AgentOS is a lightweight, single-file implementation that provides a strong foundation for building autonomous AI agents. It implements the core concepts outlined in Karpathy's Agent OS architecture while maintaining simplicity and extensibility.
git clone https://github.com/The-Swarm-Corporation/AgentOS.gitAgentOS is a minimal, single-file implementation designed for building autonomous AI agents based on Karpathy's Agent OS architecture. It provides a unified interface for multiple LLM providers including Claude, GPT models, and HuggingFace models, along with browser automation capabilities for web-based task execution. The framework includes tools for video and audio generation, file management, terminal operations, and multi-modal processing. AgentOS handles resource orchestration and context management automatically, making it suitable for developers building autonomous systems that need to interact with web browsers, generate content, and execute complex tasks. Built by Swarms.ai, it offers both simplicity and production-ready extensibility.
Install via pip: pip3 install -U agentos-sdk. Create an AgentOS instance with AgentOS(plan_on=False, max_loops=1) and call agent.run() with your task description. The framework automatically selects appropriate tools and LLM models based on your requirements.
Web scraping and data extraction with autonomous browser agents
Multi-modal content generation including video, audio, and text
Terminal and file operations for development task automation
Form filling and website testing with browser automation
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/The-Swarm-Corporation/AgentOSCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create an autonomous AI agent using AgentOS to manage [TASK] for [COMPANY] in the [INDUSTRY] sector. The agent should use [DATA] to perform its functions. Ensure the agent is lightweight, single-file, and follows Karpathy's Agent OS architecture.
# Autonomous AI Agent for Task Management
## Agent Overview
- **Task**: Inventory Management
- **Company**: GreenTech Solutions
- **Industry**: Renewable Energy
- **Data**: Real-time inventory levels, supplier lead times, and sales forecasts
## Agent Functions
1. **Monitor Inventory Levels**: Continuously track inventory levels for all products.
2. **Generate Purchase Orders**: Automatically create purchase orders when inventory levels fall below a specified threshold.
3. **Update Sales Forecasts**: Adjust sales forecasts based on recent sales data and market trends.
4. **Notify Stakeholders**: Send alerts to relevant stakeholders when significant changes occur.
## Agent Implementation
```python
# AgentOS Implementation for Inventory Management
class InventoryAgent:
def __init__(self, company, industry, data):
self.company = company
self.industry = industry
self.data = data
def monitor_inventory(self):
# Implementation for monitoring inventory levels
pass
def generate_purchase_orders(self):
# Implementation for generating purchase orders
pass
def update_sales_forecasts(self):
# Implementation for updating sales forecasts
pass
def notify_stakeholders(self):
# Implementation for notifying stakeholders
pass
# Initialize the agent
agent = InventoryAgent(
company="GreenTech Solutions",
industry="Renewable Energy",
data="Real-time inventory levels, supplier lead times, and sales forecasts"
)
```Framework for building applications with LLMs
Get more done every day with Microsoft Teams – powered by AI
Automate security compliance and monitor real-time security posture seamlessly.
Automate your spreadsheet tasks with AI power
Agentic AI Workflow platform
Connected workspace for docs, wikis, and projects
Take a free 3-minute scan and get personalized AI skill recommendations.
Take free scan