genux 0.1.9

Multi-Agent Linux Command Execution System

Genux - A Multi-Agent Linux Command System

A AI-powered system that converts natural language requests into safe and executable Linux commands and execuutes them using a multi-agent architecture.

Features

• Natural Language Processing: Convert plain English requests into Linux commands
• Multi-Agent Architecture: Four specialized agents working together for optimal results
• Safety First: Built-in security analysis and risk assessment
• Knowledge Base: Learns from successful executions to improve future performance
• Web Search Integration: Searches for current information when needed
• Error Recovery: Automatic replanning when commands fail

Architecture

Here’s an overview of the system architecture showing how different components and agents interact with each other:

The system consists of four specialized agents:

1. Comprehend Agent

• Analyzes natural language input
• Determines task complexity and requirements
• Searches for additional context when needed

2. Planner Agent

• Creates step-by-step execution plans
• Leverages knowledge base for similar requests
• Generates Linux commands optimized for the task

3. Examiner Agent

• Performs security risk assessment
• Categorizes commands by danger level
• Requests user permission for risky operations

4. Execution Agent

• Safely executes approved commands
• Provides real-time feedback
• Handles errors and timeouts gracefully

📦 Installation

Prerequisites

• Python 3.8 or higher
• Linux operating system
• Internet connection for web search functionality

Required API Keys

1. Groq API Key – For AI language model access

   • Sign up at Groq Console
   • Generate an API key

2. Tavily API Key – For web search functionality

   • Sign up at Tavily
   • Get your API key

Setup Instructions

Install PyPi package

pip install genux

Or run manually

1. Clone the repository

   git clone https://github.com/Collgamer0008/Genux.git
   cd Genux
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run main:

   cd genux
   python3 main.py 
   

Usage

Run the system and choose your input method:

genux

if you have not saved API keys as Env Variables , You will be prompted to enter API keys manually

Enter your GROQ API Key: 
Enter your TAVILY API Key: 

Then, you'll be prompted to choose:

• Single line input: For simple requests
• Multi-line input: For complex, detailed requests (end with END)

---

Example Requests

Simple Commands

• Create a new folder called projects
• Install Docker on Ubuntu
• Set up a Python virtual environment

•  create folder structure like this :
    project-name/
  │
  ├── src/              
  │   ├── main/       
  │   ├── utils/       
  │   └── __init__.py   
  │
  ├── tests/            
  │
  ├── docs/             
  │
  ├── config/          
  │
  ├── scripts/         
  │
  ├── .gitignore       
  ├── README.md         
  ├── requirements.txt  
  └── LICENSE ```
  

Dynamic Content

• Create a file with today's weather information
• Generate a report with current cryptocurrency prices
• Make a list of trending GitHub repositories

---

Security

The Examiner Agent checks the command plan generated for possible damageable commands or commands that install modules, etc, if present, asks for user permission to execute the plan, which adds a layer of human in the loop security, and categorizes the command into risk levels

Risk Assessment Levels

• Low Risk: Simple file operations, directory navigation
• Medium Risk: Package installations, non-system modifications
• High Risk: System file changes, service modifications, network configuration

Safety Mechanisms

• Command analysis before execution
• User permission requests for dangerous operations
• Timeout protection (5 minutes per command)
• Error handling and recovery
• No automatic system-level changes without approval

---

Knowledge Base

The system maintains a local knowledge base (command_action-kb.json) that:

• Stores successfully executed plans
• Enables faster responses for similar requests
• Improves accuracy over time
• Uses fuzzy matching to find relevant past solutions

Configuration

You can modify these parameters only if you install genux manually

Customizing the LLM Model

Edit the model configuration in the code:

llm = ChatGroq(
    api_key=os.environ.get("GROQ_API_KEY"),
    model="deepseek-r1-distill-llama-70b",  # Change this
    temperature=0.3
)

Adjusting Risk Thresholds

Modify the security classification in ExaminerAgent.examine_plan() to adjust what commands require permission.

Requirements

langchain>=0.1.0
langchain-groq>=0.1.0
langchain-community>=0.0.20
tavily-python>=0.3.0
langchain-tavily>=0.2.11
pyfiglet>=1.0.4

Contributing

1. Fork the repository
2. Create a feature branch

   git checkout -b feature/amazing-feature
   

3.Commit your changes

git commit -m "Add amazing feature"

4.Push to the branch

git push origin feature/amazing-feature

5.Open a Pull Request