README.md

metadata

title: ShallowCodeResearch
emoji: 📉
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
short_description: Coding research assistant that generates code and tests it
tags:
  - mcp
  - multi-agent
  - research
  - code-generation
  - ai-assistant
  - gradio
  - python
  - web-search
  - llm
  - modal
python_version: '3.12'

---

MCP Hub - Multi-Agent AI Research & Code Assistant

🚀 Advanced multi-agent system for AI-powered research and code generation

What is MCP Hub?

MCP Hub is a sophisticated multi-agent research and code assistant built using Gradio's Model Context Protocol (MCP) server functionality. It orchestrates specialized AI agents to provide comprehensive research capabilities and generate executable Python code.

✨ Key Features

• 🧠 Multi-Agent Architecture: Specialized agents working in orchestrated workflows
• 🔍 Intelligent Research: Web search with automatic summarization and citation formatting
• 💻 Code Generation: Context-aware Python code creation with secure execution
• 🔗 MCP Server: Built-in MCP server for seamless agent communication
• 🎯 Multiple LLM Support: Compatible with Nebius, OpenAI, Anthropic, and HuggingFace
• 🛡️ Secure Execution: Modal sandbox environment for safe code execution
• 📊 Performance Monitoring: Advanced metrics collection and health monitoring

🚀 Quick Start

1. Configure your environment by setting up API keys in the Settings tab
2. Choose your LLM provider (Nebius recommended for best performance)
3. Input your research query in the Orchestrator Flow tab
4. Watch the magic happen as agents collaborate to research and generate code

🏗️ Architecture

Core Agents

• Question Enhancer: Breaks down complex queries into focused sub-questions
• Web Search Agent: Performs targeted searches using Tavily API
• LLM Processor: Handles text processing, summarization, and analysis
• Citation Formatter: Manages academic citation formatting (APA style)
• Code Generator: Creates contextually-aware Python code
• Code Runner: Executes code in secure Modal sandboxes
• Orchestrator: Coordinates the complete workflow

Workflow Example

User Query: "Create Python code to analyze Twitter sentiment"
    ↓
Question Enhancement: Split into focused sub-questions
    ↓
Web Research: Search for Twitter APIs, sentiment libraries, examples
    ↓
Context Integration: Combine research into comprehensive context
    ↓
Code Generation: Create executable Python script
    ↓
Secure Execution: Run code in Modal sandbox
    ↓
Results: Code + output + research summary + citations

🛠️ Setup Requirements

Required API Keys

• LLM Provider (choose one):
  • Nebius API (recommended)
  • OpenAI API
  • Anthropic API
  • HuggingFace Inference API
• Tavily API (for web search)
• Modal Account (for code execution)

Environment Configuration

Set these environment variables or configure in the app:

LLM_PROVIDER=nebius  # Your chosen provider
NEBIUS_API_KEY=your_key_here
TAVILY_API_KEY=your_key_here
# Modal setup handled automatically

🎯 Use Cases

Research & Development

• Academic Research: Automated literature review and citation management
• Technical Documentation: Generate comprehensive guides with current information
• Market Analysis: Research trends and generate analytical reports

Code Generation

• Prototype Development: Rapidly create functional code based on requirements
• API Integration: Generate code for working with various APIs and services
• Data Analysis: Create scripts for data processing and visualization

Learning & Education

• Code Examples: Generate educational code samples with explanations
• Concept Exploration: Research and understand complex programming concepts
• Best Practices: Learn current industry standards and methodologies

🔧 Advanced Features

Performance Monitoring

• Real-time metrics collection
• Response time tracking
• Success rate monitoring
• Resource usage analytics

Intelligent Caching

• Reduces redundant API calls
• Improves response times
• Configurable TTL settings

Fault Tolerance

• Circuit breaker protection
• Rate limiting management
• Graceful error handling
• Automatic retry mechanisms

Sandbox Pool Management

• Pre-warmed execution environments
• Optimized performance
• Resource pooling
• Automatic scaling

📱 Interface Tabs

1. Orchestrator Flow: Complete end-to-end workflow
2. Individual Agents: Access each agent separately for specific tasks
3. Advanced Features: System monitoring and performance analytics

🤝 MCP Integration

This application demonstrates advanced MCP (Model Context Protocol) implementation:

• Server Architecture: Full MCP server with schema generation
• Function Registry: Proper MCP function definitions with typing
• Multi-Agent Communication: Structured data flow between agents
• Error Handling: Robust error management across agent interactions

📊 Performance

• Response Times: Optimized for sub-second agent responses
• Scalability: Handles concurrent requests efficiently
• Reliability: Built-in fault tolerance and monitoring
• Resource Management: Intelligent caching and pooling

🔍 Technical Details

• Python: 3.12+ required
• Framework: Gradio with MCP server capabilities
• Execution: Modal for secure sandboxed code execution
• Search: Tavily API for real-time web research
• Monitoring: Comprehensive performance and health tracking

---

Ready to experience the future of AI-assisted research and development?

Start by configuring your API keys and dive into the world of multi-agent AI collaboration! 🚀