modern UI components
- Distributed workflows management interface with real-time updates
- Socket.IO integration for live agent status monitoring
- Agent management dashboard with cluster visualization
- Project management interface with metrics and task tracking
- Responsive design with proper error handling and loading states
Backend Infrastructure:
- Distributed coordinator for multi-agent workflow orchestration
- Cluster management API with comprehensive agent operations
- Enhanced database models for agents and projects
- Project service for filesystem-based project discovery
- Performance monitoring and metrics collection
- Comprehensive API documentation and error handling
Documentation:
- Complete distributed development guide (README_DISTRIBUTED.md)
- Comprehensive development report with architecture insights
- System configuration templates and deployment guides
The platform now provides a complete web interface for managing the distributed AI cluster
with real-time monitoring, workflow orchestration, and agent coordination capabilities.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
