CodeLens AI

AI-Powered Intelligent Code Analysis Platform

Automated Code Review • Refactoring • Test Generation • Observability • Analytics

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📖 Overview

CodeLens AI is a production-style full-stack platform that leverages Large Language Models (LLMs) to provide intelligent code analysis capabilities for developers.

The platform enables automated code review, code explanation, refactoring suggestions, and unit test generation through OpenRouter-powered AI integration while maintaining enterprise-grade observability, caching, authentication, and monitoring infrastructure.

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✨ Key Features

• AI Analysis: Automated code review, natural language explanations, and refactoring suggestions.
• Test Generation: Instant AI-generated unit tests for multiple programming languages.
• Security: Secure JWT-based authentication and user authorization.
• Performance: Redis caching layer for optimized API response times and LLM costs.
• Persistence: MongoDB-based storage for analysis history and user data.
• Observability: Real-time Prometheus metrics and interactive Grafana dashboards.
• Deployment: Fully Dockerized multi-service environment for easy setup.

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🏗 System Architecture

Frontend (React.js)
        │
        ▼
Backend API (Express.js)
        │
 ┌──────┴──────┐
 ▼             ▼
MongoDB      Redis
(Storage)    (Caching)
        │
        ▼
OpenRouter API
(LLM Integration)
        │
        ▼
Prometheus + Grafana
(Monitoring & Analytics)

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🛠 Technology Stack

Frontend

• Framework: React.js
• State/Data: Axios, Recharts
• UI/UX: React Hot Toast

Backend

• Runtime: Node.js (Express.js)
• Database: MongoDB
• Cache: Redis
• Security: JWT Authentication
• Logging: Winston

AI & DevOps

• LLM Provider: OpenRouter API
• Containerization: Docker, Docker Compose
• Monitoring: Prometheus, Grafana

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🚀 Getting Started

1. Clone the Repository

git clone [https://github.com/sandeep7348/ai-code-analysis-platform.git](https://github.com/sandeep7348/ai-code-analysis-platform.git)
cd ai-code-analysis-platform

2. Environment Configuration

Create a .env file in the backend/ directory:

OR_API_KEY=your_openrouter_api_key
OR_BASE=[https://openrouter.ai/api/v1](https://openrouter.ai/api/v1)
OR_MODEL=meta-llama/llama-3.3-8b-instruct:free
PORT=5000
JWT_SECRET=your_secure_jwt_secret
CORS_ORIGIN=http://localhost:3000
MONGO_URI=mongodb://mongodb:27017/codelens
REDIS_URL=redis://redis:6379

3. Run Application

docker-compose up --build

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📊 Application Services

Service	URL
Frontend	http://localhost:3000
Backend API	http://localhost:5000
Grafana Dashboard	http://localhost:3001
Prometheus Metrics	http://localhost:9090

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🔌 API Reference

AI Analysis

POST /api/analyze

Payload:

{
  "code": "function add(a, b) { return a + b }",
  "language": "javascript",
  "mode": "review" 
}

*Supported Modes: review, explain, refactor, test*

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📈 Monitoring & Observability

The platform includes integrated monitoring for performance analysis:

• API request rates and duration analytics.
• AI Model response latency.
• Redis cache hit/miss ratio.
• Grafana Credentials: admin / grafana123

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📸 Screenshots

Main Dashboard

AI Analysis Results

History

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👤 Author

Sandeep Choudhary GitHub Profile