Transform your $20 Cursor/Windsurf into a Devin-like experience in one minute! This repository contains configuration files and tools that enhance your Cursor or Windsurf IDE with advanced agentic AI capabilities similar to Devin, including:

• Process planning and self-evolution
• Extended tool usage (web browsing, search, LLM-powered analysis)
• Automated execution (for Windsurf in Docker containers)

You can install cursor-agent using pip:

# Install from PyPI
pip install cursor-agent

# Initialize in current directory
cursor-agent

# Or specify a target directory
cursor-agent /path/to/project

You can also run cursor-agent using Docker:

# Using docker directly
docker run -v $(pwd):/workspace -e OPENAI_API_KEY=your_key cursor-agent /workspace

# Or using docker-compose
export TARGET_DIR=$(pwd)  # Directory to initialize
export OPENAI_API_KEY=your_key  # Your API keys
docker-compose up

Available environment variables:

• TARGET_DIR: Directory to initialize (default: current directory)
• OPENAI_API_KEY: OpenAI API key
• ANTHROPIC_API_KEY: Anthropic API key
• DEEPSEEK_API_KEY: DeepSeek API key
• GOOGLE_API_KEY: Google API key

To get the latest version:

# Check and update to latest version
python -m cursor_agent.update

# Force update even if current version is up to date
python -m cursor_agent.update --force

The easiest way to add Cursor agent capabilities to your project is using the initialization script:

# Initialize in current directory
python init_cursor_agent.py

# Or specify a target directory
python init_cursor_agent.py /path/to/project

# Force overwrite existing files (creates backups)
python init_cursor_agent.py --force

# Skip virtual environment creation
python init_cursor_agent.py --skip-venv

The script will:

1. Copy necessary configuration files
2. Set up Python virtual environment
3. Install required dependencies
4. Configure environment variables

If you prefer manual setup, follow these steps:

1. Create Python virtual environment:

# Create a virtual environment in ./venv
python3 -m venv venv

# Activate the virtual environment
# On Unix/macOS:
source venv/bin/activate
# On Windows:
.\venv\Scripts\activate

2. Configure environment variables:

# Copy the example environment file
cp .env.example .env

# Edit .env with your API keys and configurations

3. Install dependencies:

# Install required packages
pip install -r requirements.txt

# Install Playwright's Chromium browser (required for web scraping)
python -m playwright install chromium

• Web scraping with JavaScript support (using Playwright)
• Search engine integration (DuckDuckGo)
• LLM-powered text analysis
• Process planning and self-reflection capabilities

After installation, the following command-line tools are available:

1. cursor-agent: Initialize a directory with Cursor agent capabilities

   cursor-agent [directory]

2. cursor-llm: Interact with various LLM providers

   cursor-llm --prompt "Your prompt" --provider "anthropic"
   # Supported providers: OpenAI (default), DeepSeek, Anthropic, Gemini, Local LLM

3. cursor-scrape: Web scraping with JavaScript support

   cursor-scrape --max-concurrent 3 URL1 URL2 URL3

4. cursor-search: Search engine integration

   cursor-search "your search keywords"

5. cursor-update: Update cursor-agent to latest version

   cursor-update
   cursor-update --force  # Force update

6. cursor-verify: Verify setup and dependencies

   cursor-verify

The project uses pytest for testing. To run tests:

# Install test dependencies
pip install pytest pytest-cov

# Run all tests with coverage
pytest

# Run specific test file
pytest tests/test_init_cursor_agent.py

# Run tests excluding slow ones
pytest -m "not slow"

# Run only unit tests
pytest -m unit

The project uses GitHub Actions for continuous integration, running tests on:

• Multiple Python versions (3.8, 3.9, 3.10, 3.11)
• Multiple operating systems (Ubuntu, Windows, macOS)

The CI pipeline:

1. Runs all tests
2. Generates coverage reports
3. Uploads coverage to Codecov
4. Fails if coverage drops below threshold

The project uses automated changelog generation based on conventional commits.

1. Commit Message Format:

   type(scope): description
   
   [optional body]
   [optional footer]
   

   Types:

   • feat: New feature
   • fix: Bug fix
   • docs: Documentation
   • style: Formatting
   • refactor: Code restructuring
   • perf: Performance improvement
   • test: Tests
   • build: Build system
   • ci: CI/CD
   • chore: Maintenance

2. Generate Changelog:

   # Preview changelog
   python tools/generate_changelog.py
   
   # Update CHANGELOG.md
   python tools/generate_changelog.py --update
   
   # Specify version
   python tools/generate_changelog.py --version v1.0.0

3. Automated Generation:

   • Changelog is automatically generated on new releases
   • Generated from commits since last tag
   • Categorized by commit type
   • Included in GitHub release notes

The project supports multiple deployment methods:

1. PyPI Package:

   # Install latest release
   pip install cursor-agent
   
   # Install specific version
   pip install cursor-agent==1.0.0

2. Docker Container:

   # Build locally
   docker build -t cursor-agent .
   
   # Run with volume mount
   docker run -v /path/to/project:/workspace cursor-agent

3. Manual Setup:

   git clone https://github.com/grapeot/devin.cursorrules.git
   cd devin.cursorrules
   python init_cursor_agent.py /path/to/project

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

For detailed information about the motivation and technical details behind this project, check out the blog post: Turning $20 into $500 - Transforming Cursor into Devin in One Hour

MIT License