How do I convert a GitHub repo to a text file?

Paste the repository URL above and the file tree appears as a checkbox list. Untick what the model does not need, generate, and you get one text file: directory structure at the top, then each selected file under a header with its path. Nothing to clone, nothing to install, free and no sign-up.

How do I convert a private GitHub repository?

Paste a personal access token alongside the URL. Create a fine-grained token scoped to just that one repository with read-only Contents permission and a short expiry — that is the least privilege the API will accept for this. The token is used to call GitHub from your browser and is not stored on our side.

How many tokens is my codebase, and will it fit?

The counter updates as you tick files, so you can see the number before you paste anything. As a rough guide, source code runs around 3 to 4 characters per token, so a 500 KB selection lands near 140,000 tokens. That fits a 200K context window and will not fit into a single chat message on most interfaces.

Which files should I exclude before feeding a repo to an LLM?

node_modules, vendor, and any dependency directory first — they are usually the overwhelming majority of the bytes and contribute nothing about your code. Then lockfiles, build output, minified bundles, test fixtures, and binary assets. On a typical JavaScript project that pass alone often removes over ninety percent of the tokens.

How does this compare to gitingest, repomix, or code2prompt?

Those are strong tools and the honest difference is workflow, not capability. They are command-line first, scriptable, and the right answer inside CI or a batch job. This runs in a browser tab with a checkbox tree, so you can see the repo, deselect what you do not want, and watch the token count move — better for the one-off case where you are still deciding what to include.

Is it safe to put my code through this?

Public repositories are read through the GitHub API from your browser, so the code goes from GitHub to you. For anything confidential the stronger option is the local folder converter, which never uploads at all. Either way, scan your selection for API keys and credentials before pasting it into a model — that is the step people skip.

Repo2Txt Guide: Transform Your Codebase for AI Analysis

Repo2Txt: Transforming Codebases for AI Analysis

In the rapidly evolving landscape of AI and machine learning, the ability to analyze entire codebases has become increasingly valuable. Repo2Txt stands at the forefront of this revolution, offering a powerful tool that converts repositories into structured text formats ideal for processing with large language models like ChatGPT 4.1, DeepSeek R1, and others.

What Is Repo2Txt?

Repo2Txt is a specialized utility that transforms entire code repositories into well-structured text representations, enabling advanced AI analysis of complex codebases. Whether you're a developer seeking to understand an unfamiliar codebase, a team performing code reviews, or a researcher analyzing programming patterns, Repo2Txt bridges the gap between raw code and AI comprehension.

Seamless conversion of GitHub repositories to text
Support for local repository processing
Preservation of file structure and relationships
Integration with modern LLMs with large context windows
Privacy-focused options for sensitive codebases

Key Features

🔍 Comprehensive Analysis

Repo2Txt captures not just code, but also directory structures, dependencies, and metadata, providing a complete picture of your project.

🔒 Privacy-Focused Options

Process repositories locally without sending sensitive code to external services, ensuring data security for proprietary projects.

⚡ AI-Ready Output

Formatted specifically to maximize comprehension by large language models, enabling more accurate and useful AI responses.

🔄 Integration Capabilities

Easily integrate with AI platforms like ChatGPT, Claude, or DeepSeek for immediate codebase analysis.

Use Cases

Code Reviews

Analyze entire codebases for quality, patterns, and potential issues

Documentation Generation

Create comprehensive documentation based on codebase analysis

Onboarding Acceleration

Help new team members quickly understand complex projects

How Repo2Txt Enhances AI Code Understanding

Large language models like ChatGPT 4.1 excel at processing and understanding text but may struggle with the complex structure of code repositories. Repo2Txt bridges this gap by:

Converting nested directory structures into a readable format
Preserving important relationships between files and components
Including metadata that provides context for code interpretation
Optimizing the output for LLMs with large context windows (like GPT-4.1's 1M tokens)
Enabling holistic analysis rather than file-by-file examination

Ready to Transform Your Repository Analysis?

Unlock the full potential of AI-powered code understanding with Repo2Txt. Convert GitHub repositories or local projects into AI-ready text and gain deeper insights into your codebase than ever before.

Try Repo2Txt Online Convert Local Projects Use Crawl4AI For Websites

For custom integrations and enterprise implementations, check out Its IT Group's specialized consulting services.

Expert Tip

When analyzing large repositories, consider using the --chunk-size option to break the output into manageable pieces that fit within your LLM's context window. For most modern LLMs like ChatGPT 4.1, you can use chunks up to 800K tokens.

Start transforming your code repositories today with Repo2Txt, or contact our experts for custom implementations and integrations with your development workflow.