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Devin.cursorrules

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)

Usage

  1. Copy all files from this repository to your project folder
  2. For Cursor users: The .cursorrules file will be automatically loaded
  3. For Windsurf users: Use both .windsurfrules and scratchpad.md for similar functionality

Update: Multi-Agent Support (Experimental)

This project includes experimental support for a multi-agent system that enhances Cursor's capabilities through a two-agent architecture:

Architecture

  • Planner (powered by OpenAI's o1 model): Handles high-level analysis, task breakdown, and strategic planning
  • Executor (powered by Claude): Implements specific tasks, runs tests, and handles implementation details

Actual .cursorrules file

Key Benefits

  1. Enhanced Task Quality

    • Separation of strategic planning from execution details
    • Better cross-checking and validation of solutions
    • Iterative refinement through Planner-Executor communication
  2. Improved Problem Solving

    • Planner can design comprehensive test strategies
    • Executor provides detailed feedback and implementation insights
    • Continuous communication loop for optimization

Real-World Example

A real case study of the multi-agent system debugging the DuckDuckGo search functionality:

  1. Initial Analysis

    • Planner designed a series of experiments to investigate intermittent search failures
    • Executor implemented tests and collected detailed logs
  2. Iterative Investigation

    • Planner analyzed results and guided investigation to the library's GitHub issues
    • Identified a bug in version 6.4 that was fixed in 7.2
  3. Solution Implementation

    • Planner directed version upgrade and designed comprehensive test cases
    • Executor implemented changes and validated with diverse search scenarios
    • Final documentation included learnings and cross-checking measures

Usage

To use the multi-agent system:

  1. Switch to the multi-agent branch
  2. The system will automatically coordinate between Planner and Executor roles
  3. Planner uses tools/plan_exec_llm.py for high-level analysis
  4. Executor implements tasks and provides feedback through the scratchpad

This experimental feature transforms the development experience from working with a single assistant to having both a strategic planner and a skilled implementer, significantly improving the depth and quality of task completion.

Setup

  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
  1. Configure environment variables:
# Copy the example environment file
cp .env.example .env

# Edit .env with your API keys and configurations
  1. Install dependencies:
# Install required packages
pip install -r requirements.txt

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

Tools Included

  • Web scraping with JavaScript support (using Playwright)
  • Search engine integration (DuckDuckGo)
  • LLM-powered text analysis
  • Process planning and self-reflection capabilities
  • Token and cost tracking for LLM API calls
    • Supports OpenAI (o1, gpt-4o) and Anthropic (Claude-3.5) models
    • Tracks token usage, costs, and thinking time
    • Provides session-based tracking with detailed statistics
    • Command-line interface for viewing usage statistics

Testing

The project includes comprehensive unit tests for all tools. To run the tests:

# Make sure you're in the virtual environment
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# Run all tests
PYTHONPATH=. python -m unittest discover tests/

The test suite includes:

  • Search engine tests (DuckDuckGo integration)
  • Web scraper tests (Playwright-based scraping)
  • LLM API tests (OpenAI integration)

Background

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

License

MIT License

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Magic to turn Cursor/Windsurf as 90% of Devin

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