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A lightweight Python utility that aggregates and exports comprehensive system information to JSON, specifically designed for feeding system context into Large Language Models (LLMs).

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system-info-now

A lightweight Python utility that aggregates and exports comprehensive system information to JSON, specifically designed for feeding system context into Large Language Models (LLMs).

Description

system-info-now provides a snapshot of your system's current state in a standardized, easy-to-parse format. It gathers real-time data about your operating system, hardware, running processes, and environment variables, making it ideal for:

  • System diagnostics
  • Environment documentation
  • Troubleshooting
  • Providing contextual information to AI models and language models

By generating a detailed JSON output, the tool enables seamless integration of system context into AI-powered workflows, allowing LLMs to understand the specific environment and configuration details.

⚠️ IMPORTANT: Sensitive Data Warning

CAUTION: This tool collects comprehensive system information that may include:

  • Environment variables
  • Path details
  • User information
  • Potentially sensitive configuration data

🛡️ ALWAYS REVIEW THE OUTPUT FILE BEFORE SHARING OR FEEDING TO ANY LLM

Features

System Debug Data

  • Operating system details and version
  • CPU and GPU specifications
  • Memory usage and statistics
  • Disk utilization
  • Network interface information
  • Current running processes
  • Git repository status
  • System uptime and load averages
  • Environment variables
  • User information

Language-Specific Debug Data

Currently supported (disabled by default):

  • Python environment details
  • JavaScript environment information

More languages coming soon!

Operating System Support

Currently supported:

  • macOS
  • Linux

Coming soon:

  • Windows

Installation

  1. Clone the repository:
git clone https://github.com/bjoaquinc/system-info-now.git
cd system-info-now
  1. Install required dependencies:
pip install psutil pyyaml

Usage

  1. Basic usage:

Run the script from the project directory:

python main.py
  1. Configuration (optional): Update the config.yaml file in the project root:
# System Info Now Configuration File
output:
  format: json # Only json is currently supported
  directory: output
  filename: system_data.json

logging:
  level: INFO
  directory: logs

# Customizable root directory path for project 
# specific context gathering used by language 
# collectors
project:
  root_dir: "." # Default to current directory, can be set to absolute path

collectors:
  # Core system information - recommended to keep enabled
  system: true
  
  # Language-specific collectors - disabled by default
  # WARNING: When feeding data to LLMs, the complex structure with many versions, 
  # paths, and package details in these collectors may cause hallucinations.
  # Only enable if you specifically need this language data.
  python: false
  javascript: false
  

This will create an output/system_data.json file in your current directory.

Requirements

  • Python 3.x
  • Mac OS or Linux (Working on supporting Windows soon)

Output Example

{
  "system_debug_data": {
    // ... system details including OS, CPU, GPU, memory, disk, network info
  },
  "python_debug_data": {
    // ... Python runtime information, paths, dependencies, and virtual environment details
  },
  "javascript_debug_data": {
    // ... Node.js version, global packages, installed browsers, and npm configuration
  }
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Future Plans

  • Support more operating systems (Windows coming soon)
  • Support more languages for debugging
  • Support more useful data in existing languages

Support

If you encounter any problems or have suggestions, please file an issue on the GitHub repository.

Authors

  • Joaquin Coromina (@bjoaquinc)
  • Amar Begovic (@AmarBego)

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A lightweight Python utility that aggregates and exports comprehensive system information to JSON, specifically designed for feeding system context into Large Language Models (LLMs).

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