README.md

---

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:

```bash

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! 🚀