- Update API base URL from localhost to https://api.hive.home.deepblack.cloud
- Update WebSocket URL to https://hive.home.deepblack.cloud for proper TLS routing
- Remove metadata field from Project model to fix SQLAlchemy conflict
- Remove index from JSON expertise column in AgentRole to fix PostgreSQL indexing
- Update push script to use local registry instead of Docker Hub
- Add Gitea repository support and monitoring endpoints
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Backend:
- Database migration for agent role fields and predefined roles
- AgentRole and AgentCollaboration models
- Updated Agent model with role-based fields
Frontend:
- AgentRoleSelector component for role assignment
- CollaborationDashboard for monitoring agent interactions
- AgentManagement interface with role analytics
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
✨ Features Added:
- Task Management API with comprehensive filtering and statistics
- Workflow Management API with multi-agent orchestration
- CLI Agent Management API with health monitoring
- Extended response models with performance metrics
- Advanced error handling with standardized error codes
📊 API Coverage Completed:
- Tasks API: CRUD operations, filtering, pagination, statistics, cancellation
- Workflows API: Creation, execution, monitoring, template management
- CLI Agents API: Registration, health checks, predefined setups, SSH management
- Enhanced CLI agent models with performance analytics
🛠️ Technical Improvements:
- Comprehensive Pydantic models for all CLI agent operations
- Advanced filtering with type safety and validation
- Performance metrics integration across all endpoints
- Health monitoring with deep check capabilities
- Predefined agent configuration for quick setup
🌐 Developer Experience:
- Interactive API documentation with realistic examples
- Comprehensive error responses with troubleshooting guidance
- Best practices and use case documentation
- Professional-grade endpoint descriptions with detailed workflows
Phase 2 establishes enterprise-grade API documentation standards
across all major Hive components, providing developers with
comprehensive, interactive documentation for efficient integration.
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
✨ Features:
- Comprehensive Pydantic response models with examples
- Enhanced FastAPI configuration with rich OpenAPI metadata
- Centralized error handling with standardized error codes
- Professional Swagger UI styling and branding
- Health check endpoints with detailed component status
- Type-safe request/response models for all endpoints
📊 Coverage:
- Agent Management API fully documented
- Standardized error responses across all endpoints
- Interactive API documentation with try-it-now functionality
- Custom OpenAPI schema with authentication schemes
🛠️ Technical Improvements:
- Created app/models/responses.py with comprehensive models
- Added app/core/error_handlers.py for centralized error handling
- Enhanced app/api/agents.py with detailed documentation
- Custom documentation configuration in app/docs_config.py
- Global exception handlers for consistent error responses
🌐 Access Points:
- Swagger UI: /docs
- ReDoc: /redoc
- OpenAPI JSON: /openapi.json
This establishes professional-grade API documentation that matches
Hive's technical excellence and provides developers with comprehensive,
interactive documentation for efficient integration.
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
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>
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>
Major changes:
- Consolidate 3 different User models into single unified model (models/user.py)
- Use UUID primary keys throughout (matches existing database schema)
- Add comprehensive authentication fields while preserving existing data
- Remove duplicate User model from auth.py, keep APIKey/RefreshToken/TokenBlacklist
- Update all imports to use unified User model consistently
- Create database migration (002_add_auth_fields.sql) for safe schema upgrade
- Fix frontend User interface to handle UUID string IDs
- Add backward compatibility fields (name property, role field)
- Maintain relationships for authentication features (api_keys, refresh_tokens)
Schema conflicts resolved:
✅ Migration schema (UUID, 7 fields) + Basic model (Integer, 6 fields) + Auth model (Integer, 10 fields)
→ Unified model (UUID, 12 fields with full backward compatibility)
✅ Field inconsistencies (name vs full_name) resolved with compatibility property
✅ Database foreign key constraints updated for UUID relationships
✅ JWT token handling fixed for UUID user IDs
This completes the holistic database schema unification requested after quick
patching caused conflicts. All existing data preserved, full auth system functional.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Create comprehensive authentication backend with JWT and API key support
- Add database models for users, API keys, and tokens with proper security
- Implement authentication middleware and API endpoints
- Build complete frontend authentication UI with:
- LoginForm component with JWT authentication
- APIKeyManager for creating and managing API keys
- AuthDashboard for comprehensive auth management
- AuthContext for state management and authenticated requests
- Initialize database with default admin user (admin/admin123)
- Add proper token refresh, validation, and blacklisting
- Implement scope-based API key authorization system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Parameterize CORS_ORIGINS in docker-compose.swarm.yml
- Add .env.example with configuration options
- Create comprehensive LOCAL_DEVELOPMENT.md guide
- Update README.md with environment variable documentation
- Provide alternatives for local development without production domain
🤖 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>
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>
