🚀 LeeGo - Level Up Your LeetCode Game! 🚀

中文

A powerful command-line tool that lets you manage your LeetCode practice progress within your local IDE, featuring built-in spaced repetition learning, progress tracking, and Git integration. Additionally, it integrates a large language model (LLM) to boost your practice efficiency by automatically generating solutions, debugging test cases, and visualizing your weak areas.

Features

• 🎯 Problem Management

  • Add new problems with auto-generated templates
  • Submit solutions with comprehensive metadata
  • Track practice history and performance
  • Auto-archive previous solutions

• 📊 Progress Tracking

  • Visual practice heatmap
  • Learning streaks
  • Weekly goals
  • Practice statistics click here to view the statistics page introduction

• 🧠 Spaced Repetition

  • Smart review scheduling based on the Ebbinghaus Forgetting Curve
  • Review reminders for optimal learning
  • Retention rate tracking
  • Customized review intervals: 1, 3, 7, 14, 30, 90, 180 days

• 🔄 Git Integration

  • Automatic commits with detailed metadata
  • Structured commit messages
  • Practice history preservation

• 🤖 AI Integration

  • Multiple AI providers supported:
    • OpenAI (GPT-4, GPT-3.5)
    • Anthropic (Claude)
    • DeepSeek
  • Smart solution templates
  • Test case generation
  • Code analysis

Installation

Prerequisites

Before installing leego, you need to have Bun.js installed on your system:

# For macOS, Linux, and WSL
curl -fsSL https://bun.sh/install | bash

# Verify installation
bun --version

For more installation options and troubleshooting, visit bun.sh.

Install leego

npm install -g leego

Quick Start

1. Set up your workspace:

# Initialize the workspace
leego setup

# Configure LeetCode authentication
leego set-cookies

# Set AI provider (optional)
leego set-ai-key

2. Start practicing:

# Add a new problem
leego add

# Start practicing a problem
leego start <problem-number>

# Submit your solution
leego submit <problem-number>

3. Track your progress:

# View practice statistics
leego stats

# Set weekly goals
leego set-goals

Authentication

Setting Up Cookies (Recommended Method)

1. Open Chrome/Edge and go to leetcode.com
2. Log in to your LeetCode account
3. Open DevTools:
   • Press F12 or
   • Right-click anywhere and select "Inspect"
4. In DevTools:
   • Select the "Network" tab
   • Check "XHR" filter
   • Click any button on leetcode.com (e.g., click your profile)
5. In the Network panel:
   • Click any request to leetcode.com
   • In the request details, find "Headers" tab
   • Scroll to find "Cookie:" under "Request Headers"
   • Copy the entire cookie string (starts with cf_clearance= or __cfduid= and ends with _gat=1)

6. Set the cookies in leego:

leego set-cookies

7. Paste the copied cookie string when prompted

The tool will verify the cookies work before saving them.

Commands

Problem Management

• leego add [problem-number]

  • Add a new LeetCode problem
  • Auto-generates solution template and test files
  • Organizes problems by type
  • Uses AI for template generation (if configured)

• leego start <problem-number>

  • Start practicing a problem
  • Initializes test environment
  • Archives previous attempts
  • Starts test watch mode

• leego submit <problem-number>

  • Submit your solution
  • Runs tests
  • Records practice metadata
  • Updates learning progress
  • Creates Git commit

Progress Tracking

• leego stats

  • Opens statistics dashboard in browser
  • Shows practice heatmap
  • Displays learning progress
  • Shows review schedule

• leego set-goals

  • Set weekly practice goals
  • Track completion rate
  • View historical performance

• 🤖 Large Language Model Support

  • Multiple LLM providers:
    • OpenAI (GPT-4, GPT-3.5)
    • Anthropic (Claude)
    • DeepSeek
    • Custom LLM implementations:
      • Local models (e.g., llama.cpp, ggml)
      • Self-hosted services
      • Alternative providers
      • Custom API endpoints
  • Intelligent code generation:
    • Solution templates
    • Test cases
    • Code analysis
    • Complexity analysis

Custom LLM Integration

You can integrate your own LLM implementation by:

1. Creating .leetcode/llm.ts in your workspace
2. Implementing the generateWithAI function:

export async function generateWithAI(prompt: string): Promise<string> {
  try {
    // Implement your custom LLM logic here
    // Examples:
    // - Call local models (llama.cpp, ggml)
    // - Use self-hosted services
    // - Connect to alternative AI providers
    // - Call custom API endpoints
    return 'Generated response';
  } catch (error) {
    throw new Error(`Custom LLM error: ${error.message}`);
  }
}

Project Structure

workspace/
├── 01-arrays-hashing/     # Problem categories
├── 02-two-pointers/
├── 03-sliding-window/
├── ...
└── problem-folder/
    ├── index.ts           # Solution implementation
    ├── index.test.ts      # Test cases
    ├── README.md          # Problem description
    └── .meta/
        ├── metadata.json  # Practice metadata
        ├── template.ts    # Original template
        └── archives/      # Previous solutions

Metadata Tracking

The tool tracks comprehensive metadata for each problem:

• Practice history
• Time spent
• Approach used
• Complexity analysis
• Test results
• Review schedule
• Retention rates

Git Integration

Automatic commits include:

solve(0011M): Container With Most Water [Medium]

Status: passed
Time Spent: 30m
Approach: Two Pointers
Time Complexity: O(n)
Space Complexity: O(1)
Timestamp: 24-01-27 15:30:45

Spaced Repetition System

The tool implements a spaced repetition system based on the Ebbinghaus Forgetting Curve:

• Review intervals: 1, 3, 7, 14, 30, 90, 180 days
• Retention rate calculation based on:
  • Time since last review
  • Number of practice attempts
  • Problem difficulty
• Smart scheduling based on performance
• Visual retention indicators

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License - see LICENSE file for details