Adaptive Multi-Agent Development Environment Architecture
This repository contains the design and proof-of-concept implementation for an AI orchestration system that transforms VS Code into an adaptive multi-agent development environment.
Transform every VS Code instance into a personalized, adaptive AI development command center that automatically discovers, coordinates, and optimizes whatever AI tools the developer chooses to use - creating a development experience that's more than the sum of its parts.
- Auto-detects available AI tools across all integration types (MCP servers, VS Code extensions, CLI tools, APIs)
- Probes actual capabilities vs theoretical assumptions
- Adapts in real-time to tool availability changes
- 🔌 MCP Server Access - For Claude Code, custom tools, future MCP-enabled assistants
- 🧩 VS Code Extension API - For GitHub Copilot, Amazon Q, workspace integration
- 🌐 Direct API Calls - For services without MCP or extension interfaces
- 💻 CLI Integration - For command-line tools like Gemini CLI
- Routes tasks based on actual tool availability and performance
- Provides graceful degradation when preferred tools are unavailable
- Learns from user preferences and feedback
ai-orchestration-vscode/
├── ai-orchestration-mockup.qmd # Complete architecture documentation
├── test-copilot-extension/ # Proof-of-concept VS Code extension
│ ├── package.json # Extension manifest
│ ├── src/extension.ts # Basic Copilot API integration test
│ └── tsconfig.json # TypeScript configuration
├── README.md # This file
├── ARCHITECTURE.md # Technical architecture overview
├── docs/ # Additional documentation
│ ├── integration-guide.md # How to integrate new AI tools
│ ├── user-guide.md # End-user documentation
│ └── api-reference.md # API documentation for developers
└── examples/ # Example implementations
├── mcp-connectors/ # Example MCP server connectors
├── extension-connectors/ # Example VS Code extension connectors
└── workflows/ # Example orchestration workflows
- Works with any combination of AI tools
- No hardcoded assumptions about which tools are available
- Future-proof against new tools and integration methods
- Right AI for the right task through capability matching
- Parallel execution where possible for performance
- Conflict resolution for overlapping capabilities
- Real-time adaptation to tool health and availability
- Single interface for multiple AI tools
- Transparent operations with clear explanations
- User control with override capabilities at any level
- Minimal configuration required
This repository contains:
- ✅ Complete Architecture Design - Detailed in
ai-orchestration-mockup.qmd - ✅ Proof-of-Concept Extension - Basic VS Code extension that demonstrates GitHub Copilot API access
- 🚧 Implementation Roadmap - Detailed implementation plan
- 🔄 Active Research - Ongoing investigation into AI tool integration methods
Open ai-orchestration-mockup.qmd in any Quarto-compatible viewer or convert to HTML:
# If you have Quarto installed
quarto render ai-orchestration-mockup.qmd
# Or view the raw markdown for full detailscd test-copilot-extension
npm install
npm run compile
# Then load the extension in VS Code for testing- Architecture design and documentation
- Proof-of-concept VS Code extension
- GitHub Copilot API integration test
- Basic MCP server discovery
- Multi-protocol tool discovery system
- Capability probing and testing framework
- Health monitoring and adaptation
- Configuration management
- Task classification and routing logic
- User preference integration
- Performance-based routing optimization
- Fallback and degradation strategies
- MCP server connector framework
- VS Code extension connector system
- CLI tool integration layer
- API service connectors
- Discovery results visualization
- Routing explanation dialogs
- Configuration interfaces
- Performance monitoring dashboard
- Machine learning-enhanced routing
- Workflow template system
- Team collaboration features
- Ecosystem integration expansion
This project is in early research and design phase. Contributions welcome in the form of:
- Architecture feedback - Review the design document and provide insights
- Integration research - Investigate how different AI tools can be integrated
- Proof-of-concept code - Build small demos of specific integration approaches
- Use case documentation - Document real-world scenarios where this would be valuable
- Architecture Overview - Complete system design with diagrams and code examples
- Integration Guide - How to add support for new AI tools
- User Guide - End-user documentation and workflows
- API Reference - Developer API documentation
- Claude Code - Official Anthropic CLI for Claude
- Model Context Protocol - Standard for AI tool integration
- VS Code Extension API - VS Code extensibility platform
This project is licensed under the MIT License - see the LICENSE file for details.
Create a development experience where the right AI expertise is always available for the right task, seamlessly coordinated through a familiar interface, regardless of which specific tools the developer has chosen to install.
Status: 🔬 Research & Design Phase Next Milestone: Core discovery engine implementation Target: Transform AI-assisted development from fragmented tools to collaborative ecosystems