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CodeMentor AI - Intelligent Code Review Assistant

AI-powered code review system that analyzes code for security vulnerabilities, code quality issues, and best practice violations using Groq's fastest LLM inference.

Overview

CodeMentor AI is a full-stack application that leverages artificial intelligence to provide instant, comprehensive code reviews. It detects security vulnerabilities (SQL injection, XSS, command injection), analyzes code quality metrics, and provides actionable recommendations for improvement.

Key Features:

  • ✅ Real-time code analysis with AI-powered insights
  • ✅ Multi-language support (Python, JavaScript, Java, TypeScript, Go, Ruby, PHP, C++, etc.)
  • ✅ OWASP Top 10 vulnerability detection
  • ✅ Code quality scoring and metrics
  • ✅ Server-Sent Events (SSE) streaming for real-time results
  • ✅ Review history and analytics dashboard
  • ✅ Beautiful, responsive React UI
  • ✅ Docker Compose for easy deployment

Tech Stack

Backend

  • Framework: FastAPI (Python)
  • LLM API: Groq API (gpt-oss-20b model)
  • Code Analysis: Custom AST parser and vulnerability detector
  • Database: MongoDB with Motor (async driver)
  • Server: Uvicorn
  • Streaming: Server-Sent Events (SSE)

Frontend

  • Framework: React 18 with TypeScript
  • Build Tool: Vite
  • State Management: Zustand
  • Editor: Monaco Editor
  • Styling: Tailwind CSS
  • HTTP Client: Axios

Quick Start

Prerequisites

Local Development

  1. Backend Setup
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export GROQ_API_KEY=your_api_key_here
python -m uvicorn app.main:app --reload
  1. Frontend Setup
cd frontend
npm install
npm run dev
  1. Access

API Endpoints

Code Review (Streaming)

GET /api/review/stream?code=<code>&language=<language>

Project Structure

CodeMentorAI/
├── backend/
│   ├── app/
│   │   ├── main.py
│   │   ├── config.py
│   │   ├── api/routes.py
│   │   └── models/schemas.py
│   ├── mcp_server/
│   │   ├── server.py
│   │   └── tools/
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── hooks/
│   │   ├── services/
│   │   └── App.tsx
│   └── package.json
└── docker-compose.yml

Features

  • Real-time code analysis with streaming results
  • Multi-language support (10+ languages)
  • OWASP vulnerability detection
  • Code quality metrics and scoring
  • Review history tracking
  • Analytics dashboard
  • Beautiful responsive UI

Security

CodeMentor AI detects:

  • SQL Injection attacks
  • Cross-Site Scripting (XSS)
  • Command Injection
  • Hardcoded credentials
  • Insecure deserialization
  • Missing input validation
  • And more OWASP Top 10 vulnerabilities

Architecture

System Overview

┌─────────────────────────────────────────────────────────────┐
│                    Browser / Client                          │
│              (React 18 + TypeScript)                         │
│                  Port: 5173                                  │
└────────────────────┬────────────────────────────────────────┘
                     │ HTTP + SSE
                     ▼
┌─────────────────────────────────────────────────────────────┐
│              API Gateway (FastAPI)                           │
│              http://localhost:8000                           │
│                                                               │
│  Routes:                                                    │
│  • GET  /api/review/stream  (SSE)                          │
│  • POST /api/review         (JSON)                         │
│  • GET  /health             (Status)                       │
└────────────────────┬────────────────────────────────────────┘
                     │
        ┌────────────┼────────────┐
        │            │            │
        ▼            ▼            ▼
    ┌─────┐    ┌──────────┐  ┌─────────┐
    │ MCP │    │   Groq   │  │MongoDB  │
    │Code │    │API       │  │Database │
    │Parser   │(LLM)     │  │(Storage)│
    └─────┘    └──────────┘  └─────────┘

Frontend Architecture

Component Hierarchy

App (Main Component)
├── Header
│   └── Navigation Tabs
├── Editor Tab
│   ├── CodeEditor (Monaco)
│   └── ReviewPanel
├── History Tab
│   └── History Table
└── Analytics Tab
    └── Dashboard

State Management

Using Zustand store:

  • Code and language state
  • Analysis results
  • Review history
  • Loading/error states
  • UI state (selected tab, etc.)

