Turn your codebase into a single text file for LLM analysis—with just one command!

RepoToTxt makes it super easy to analyze your repositories with AI tools. Feed your entire project to your favorite LLM without the hassle of copying files one by one.

First, create a virtual environment and install the required packages:

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

For GitHub repositories, you'll need a personal access token:

On Windows (PowerShell):

$env:GITHUB_TOKEN="your-token-here"

On macOS/Linux:

export GITHUB_TOKEN="your-token-here"

RepoToTxt is flexible and works with both GitHub repositories and local projects:

# Analyze a GitHub repo
python main.py https://github.com/username/repository

# Analyze a local directory
python main.py /path/to/your/project

Your analysis will be saved in the outputs folder as {repo_name}_analysis.txt.

Working on a large project? Focus on just the parts you need:

python main.py /path/to/base/directory frontend backend utils

This creates separate analysis files for each subdirectory:

• outputs/frontend_analysis.txt
• outputs/backend_analysis.txt
• outputs/utils_analysis.txt

Got files you want to exclude? Use the --skip flag:

python main.py /path/to/directory --skip node_modules temp_files old

Large codebases can be too much for LLMs to handle. Use compression to make your analysis more digestible:

# Light compression - preserves most details
python main.py /path/to/directory --compress light

# Medium compression - balanced approach
python main.py /path/to/directory --compress medium

# Heavy compression - maximum token reduction
python main.py /path/to/directory --compress heavy

Compression works by:

• Summarizing lengthy docstrings and comments
• Reducing repetitive code sections
• Focusing on the most important files and functions
• Preserving the overall structure and core functionality

Want to specify your own output filename?

python main.py /path/to/directory --output my-special-analysis

This saves the result to outputs/my-special-analysis.txt.

If you're curious about how compression is affecting your files:

python main.py /path/to/directory --compress medium --compression-debug

Want to add custom instructions for your LLM? Open main.py and update the ADD_INSTRUCTIONS variable.

Your analysis file includes:

1. README content - Project overview and setup instructions
2. Repository structure - Directory and file organization
3. File contents - The actual code (with optional summarization for large files)

• Make sure GITHUB_TOKEN is set correctly when analyzing GitHub repositories
• Binary files and very large files are automatically skipped or summarized
• The tool respects .gitignore patterns in your repository

Contributions are welcome! Feel free to open issues or submit pull requests.