open-cursor-agent 0.1.0

Swarms - TGSC

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

Feature	Description
File system operations	Read, write, search, and manage files
Command execution	Execute commands with timeout and security controls
Web search integration	Access real-time information via web search
Task dependency management	Manage tasks with priority awareness
Execution history tracking and logging	Record and monitor action history and logs
Workspace isolation	Ensure security-first approach to isolate workspace

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.