API Communication

  • Standard HTTP: POST requests for complete reviews
  • Server-Sent Events: GET requests with streaming responses
  • Real-time Updates: EventSource API for SSE handling

Backend Architecture

Core Components

  1. main.py: FastAPI application entry point

    • Route definitions
    • CORS configuration
    • Error handlers
    • Server lifecycle

  2. config.py: Configuration management

    • Environment variables
    • Database settings
    • API credentials
    • Server settings

  3. api/routes.py: Endpoint definitions

    • /api/review (POST) - Standard review
    • /api/review/stream (GET) - Streaming review
    • /health (GET) - Health check

  4. models/schemas.py: Data validation

    • CodeReviewRequest model
    • Vulnerability model
    • CodeQualityMetrics model
    • CodeReviewResult model

Code Analysis Pipeline

  1. Input Parsing: Extract code and language
  2. Validation: Check code size and language support
  3. Groq API Call: Send to LLM for analysis
  4. Response Processing: Parse and structure results
  5. Quality Calculation: Compute metrics and scores
  6. Result Streaming: Send via SSE or HTTP response

MCP Server (Code Analysis)

  • server.py: Main analysis engine
  • code_parser.py: Multi-language parsing and AST analysis
  • vulnerability_rules.py: OWASP and security patterns

API Endpoints

Code Review - Streaming

GET /api/review/stream?code=<code>&language=<language>

Response: Server-Sent Events
Event 1: {"type": "start"}
Event N: {"type": "analyzing", "progress": N%}
Event N+1: {"type": "complete", "data": {...}}

Code Review - Standard

POST /api/review
Content-Type: application/json

{
  "code": "...",
  "language": "python"
}

Response:
{
  "vulnerabilities": [...],
  "quality_metrics": {...},
  "score": 0-100
}

Data Models

Vulnerability

{
  type: string;           // SQL Injection, XSS, etc.
  severity: 'critical' | 'high' | 'medium' | 'low';
  line: number;
  column: number;
  description: string;
  recommendation: string;
  cwe_id?: string;        // CWE reference
}

CodeQualityMetrics

{
  complexity: number;      // 0-100
  maintainability: number; // 0-100
  duplication: number;     // 0-100
  documentation: number;   // 0-100
  error_handling: number;  // 0-100
}

CodeReviewResult

{
  code: string;
  language: string;
  vulnerabilities: Vulnerability[];
  quality_metrics: CodeQualityMetrics;
  score: number;          // 0-100
  timestamp: Date;
}

Database Schema (MongoDB)

Collections

reviews

  • Code submissions and analysis results
  • Indexed by user_id, created_at, severity
  • Stores full review history

users (Future)

  • User accounts
  • Authentication data
  • Subscription information

Performance Optimization

Frontend Optimization

  • Code splitting
  • Lazy component loading
  • Memoization
  • Request debouncing

Backend Optimization

  • Async/await for concurrency
  • Connection pooling
  • Streaming responses
  • Result caching

Database Optimization

  • Indexed queries
  • Connection pooling
  • Document optimization

Deployment Architecture

Docker Compose

Three services:

  1. Frontend: Node.js development server or Nginx
  2. Backend: Python FastAPI server
  3. MongoDB: Database service

Services will be available at:

Production Ready

  • Health checks configured
  • Volume management for database
  • Network isolation
  • Restart policies

Security Architecture

Input Validation

  • Code size limit (50KB)
  • Language whitelist
  • Language parameter validation
  • Request rate limiting (ready)

API Security

  • CORS policy enforcement
  • Environment variable management
  • No credentials in code
  • Request/response logging

Data Security

  • MongoDB connection with auth (configurable)
  • No sensitive data in logs
  • HTTPS ready for production
  • Secure error messages

Deployment

Docker Setup

# Build and start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Environment Variables

Create a .env file in the backend directory:

