| ---
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| title: ShallowCodeResearch
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| emoji: 📉
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| colorFrom: blue
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| colorTo: pink
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| sdk: gradio
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| sdk_version: 5.33.0
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| app_file: app.py
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| pinned: false
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| short_description: Coding research assistant that generates code and tests it
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| tags:
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| - mcp
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| - multi-agent
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| - research
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| - code-generation
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| - ai-assistant
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| - gradio
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| - python
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| - web-search
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| - llm
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| - modal
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| python_version: "3.12"
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| ---
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| ---
|
|
|
| # MCP Hub - Multi-Agent AI Research & Code Assistant
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|
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| 🚀 **Advanced multi-agent system for AI-powered research and code generation**
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|
|
| ## What is MCP Hub?
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|
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| 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.
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|
|
| ## ✨ Key Features
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|
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| - 🧠 **Multi-Agent Architecture**: Specialized agents working in orchestrated workflows
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| - 🔍 **Intelligent Research**: Web search with automatic summarization and citation formatting
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| - 💻 **Code Generation**: Context-aware Python code creation with secure execution
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| - 🔗 **MCP Server**: Built-in MCP server for seamless agent communication
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| - 🎯 **Multiple LLM Support**: Compatible with Nebius, OpenAI, Anthropic, and HuggingFace
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| - 🛡️ **Secure Execution**: Modal sandbox environment for safe code execution
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| - 📊 **Performance Monitoring**: Advanced metrics collection and health monitoring
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|
|
| ## 🚀 Quick Start
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|
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| 1. **Configure your environment** by setting up API keys in the Settings tab
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| 2. **Choose your LLM provider** (Nebius recommended for best performance)
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| 3. **Input your research query** in the Orchestrator Flow tab
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| 4. **Watch the magic happen** as agents collaborate to research and generate code
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|
|
| ## 🏗️ Architecture
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|
|
| ### Core Agents
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|
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| - **Question Enhancer**: Breaks down complex queries into focused sub-questions
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| - **Web Search Agent**: Performs targeted searches using Tavily API
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| - **LLM Processor**: Handles text processing, summarization, and analysis
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| - **Citation Formatter**: Manages academic citation formatting (APA style)
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| - **Code Generator**: Creates contextually-aware Python code
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| - **Code Runner**: Executes code in secure Modal sandboxes
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| - **Orchestrator**: Coordinates the complete workflow
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|
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| ### Workflow Example
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|
|
| ```
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| User Query: "Create Python code to analyze Twitter sentiment"
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| ↓
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| Question Enhancement: Split into focused sub-questions
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| ↓
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| Web Research: Search for Twitter APIs, sentiment libraries, examples
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| ↓
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| Context Integration: Combine research into comprehensive context
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| ↓
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| Code Generation: Create executable Python script
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| ↓
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| Secure Execution: Run code in Modal sandbox
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| ↓
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| Results: Code + output + research summary + citations
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| ```
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|
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| ## 🛠️ Setup Requirements
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|
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| ### Required API Keys
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|
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| - **LLM Provider** (choose one):
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| - Nebius API (recommended)
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| - OpenAI API
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| - Anthropic API
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| - HuggingFace Inference API
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| - **Tavily API** (for web search)
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| - **Modal Account** (for code execution)
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|
|
| ### Environment Configuration
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|
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| Set these environment variables or configure in the app:
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|
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| ```bash
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| LLM_PROVIDER=nebius # Your chosen provider
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| NEBIUS_API_KEY=your_key_here
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| TAVILY_API_KEY=your_key_here
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| # Modal setup handled automatically
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| ```
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|
|
| ## 🎯 Use Cases
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|
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| ### Research & Development
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| - **Academic Research**: Automated literature review and citation management
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| - **Technical Documentation**: Generate comprehensive guides with current information
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| - **Market Analysis**: Research trends and generate analytical reports
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|
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| ### Code Generation
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| - **Prototype Development**: Rapidly create functional code based on requirements
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| - **API Integration**: Generate code for working with various APIs and services
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| - **Data Analysis**: Create scripts for data processing and visualization
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|
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| ### Learning & Education
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| - **Code Examples**: Generate educational code samples with explanations
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| - **Concept Exploration**: Research and understand complex programming concepts
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| - **Best Practices**: Learn current industry standards and methodologies
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|
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| ## 🔧 Advanced Features
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|
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| ### Performance Monitoring
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| - Real-time metrics collection
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| - Response time tracking
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| - Success rate monitoring
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| - Resource usage analytics
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|
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| ### Intelligent Caching
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| - Reduces redundant API calls
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| - Improves response times
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| - Configurable TTL settings
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|
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| ### Fault Tolerance
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| - Circuit breaker protection
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| - Rate limiting management
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| - Graceful error handling
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| - Automatic retry mechanisms
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|
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| ### Sandbox Pool Management
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| - Pre-warmed execution environments
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| - Optimized performance
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| - Resource pooling
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| - Automatic scaling
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|
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| ## 📱 Interface Tabs
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| 1. **Orchestrator Flow**: Complete end-to-end workflow
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| 2. **Individual Agents**: Access each agent separately for specific tasks
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| 3. **Advanced Features**: System monitoring and performance analytics
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|
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| ## 🤝 MCP Integration
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| This application demonstrates advanced MCP (Model Context Protocol) implementation:
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|
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| - **Server Architecture**: Full MCP server with schema generation
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| - **Function Registry**: Proper MCP function definitions with typing
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| - **Multi-Agent Communication**: Structured data flow between agents
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| - **Error Handling**: Robust error management across agent interactions
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|
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| ## 📊 Performance
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| - **Response Times**: Optimized for sub-second agent responses
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| - **Scalability**: Handles concurrent requests efficiently
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| - **Reliability**: Built-in fault tolerance and monitoring
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| - **Resource Management**: Intelligent caching and pooling
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|
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| ## 🔍 Technical Details
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|
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| - **Python**: 3.12+ required
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| - **Framework**: Gradio with MCP server capabilities
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| - **Execution**: Modal for secure sandboxed code execution
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| - **Search**: Tavily API for real-time web research
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| - **Monitoring**: Comprehensive performance and health tracking
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|
|
| ---
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|
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| **Ready to experience the future of AI-assisted research and development?**
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| Start by configuring your API keys and dive into the world of multi-agent AI collaboration! 🚀
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|
