Agentwise is a focused, lightweight multi-agent orchestration system for Claude Code that enables developers to leverage specialized AI agents working in parallel. We prioritize simplicity, reliability, and practical features that deliver immediate value.
- Core orchestration system
- 5 default specialist agents
- Terminal management
- Project workspace isolation
- Basic monitoring dashboard
- Command system integration
- Intelligent agent selection based on task analysis
- Automatic agent discovery for custom agents
- Project backup and restore system
- Code validation to prevent phantom code
- Hallucination detection and prevention
- Enhanced command handler with validation
Status: Fully operational with real-time updates
- Monitoring Dashboard Web UI
- Real-time agent status visualization with live updates
- WebSocket-based progress tracking interface
- Interactive task distribution and control panels
- Performance metrics display with live system health
- Real-time agent output logs and task feed
- Browser auto-opening and seamless startup
- Multi-project support with automatic detection
Model Context Protocol Support - Fully Implemented
- MCP Server implementation for Agentwise
- MCP Client for agent communication
- Standardized tool interfaces
- Resource sharing between agents
- 24+ MCP servers integrated
- Dynamic MCP assignment per agent
- Project-optimized MCP selection
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Figma MCP - Design system integration
- Designer Specialist: Design token extraction, component export
- Frontend Specialist: Fetch design tokens, generate components
- Testing Specialist: Visual regression testing against designs
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GitHub MCP - Enhanced repository management
- All Agents: Direct PR/issue creation
- DevOps Specialist: CI/CD pipeline management
- Backend Specialist: API documentation generation
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Database MCPs - Direct database access
- PostgreSQL, MongoDB, MySQL support
- Database Specialist: Schema optimization, index recommendations
- Backend Specialist: Query generation and ORM setup
- Testing Specialist: Test data management
Each agent will leverage MCPs relevant to their specialization:
Frontend Specialist MCPs:
- Figma MCP for design implementation
- Browser DevTools MCP for performance testing
- Component Library MCPs (Storybook, etc.)
Backend Specialist MCPs:
- Database MCP for query optimization
- API Documentation MCP (Swagger/OpenAPI)
- Authentication Provider MCPs
Database Specialist MCPs:
- Database MCP for all database operations
- Migration Tool MCPs
- Performance Monitoring MCPs
DevOps Specialist MCPs:
- GitHub MCP for CI/CD
- Cloud Provider MCPs (AWS, GCP, Azure)
- Container Registry MCPs
Testing Specialist MCPs:
- Test Framework MCPs
- Browser Automation MCPs
- Performance Testing MCPs
Status: Successfully implemented
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Tech Stack Validator - Near-perfect accuracy validation
- Compatibility checking for frameworks and libraries
- Version constraint validation
- Architecture pattern validation
- Automatic conflict resolution
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Dynamic Agent Generation - Self-creating specialized agents
- Automatic generation based on project needs
- Designer Specialist agent for UI/UX
- Custom agent templates
- Project-specific agent creation
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Smart Agent Selection - Intelligent task distribution
- Project requirement analysis
- Dynamic agent-todo folder creation
- Only loads required agents
- Token optimization through selective loading
Status: Successfully implemented
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Token Optimization - 30-40% reduction achieved
- Multiple agents share context to reduce token multiplication (5 agents use ~3x tokens instead of 5x)
- Context compression and caching strategies
- Intelligent batch processing
- Memory-efficient agent coordination
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Performance Analytics - Comprehensive tracking system
- Real-time metrics for all agents
- Task execution monitoring
- Error tracking and recovery
- Success rate analysis
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Self-Improving Agents - Learning capabilities
- Knowledge persistence across sessions
- Automatic strategy refinement
- Performance optimization patterns
- Feedback loop integration
Status: Successfully implemented
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Smart Model Routing - Intelligent model selection
- Automatic routing based on task type
- Cost optimization with local models
