knowledge_graph.md

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