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# Knowledge Graph: Modal + MCO + Gradio Integration
## Core Components
### 1. Modal API
- **Purpose**: Cloud platform for running AI models and serverless functions
- **Key Features**:
- Run LLM inference (Claude, GPT-4, etc.)
- Execute Python code in isolated environments
- Persistent storage for files and data
- Webhook endpoints for external access
- **Integration Points**:
- Python SDK for creating and deploying functions
- API keys for authentication
- Function decorators for configuration
### 2. MCO MCP Server
- **Purpose**: Orchestration layer for agent frameworks
- **Key Components**:
- SNLP Parser: Processes the four MCO files (core, sc, features, styles)
- Orchestration Engine: Manages workflow state and progressive revelation
- MCP Tool Provider: Exposes orchestration tools via MCP protocol
- **Integration Points**:
- MCP SDK with stdio transport
- Tool definitions for agent frameworks
- Configuration via SNLP files
### 3. Gradio UI
- **Purpose**: Web interface for demonstrating and interacting with the system
- **Key Features**:
- Single-page design with multiple components
- Real-time updates and streaming
- File upload/download capabilities
- Custom CSS and JavaScript
- **Integration Points**:
- Python API for component creation
- JavaScript for custom behaviors
- WebSocket for real-time updates
## Integration Architecture
### Modal → MCO Connection
- Modal functions call MCO MCP server tools
- MCO configuration stored in Modal app
- Agent code runs in Modal, orchestrated by MCO
### MCO → Gradio Connection
- MCO logs and events streamed to Gradio UI
- SNLP files generated in Gradio, used by MCO
- Orchestration status displayed in Gradio
### Gradio → Modal Connection
- User inputs from Gradio sent to Modal functions
- Modal function outputs displayed in Gradio
- File transfers between systems
## Data Flow
1. **User Input** → Gradio UI
2. **Task Definition** → Modal Agent
3. **Orchestration Request** → MCO MCP Server
4. **Directive** → Modal Agent
5. **Execution Results** → MCO MCP Server
6. **Status Updates** → Gradio UI
7. **Final Output** → User
## Technical Requirements
### Modal Implementation
- Python SDK installation
- Function definitions with proper decorators
- API key management
- Code interpreter implementation
- File system access
### MCO Server Setup
- NPM package installation
- SNLP file configuration
- MCP tool definitions
- Stdio transport configuration
### Gradio UI Development
- Component layout and styling
- Real-time update mechanisms
- Thinking process visualization
- Log display implementation
- SNLP editor with toggle functionality
## Integration Challenges
1. **Cross-System Communication**:
- Modal functions communicating with MCO server
- Real-time updates from MCO to Gradio
2. **State Management**:
- Maintaining agent state across steps
- Tracking orchestration progress
3. **File Handling**:
- Transferring SNLP files between systems
- Generating and downloading files
4. **Authentication**:
- Managing Modal API keys securely
- Handling session persistence
5. **Deployment**:
- Ensuring all components work in Hugging Face Spaces
- Managing dependencies across systems
## Implementation Strategy
1. **Layered Development**:
- Build and test each component separately
- Integrate incrementally
- Validate each integration point
2. **Real-World Testing**:
- No simulations or mock data
- End-to-end testing with real API calls
- Validate with actual MCO orchestration
3. **Fallback Mechanisms**:
- Handle API failures gracefully
- Provide clear error messages
- Implement retry logic where appropriate
## Success Metrics
1. **Functionality**:
- Agent successfully orchestrated by MCO
- SNLP files correctly generated and processed
- All components communicate properly
2. **User Experience**:
- Clear visualization of thinking process
- Intuitive SNLP editing
- Responsive UI with real-time updates
3. **Technical Quality**:
- No simulations or mock data
- Robust error handling
- Cross-platform compatibility
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