DIY Your AI Agent: AI-powered Smart Terminal Assistant

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🔥 Project Overview

This project is an AI-powered smart terminal assistant (Linux Agent) based on Large Language Models (LLM). It automatically analyzes user input, determines whether a terminal command needs to be executed, and returns results in JSON format, ensuring secure interactions.

Collaboration Process Between Conversation and Command Execution:

User Input → LLM Intent Recognition → Determine if Command Execution is Needed →  
   → Yes: LLM Generates Command → Execute Command → Return Result → Generate Natural Language Response  
   → No: Directly Generate Natural Language Response  

Key Features:

• 🖥 Intelligent Parsing: Determines whether user requests involve file operations, process management, etc.
• 🔒 Security Protection: Automatically prompts for confirmation when encountering dangerous commands (e.g., rm -rf).
• 🔄 Interactive Experience: Provides structured JSON responses to ensure readability and scalability.
• ⚡ Instant Execution: Automatically executes commands in the Bash terminal upon user confirmation and returns results.

🛠 Example Usage

Start the interactive smart terminal:

python agent_en.py

Then enter any Linux-related command, for example:

Smart_Shell> Show the files in the current directory

Agent Response (JSON Format):

{
  "action": "execute_command",
  "command": "ls -l",
  "explanation": "Lists detailed information of the current directory"
}

If the command is potentially dangerous, such as rm -rf, the system will automatically prompt for a second confirmation.

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📖 How It Works

This AI Agent operates based on LLM + rule constraints, with core logic including:

1. LLM-based reasoning: Uses models like Qwen/Qwen2.5-7B-Instruct (or other LLMs) to analyze user input.
2. Rule-based guidance: Ensures AI responses comply with predefined formats (e.g., JSON output).
3. Interactive mechanism: Users manually confirm commands to prevent accidental operations.
4. Terminal execution: Uses subprocess.Popen() to run Bash commands and return execution results.

The simplified version of the code (MVP): You can check agent_mvp_en.py to browse it.

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🚀 Quick Start

1️⃣ Environment Setup

Requires Python 3.8+, and install the necessary dependencies:

pip install openai rich

2️⃣ Configure API

Modify agent_en.py:

url = "your_api_url"
api_key = "your_api_key"

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💡 How to Improve?

If you want to make this Agent more intelligent and powerful, consider the following approaches:

1️⃣ Ask the Friendly AI 🤖

• Engage in discussions with the model directly in the terminal to refine your ideas.
• Ask questions like: "How can I make the Agent more autonomous?" or "How can I optimize my terminal assistant?"

2️⃣ Model Context Protocol (MCP) 🏗️

• MCP (Model Context Protocol) is an open protocol designed to seamlessly integrate Large Language Models (LLMs) with external data sources and tools.
• By standardizing interactions between AI systems and data sources, MCP enables models to generate more accurate and contextually relevant responses.
• Refer to the MCP Official Repository and Anthropic Blog to learn more.

3️⃣ AI-driven Automation Scripts 🚀

• Enable AI not just to execute commands but also to invoke other automation scripts, forming a more adaptive workflow.
• Write automation scripts for tasks like batch file organization or automated log analysis, and let AI call those best practices at appropriate times to achieve higher-level intelligent automation.

By implementing these improvements, your AI terminal assistant can evolve beyond just a "command parser" into a truly intelligent Agent! 💡✨

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📜 License

This project is licensed under the MIT License—feel free to modify and use it!

🤝 Contributions

Issues & PRs are welcome! If you have better ideas, feel free to contribute code!