- Migrated from HIVE branding to WHOOSH across all components
- Enhanced backend API with new services: AI models, BZZZ integration, templates, members
- Added comprehensive testing suite with security, performance, and integration tests
- Improved frontend with new components for project setup, AI models, and team management
- Updated MCP server implementation with WHOOSH-specific tools and resources
- Enhanced deployment configurations with production-ready Docker setups
- Added comprehensive documentation and setup guides
- Implemented age encryption service and UCXL integration
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- 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>
- Force backend and frontend services to run on walnut where images are built
- Fixes 'No such image' errors on other nodes
- Both services now running successfully (1/1 replicas)
- Frontend accessible at https://hive.home.deepblack.cloud
- Backend API responding on /api/ endpoints
🤖 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>
- 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>
The backend had a redundant MCP server implementation that was commented out
and not being used. The standalone MCP server in /mcp-server/ is already
functional and provides complete MCP integration.
Changes:
- Removed commented MCP server import and initialization code from main.py
- Deleted redundant /backend/app/mcp/distributed_mcp_server.py
- Cleaned up unused imports and code paths
Benefits:
- Eliminates code duplication and maintenance burden
- Removes confusion about which MCP server to use
- Simplifies backend codebase
- Standalone MCP server in /mcp-server/ provides full functionality
🤖 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 refactoring:
- Created UnifiedCoordinator that combines HiveCoordinator and DistributedCoordinator
- Eliminated code duplication and architectural redundancy
- Unified agent management, task orchestration, and workflow execution
- Single coordinator instance replaces two global coordinators
- Backward compatibility maintained through state aliases
Key features of UnifiedCoordinator:
✅ Combined agent types: Ollama + CLI agents with unified management
✅ Dual task modes: Simple tasks + complex distributed workflows
✅ Performance monitoring: Prometheus metrics + adaptive load balancing
✅ Background processes: Health monitoring + performance optimization
✅ Redis integration: Distributed caching and coordination (optional)
✅ Database integration: Agent loading + task persistence preparation
API updates:
- Updated all API endpoints to use unified coordinator
- Maintained interface compatibility for existing endpoints
- Fixed attribute references for unified agent model
- Simplified dependency injection pattern
Architecture benefits:
- Single point of coordination eliminates race conditions
- Reduced memory footprint (one coordinator vs two)
- Simplified initialization and lifecycle management
- Consistent feature set across all orchestration modes
- Better separation of concerns within single coordinator class
This resolves the critical architectural issue of redundant coordinators
while maintaining full backward compatibility and adding enhanced features.
🤖 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>
Major Features Added:
- Fix Socket.IO connectivity by updating Dockerfile to use socket_app
- Resolve distributed workflows API to return arrays instead of errors
- Expand agent coverage from 3 to 7 agents (added OAK and ROSEWOOD)
- Create comprehensive systemd service for MCP server with auto-discovery
- Add daemon mode with periodic agent discovery every 5 minutes
- Implement comprehensive test suite with 100% pass rate
Infrastructure Improvements:
- Enhanced database connection handling with retry logic
- Improved agent registration with persistent storage
- Added proper error handling for distributed workflows endpoint
- Created management scripts for service lifecycle operations
Agent Cluster Expansion:
- ACACIA: deepseek-r1:7b (kernel_dev)
- WALNUT: starcoder2:15b (pytorch_dev)
- IRONWOOD: deepseek-coder-v2 (profiler)
- OAK: codellama:latest (docs_writer)
- OAK-TESTER: deepseek-r1:latest (tester)
- ROSEWOOD: deepseek-coder-v2:latest (kernel_dev)
- ROSEWOOD-VISION: llama3.2-vision:11b (tester)
System Status: All 7 agents healthy, Socket.IO operational, MCP server fully functional
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Replace separate hive-api.home.deepblack.cloud subdomain with unified hive.home.deepblack.cloud
- Update Traefik routing: /api/* → backend, /* → frontend with proper priorities
- Add /api/health endpoint while maintaining /health for Docker health checks
- Update Socket.IO configuration to use single domain
- Fix CORS settings for consolidated domain
- Update MCP server endpoint to use /api path prefix
- Update all documentation to reflect single domain architecture
System now fully operational with simplified routing and proper SSL certificates.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Backend fixes:
- Remove --reload flag to prevent dev mode cycling
- Add curl for health checks
- Configure PostgreSQL connection properly
- Fix Docker CMD for production deployment
Frontend fixes:
- Use serve for production static file serving
- Add curl for health checks (installed as root before user switch)
- Configure proper host binding for containers
- Fix Dockerfile layer ordering
Results:
- ✅ Backend: 1/2 replicas running, health checks passing
- ✅ Frontend: 2/2 replicas running, serving requests
- ✅ Health endpoints responding correctly
- ✅ Services stable and persistent
🤖 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>
