Gitingest
Turn any Git repo into a prompt-friendly text digest for LLMs
Founded
2024
Starting Price
Free
Category
Developer Tools
Last updated March 8, 2026
About Gitingest
Gitingest is an open-source tool that transforms GitHub repositories into LLM-friendly text digests. Simply replace 'hub' with 'ingest' in any GitHub URL to get a structured, prompt-ready extract of the codebase complete with file structure, token counts, and smart formatting optimized for AI models like ChatGPT and Claude.
Pros & Cons
Pros
- Incredibly simple UX — just change 'hub' to 'ingest' in any GitHub URL for instant results
- Completely free and open-source with MIT license and zero data collection
- Multiple access methods: web app, browser extensions, CLI, Python package, and MCP server
- Smart filtering respects .gitignore and supports custom patterns to keep extracts focused
- Token count display helps manage LLM context window limits before pasting
Key Features
URL-Based Conversion
Replace 'hub' with 'ingest' in any GitHub URL to instantly generate a prompt-friendly text extract of the repository
Smart Formatting
Automatically formats code repository contents in an optimized structure suitable for direct use in LLM prompts
Token Count Statistics
Displays file and directory structures, extract size, and token counts to help manage LLM context windows
File Filtering
Respects .gitignore rules by default and supports custom include/exclude patterns to focus on relevant files
Private Repository Support
Access private repositories using GitHub Personal Access Tokens for secure code analysis
Multi-Platform Availability
Available as a web app, Chrome extension, Firefox add-on, CLI tool, and Python package for flexible integration
Self-Hostable
Open-source MIT-licensed project that can be self-hosted locally or on platforms like Umbrel for complete data privacy
Pricing
Free
Free
- Unlimited repository analysis
- Web interface at gitingest.com
- Browser extensions (Chrome & Firefox)
- CLI tool and Python package
- Private repo support via PAT
Best For
AI-Assisted Code Review
Feed entire repositories into LLMs for comprehensive code analysis, architecture review, and documentation generation
Codebase Onboarding
Quickly understand unfamiliar codebases by generating structured text summaries that can be analyzed with AI assistants
Documentation Generation
Create comprehensive project documentation by converting repo contents into LLM prompts for automated doc generation
Bug Investigation
Provide full codebase context to AI models when debugging complex issues that span multiple files
