What is Repomix?
Repomix turns a local or remote code repository into a single file formatted for use with AI assistants and code-review workflows. It is for developers who want to give tools such as ChatGPT, Claude, or Gemini broader project context for analysis, implementation planning, or code generation. It runs as a command-line tool, offers a web interface for public GitHub repositories, and supports several output formats.
Repomix Features:
Output Formats: Export repository content as XML, Markdown, JSON, or plain text, then choose the format that suits the AI tool or workflow.
Git Awareness: Repomix follows .gitignore rules when selecting files, helping keep excluded repository content out of the packed result.
Credential Checks: Secretlint checks files for patterns matching known credential formats, and files that match are left out of the output.
Token Counts: View token counts for individual files and the repository as a whole when preparing content for an AI context limit.
File Selection: Pack a whole repository, a particular directory, or files selected with glob patterns, and specify additional files or folders to exclude.
Advanced Workflows: Use configuration files for persistent settings, run Repomix in Docker, or connect AI assistants through its Model Context Protocol server.
Repomix Benefits:
Less File Hunting: A single repository-wide file gives an AI assistant context across the project, so developers can ask questions without gathering files one by one.
More Grounded Answers: Providing related files together can help an assistant account for project structure when suggesting changes, investigating bugs, or explaining code.
Faster Preparation: Packing a chosen scope into one output reduces the manual work of collecting and arranging code before asking an AI tool for help.
Context Awareness: Per-file and repository token totals help developers judge the size of the material they plan to pass to an AI assistant.
Reduced Exposure: Ignoring excluded files and omitting files that match known credential patterns can reduce the chance of including sensitive material in shared context.
Use Cases:
AI Code Review: Prepare a project for an assistant to review code in context, identify possible issues, and consider how changes relate to other files.
Bug Investigation: Pack the relevant repository context before asking an AI assistant to trace a bug across files or explain likely causes.
Refactoring Plans: Share a codebase with an assistant to discuss an implementation plan or generate incremental changes that account for the wider project.
Documentation Drafting: Give an AI tool repository context to help draft documentation that reflects the project’s existing code and organization.
Repository Q&A: Convert a public GitHub repository in the web interface, then provide the resulting file to an AI assistant for questions or analysis.