folder2text 1.0.0

Turn any folder into AI-ready text. Requires Folder2Text app from Microsoft Store.

Folder2Text CLI

Turn any folder into AI-ready text. Bundle code context for ChatGPT, Claude, Gemini, NotebookLM, LLaMA, Mistral, DeepSeek, Grok and other LLMs.

Requirements

• Folder2Text app installed from Microsoft Store
• Python 3.10+
• Windows 10/11

Install

pip install folder2text

Quick Start

# Extract a folder and pipe to clipboard (paste into ChatGPT/Claude)
folder2text ./myproject --stdout | clip

# Extract to a file
folder2text ./myproject -o context.txt

# Check license and trial status
folder2text status

How It Works

The CLI connects to the Folder2Text desktop app's local REST API. The app handles extraction, filtering, and licensing. The CLI is a thin terminal client -- it requires the app to be installed and running.

folder2text CLI  --->  localhost API  --->  Folder2Text.exe (Store)
                      (port + secret)       extraction + trial logic

All processing happens locally. Your code never leaves your machine.

Why Folder2Text?

• LLM-Ready Output: Structured text formatted for maximum context window efficiency
• Smart Filtering: Auto-excludes node_modules, .git, binaries, and lock files
• 100% Private: Everything runs locally, no cloud, no telemetry
• 31 Languages: UI available in 31 languages
• Lightweight: ~90MB app, zero-dependency CLI

Commands

Command	Description
folder2text <paths...>	Extract folder(s) to output file
folder2text <paths...> --stdout	Print extracted text to stdout (pipe to LLM)
folder2text <paths...> -o FILE	Custom output file name
folder2text status	Show license tier and trial remaining
folder2text --version	Show CLI version
folder2text --help	Show help + Store link

Pricing

• Free trial: 3 extractions (up to 500 files each)
• Standard: 14.99 EUR one-time (lifetime license)
• Pro Monthly: 6.99 EUR/month
• Pro Annual: 59 EUR/year (save ~30%)

All payments handled through Microsoft Store.

Links

• Microsoft Store
• Website
• Privacy Policy
• Support

License

MIT