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 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.
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
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