- 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>
✨ 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 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>
- 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>
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>
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>