# API Keys
GROQ_API_KEY=your_groq_api_key
LANGSMITH_API_KEY=optional_langsmith_key

# Database
MONGODB_URL=mongodb://localhost:27017
DATABASE_NAME=codementor

# Server
API_HOST=0.0.0.0
API_PORT=8000
API_TITLE=CodeMentor AI
API_VERSION=1.0.0

# Frontend
FRONTEND_URL=http://localhost:5173

Project Files

Backend Files

  • backend/app/main.py - FastAPI server with Groq integration
  • backend/app/config.py - Configuration management
  • backend/app/api/routes.py - API endpoint definitions
  • backend/app/models/schemas.py - Pydantic data models
  • backend/app/utils/prompts.py - AI prompt templates
  • backend/mcp_server/server.py - Code analysis engine
  • backend/mcp_server/tools/code_parser.py - Multi-language parser
  • backend/mcp_server/tools/vulnerability_rules.py - Vulnerability patterns
  • backend/requirements.txt - Python dependencies
  • backend/Dockerfile - Backend container configuration

Frontend Files

  • frontend/src/main.tsx - React entry point
  • frontend/src/App.tsx - Root component
  • frontend/src/components/Header.tsx - Navigation header
  • frontend/src/components/CodeEditor.tsx - Monaco editor
  • frontend/src/components/ReviewPanel.tsx - Results display
  • frontend/src/components/History.tsx - Review history
  • frontend/src/components/Analytics.tsx - Dashboard
  • frontend/src/hooks/useReview.ts - Review logic hook
  • frontend/src/services/api.ts - API client
  • frontend/src/store/reviewStore.ts - Zustand store
  • frontend/src/types/index.ts - TypeScript types
  • frontend/src/styles/globals.css - Global styles
  • frontend/package.json - Node dependencies
  • frontend/vite.config.ts - Vite configuration
  • frontend/tailwind.config.js - Tailwind CSS config
  • frontend/tsconfig.json - TypeScript config
  • frontend/Dockerfile - Frontend container config

Configuration Files

  • docker-compose.yml - Multi-container orchestration
  • .env.example - Environment variables template
  • .gitignore - Git ignore patterns
  • requirements.txt - Root level dependencies

Documentation Files

  • README.md - This file
  • SETUP_GUIDE.md - Detailed setup instructions
  • ARCHITECTURE.md - Technical architecture details
  • CONTRIBUTING.md - Contribution guidelines
  • CHANGELOG.md - Version history
  • LICENSE - MIT License

Testing

Test Code Examples

The repository includes comprehensive test code examples in test_code.txt:

  • Python examples (SQL injection, hardcoded credentials, command injection)
  • JavaScript examples (XSS, insecure API calls, unvalidated redirects)
  • Java examples (SQL injection, security misconfiguration)
  • TypeScript examples (unsafe type assertions)
  • Code quality issues (poor naming, high complexity)

FAQ

Q: What LLM model does it use? A: Groq's gpt-oss-20b model via free Groq API (https://groq.com)

Q: Is my code private? A: Code is analyzed in-memory and not stored unless you explicitly save reviews to MongoDB.

Q: Can I self-host? A: Yes, clone the repo and follow the local development setup. Docker Compose makes it easy.

Q: What programming languages are supported? A: Python, JavaScript, TypeScript, Java, Go, Ruby, PHP, C++, C#, Rust, Swift, and more.

Q: How accurate are the vulnerability detections? A: Achieved 85% vulnerability detection accuracy in testing. Results vary by code complexity.

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push to branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

See CONTRIBUTING.md for detailed guidelines.

License

MIT License - see LICENSE file for details

Author

Shoaib - Full Stack Developer

Roadmap

  • User authentication and account management
  • Team collaboration features
  • Integration with GitHub, GitLab, Bitbucket
  • Automated CI/CD pipeline checks
  • Custom rule creation for organizations
  • Performance benchmarking dashboard
  • Mobile app
  • API rate limiting and quotas

Support

For issues, feature requests, or questions:

  • Open an issue on GitHub
  • Check existing issues and discussions
  • Review the documentation

Star ⭐ this repository if you find it helpful!

About

CodeMentor AI is a full-stack application that leverages artificial intelligence to provide instant, comprehensive code reviews. It detects security vulnerabilities (SQL injection, XSS, command injection), analyzes code quality metrics, and provides actionable recommendations for improvement.

Topics

nodejs python cybersecurity greedy-algorithms code-analysis-true

Resources

Readme

License

MIT license

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Releases 1

Code-Mentor-V1.0 Latest
Mar 12, 2026

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