AetherShell

AetherShell is an AI-powered, offline Linux shell assistant that interprets natural language, plans tasks, and securely executes commands using a local LLM (Mistral-7B). Designed for privacy-first terminal automation.

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Why AetherShell?

• Offline and Secure: No internet dependency — all AI runs locally.
• Natural Language Interface: Replace cryptic shell syntax with plain English.
• Local LLM: Integrates llama-cpp and Mistral for fast, private AI inference.
• Productive by Design: Multi-step command execution and persistent memory.
• Ideal For: Developers, sysadmins, AI enthusiasts, and privacy-conscious users.

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Features — Smarter, Safer, AI-Enhanced Terminal

1. Natural Language Shell Commands

Interact with your Linux terminal in plain English:

“Create a folder called test and open it.”

AetherShell understands and converts it to:

mkdir test && cd test

• Skip memorizing complex syntax
• Ideal for both beginners and advanced users
• Fast, intuitive command translation

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2. Local AI Integration (LLM-powered)

Powered by llama-cpp and Mistral-7B, AetherShell runs entirely offline on your machine.

“List the 10 biggest files in this directory.”

AetherShell understands and converts it to:

du -ah | sort -rh | head -n 10

• No internet required
• Maximum privacy and performance
• Works on air-gapped systems

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3. Dynamic Multi-Step Execution

AetherShell understands and executes multi-step tasks intelligently.

“Install Python, create a virtual environment, and activate it.”

sudo apt install python3
python3 -m venv venv
source venv/bin/activate

• Automates routine tasks
• Reduces manual effort
• Task planning handled by AI

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4. Memory Persistence

AetherShell saves session history and context in a structured format (aether_memory.json).

• Maintains task continuity
• Remembers prior commands
• Enables contextual conversations

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5. Offline Capability

All features work without an internet connection.

• Fully self-contained
• No external API calls
• Perfect for private, secure environments

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6. Optional Cloud Fallback (WIP)

Future support for hybrid execution using cloud models when needed.

• Seamless switch to cloud using claude, gemini, etc.
• Useful for low-end systems
• Still prioritizes local-first privacy
• This feature will only be executed, if allowed by the user.

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7. Secure Execution Sandbox (WIP)

AetherShell will feature isolated environments to run commands securely.

• Limits system-level access
• Helps prevent accidental damage
• Ideal for testing and sandboxed execution
• Ideal for script development.

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Quick Start

Requirements

• Python 3.8+
• curl
• Linux OS (Debian-based recommended)
• At least 6 GB RAM for LLM execution

Prerequisites

Before running setup.sh, ensure the following tools are installed on your system:

• curl – for downloading the model
• python3 and venv – for setting up the environment

You can install them on Debian/Ubuntu systems using:

sudo apt update
sudo apt install curl python3 python3-venv

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Installation

git clone https://github.com/hiteshdhawan/Aethershell.git
cd Aethershell
bash setup.sh

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Execution

source venv/bin/activate
python assistant.py

Folder Structure

├── assistant.py
├── action_planner.py
├── step_executor.py
├── executor.py
├── memory.py
├── prompt_engine.py
├── system_context.py
├── requirements.txt
├── setup.sh
├── models/                # Stores downloaded GGUF model
├── aether_memory.json     # Stores task memory/logs
└── venv/                  # Virtual environment (ignored)

Model

This project uses the following model via llama-cpp-python: mistral-7b-instruct-v0.1.Q4_K_M.gguf (Auto downloaded using setup.sh)

Execution speed may vary depending on your system specs.

How to Fork and Contribute

1. Fork this repository by clicking the "Fork" button on the top right.

2. Clone your fork locally:

   git clone https://github.com/your-username/Aethershell.git
   cd Aethershell

3. Create a branch for your feature or fix:

   git checkout -b your-feature-name

4. Make your changes, commit, and push:

   git add .
   git commit -m "Your message"
   git push origin your-feature-name

5. Open a Pull Request on GitHub and wait for review.

Author

Built and maintained by Hitesh Dhawan

Acknowledgements

Thanks to the open-source LLM and Python communities for providing the resources.

Feedback

Have ideas, suggestions, or found a bug?
Please open an Issue or start a Discussion — your feedback is welcome!

Keywords

This project is relevant to the following keywords and phrases:

• AI shell assistant
• Linux terminal automation
• Natural language to bash commands
• Offline LLM-powered shell
• Local AI terminal assistant
• Open-source terminal automation
• Mistral-7B Linux integration
• llama-cpp for shell tasks
• Natural language interface for Linux
• Secure and private AI command execution
• CLI-based AI assistant
• Developer productivity tool with AI
• Shell assistant for sysadmins
• Offline DevOps automation
• Python-powered AI shell agent