https://github.com/simplemindedbot/ai-orchestration-vscode
Adaptive Multi-Agent Development Environment - Transform VS Code into an intelligent orchestration hub for AI assistants and development tools
https://github.com/simplemindedbot/ai-orchestration-vscode
adaptive-routing ai-assistants ai-orchestration claude-code development-tools github-copilot mcp-protocol multi-agent vscode-extension
Last synced: 17 days ago
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Adaptive Multi-Agent Development Environment - Transform VS Code into an intelligent orchestration hub for AI assistants and development tools
- Host: GitHub
- URL: https://github.com/simplemindedbot/ai-orchestration-vscode
- Owner: simplemindedbot
- License: mit
- Created: 2025-09-16T01:55:08.000Z (22 days ago)
- Default Branch: main
- Last Pushed: 2025-09-16T02:00:28.000Z (22 days ago)
- Last Synced: 2025-09-16T04:06:11.711Z (22 days ago)
- Topics: adaptive-routing, ai-assistants, ai-orchestration, claude-code, development-tools, github-copilot, mcp-protocol, multi-agent, vscode-extension
- Language: TypeScript
- Size: 52.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Roadmap: ROADMAP.md
Awesome Lists containing this project
README
# AI Orchestration for VS Code
**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.
## 🎯 Vision
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.
## 🚀 Key Innovations
### Dynamic Tool Discovery
- **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
### Hybrid Integration Architecture
- **🔌 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
### Adaptive Task Routing
- Routes tasks based on **actual tool availability and performance**
- Provides **graceful degradation** when preferred tools are unavailable
- Learns from **user preferences and feedback**
## 📁 Repository Structure
```
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
```
## 🔍 Key Features
### True Tool Agnosticism
- Works with **any combination** of AI tools
- No hardcoded assumptions about which tools are available
- **Future-proof** against new tools and integration methods
### Intelligent Orchestration
- **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
### Seamless User Experience
- **Single interface** for multiple AI tools
- **Transparent operations** with clear explanations
- **User control** with override capabilities at any level
- **Minimal configuration** required
## 🛠️ Current Status
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
## 🚀 Getting Started
### View the Architecture
Open `ai-orchestration-mockup.qmd` in any Quarto-compatible viewer or convert to HTML:
```bash
# If you have Quarto installed
quarto render ai-orchestration-mockup.qmd
# Or view the raw markdown for full details
```
### Test the Copilot Integration
```bash
cd test-copilot-extension
npm install
npm run compile
# Then load the extension in VS Code for testing
```
## 🎯 Implementation Roadmap
### Phase 1: Foundation (Current)
- [x] Architecture design and documentation
- [x] Proof-of-concept VS Code extension
- [x] GitHub Copilot API integration test
- [ ] Basic MCP server discovery
### Phase 2: Core Discovery Engine
- [ ] Multi-protocol tool discovery system
- [ ] Capability probing and testing framework
- [ ] Health monitoring and adaptation
- [ ] Configuration management
### Phase 3: Adaptive Routing
- [ ] Task classification and routing logic
- [ ] User preference integration
- [ ] Performance-based routing optimization
- [ ] Fallback and degradation strategies
### Phase 4: Tool Integration
- [ ] MCP server connector framework
- [ ] VS Code extension connector system
- [ ] CLI tool integration layer
- [ ] API service connectors
### Phase 5: User Experience
- [ ] Discovery results visualization
- [ ] Routing explanation dialogs
- [ ] Configuration interfaces
- [ ] Performance monitoring dashboard
### Phase 6: Advanced Features
- [ ] Machine learning-enhanced routing
- [ ] Workflow template system
- [ ] Team collaboration features
- [ ] Ecosystem integration expansion
## 🤝 Contributing
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
## 📚 Documentation
- **[Architecture Overview](ai-orchestration-mockup.qmd)** - Complete system design with diagrams and code examples
- **[Integration Guide](docs/integration-guide.md)** - How to add support for new AI tools
- **[User Guide](docs/user-guide.md)** - End-user documentation and workflows
- **[API Reference](docs/api-reference.md)** - Developer API documentation
## 🔗 Related Projects
- **[Claude Code](https://claude.ai/code)** - Official Anthropic CLI for Claude
- **[Model Context Protocol](https://modelcontextprotocol.io/)** - Standard for AI tool integration
- **[VS Code Extension API](https://code.visualstudio.com/api)** - VS Code extensibility platform
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🌟 Vision Statement
*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