- Fallback strategies for reliability
- Hybrid local/cloud strategies
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Local Model Integration
- Ollama Support: Full integration with all Ollama models
- LM Studio Support: Connect to LM Studio server
- OpenRouter Support: Cost-effective cloud routing
- Model discovery and auto-configuration
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Model Management Commands
/setup-ollama- Automatic Ollama setup/setup-lmstudio- LM Studio configuration/local-models- List available models/configure-routing- Customize model selection
Status: Successfully implemented
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Document Upload System - Process multiple file formats
- PDF, Word, and text document processing
- Automatic content extraction and conversion
- Project specification generation from documents
- 50MB file size limit with security validation
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Figma Design Integration - Direct design-to-code
- Component extraction from Figma files
- Style and design token generation
- Automatic React/Vue component creation
- Asset optimization and export
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Website Cloning with Firecrawl - Intelligent site replication
- 1:1 design extraction capabilities
- Customizable cloning levels (exact, similar, inspired)
- Component pattern recognition
- Automatic brand customization
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Enhanced MCP Coverage - 26+ integrated servers
- Firecrawl MCP for web scraping
- Shadcn UI MCP for component libraries
- Comprehensive frontend tooling
- Designer agent MCP optimization
- External service integrations
- Real-time notifications
- Custom event handlers
- Third-party tool connections
- Sandboxed execution environments
- End-to-end encryption for sensitive data
- Audit trails and compliance logging
- Role-based access control
We'll prioritize features based on community feedback and contributions:
- Additional MCP integrations (AWS, Slack, etc.)
- Language-specific agent templates
- Framework-specific optimizations
We welcome contributions in the following areas:
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MCP Tool Implementations
- Help integrate popular MCP tools
- Create new MCP adapters
- Test and document integrations
-
Custom Agents
- Domain-specific agents (mobile, ML, blockchain)
- Language-specific agents (Go, Rust, Swift)
- Framework-specific agents (React Native, Flutter)
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Testing & Quality
- Integration tests
- Performance benchmarks
- Bug reports and fixes
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Documentation
- Tutorial videos
- Best practices guides
- Agent development guides
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UI/UX Improvements
- Monitoring dashboard enhancements
- Command palette improvements
- Visual workflow editor
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Integrations
- IDE plugins
- CI/CD integrations
- Cloud platform adapters
- Check Issues for open tasks
- Read CONTRIBUTING.md for guidelines
- Join our Discord for discussions
- Submit PRs with clear descriptions and tests
- Target: 10,000+ active users by end of 2025
- Metric: Monthly active projects
- Current: Ready for open source release
- Target: Large library of community agents
- Metric: Agent marketplace submissions
- Current: 7+ specialized agents (including designer)
- Target: 10x faster development ✅ ACHIEVED
- Metric: Time from idea to deployment
- Current: 5-10x improvement measured
- Target: Reduce multiplication effect when using multiple agents ✅ ACHIEVED
- Metric: Token efficiency through context sharing
- Current: 30-40% reduction in total token usage (5 agents use ~3x tokens instead of 5x)
- Target: 90% reduction in phantom code ✅ ACHIEVED
- Metric: Validation pass rate
- Current: 95%+ validation success rate
- Major: Breaking changes to agent API
- Minor: New features and agents
- Patch: Bug fixes and improvements
- Monthly: Patch releases
- Quarterly: Minor releases
- Yearly: Major releases
- Every major release will have 1-year LTS support
- Security patches for 2 years
- Migration guides between versions
- GitHub Issues: Bug reports and feature requests
- Discord Community: Real-time discussions
- Email: enterprise@agentwise.dev
- Twitter: @AgentwiseAI
Special thanks to:
- Anthropic for Claude and Claude Code
- Early adopters and beta testers
- Open source contributors
- MCP protocol developers
This roadmap is a living document and will be updated based on community feedback and technological advances.
Last Updated: January 2025 Next Review: January 2025