Open Cursor Agent

An open-source autonomous AI agent implementation inspired by Cursor Agent, built on top of Swarms - the enterprise-grade production-ready multi-agent orchestration framework. This production-grade agent can autonomously plan, execute, and complete complex tasks using a combination of Large Language Model reasoning and tool execution.

Built with Swarms Framework - Leveraging the power of Swarms, the leading open-source framework for building production-ready multi-agent systems. Swarms provides the robust infrastructure, agent orchestration, and enterprise-grade reliability that makes this agent possible.

Overview

Open Cursor Agent is a sophisticated AI agent capable of:

• Autonomous Task Planning: Breaking down complex tasks into manageable, sequential subtasks
• Multi-Tool Execution: Leveraging various tools including file operations, command execution, and web search
• Intelligent Reasoning: Using LLM-powered thinking to analyze situations and decide next actions
• State Management: Tracking task progress through well-defined execution states
• Error Handling: Robust error detection and recovery mechanisms

Features

Installation

Prerequisites

• Python 3.8 or higher
• API key for your chosen LLM provider (e.g., OpenAI)

Setup

# Clone the repository
git clone https://github.com/kyegomez/Open-Cursor-Agent
cd Open-Cursor-Agent

# Install dependencies
pip install -r requirements.txt

Environment Variables

WORKSPACE_DIR=""
OPENAI_API_KEY=""
ANTHROPIC_API_KEY=""

Usage

from open_cursor.main import OpenCursorAgent

# Initialize the agent
agent = OpenCursorAgent(
    model_name="gpt-4o",
    workspace_path=".",
)

# Example task
task_description = """
Create a transformer model in pytorch in a file called transformer.py"
"""

result = agent.run(task_description)

print(result)

Architecture

graph LR
    A[User Task] --> B[Initialize]
    B --> C[Planning]
    C --> D[Execution]
    D --> E[Thinking]
    E --> F{Complete?}
    F -->|No| D
    F -->|Yes| G[Results]
    
    C -.-> H[LLM]
    D -.-> H
    E -.-> H
    D -.-> I[Tools]
    
    style B fill:#4a90e2,color:#fff
    style C fill:#9b59b6,color:#fff
    style D fill:#e74c3c,color:#fff
    style E fill:#f39c12,color:#fff
    style G fill:#27ae60,color:#fff

Execution Flow

The agent operates through a state machine with the following phases:

1. Initialization: Task context is created and main task is registered
2. Planning Phase: LLM generates a detailed execution plan with subtasks
3. Execution Phase: Each subtask is executed using appropriate tools
4. Thinking Phase: Results are analyzed and next actions determined
5. Completion: All tasks are finalized and results are returned

Agent States

• INITIALIZING: Setting up the task context
• PLANNING: Creating a detailed execution plan
• EXECUTING: Performing planned actions
• THINKING: Analyzing results and determining next steps
• COMPLETED: Task successfully finished
• ERROR: Error encountered during execution
• PAUSED: Execution temporarily halted

Contributing

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch
3. Make your changes with appropriate tests
4. Submit a pull request with a clear description

License

This project is licensed under the terms specified in the LICENSE file.

Acknowledgments

Special Thanks: To Swarms Team and the entire Swarms community for building the infrastructure that makes advanced AI agents accessible to everyone. This project stands on the shoulders of giants.