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MeMuFi is a cross-platform GUI application for merging multiple files and directories into a single text file, optimized for use with Large Language Models (LLMs).

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MeMuFi

Version: 0.1.1

MeMuFi is a cross-platform GUI application for merging multiple files and directories into a single text file, optimized for use with Large Language Models (LLMs).

The application allows you to select files and folders, apply include and exclude rules, generate a directory tree, and combine file contents into one output file.

Features

  • 📁 Directory tree browsing
  • ✅ Select files and folders to include
  • ❌ Exclude files and directories using patterns
  • 🧾 Generate a textual project structure
  • 📦 Merge file contents into a single output file
  • 🚫 Automatic detection of binary files
  • 🖥️ Cross-platform support (Windows, macOS, Linux)

Tech Stack

  • Go — backend
  • Wails — desktop GUI framework and build system
  • HTML / CSS / JavaScript — frontend

Build Instructions

To build the application, you need Go and Wails installed.

Install Wails

go install github.com/wailsapp/wails/v2/cmd/wails@latest

Build the Application

wails build

Build the Linux Application

wails build -tags webkit2_41

The resulting binary will be located in:

build/bin/

Usage

  1. Launch the application
  2. Select the root project directory
  3. Choose files and folders to include
  4. Optionally define exclude rules
  5. Specify the output file path
  6. Click Generate

The generated file will contain:

  • the directory structure
  • the contents of all included text files

Output File Structure

Directory structure:
└── project/
    ├── file1.go
    └── file2.md

================================================
FILE: project/file1.go
================================================
<file contents>

Project Status

Version 0.1.1 is an initial release. The API and functionality may change in future versions.

License

MIT

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MeMuFi is a cross-platform GUI application for merging multiple files and directories into a single text file, optimized for use with Large Language Models (LLMs).

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