@paradiselabs/mco-protocol/Gradio-Hackathon/mco-hackathon-final/knowledge_graph.md

MCO (Model Configuration Orchestration) MCP Server & Configuration Tool

# 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