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repomix

Package entire code repositories into single, AI-optimized files. Ideal for providing codebase context to LLMs like Claude, ChatGPT, and Gemini for analysis, security audits, and bug investigations.

Introduction

Repomix is a powerful CLI utility designed to transform complex, multi-file code repositories into a consolidated, AI-friendly format. By packaging entire codebases into a single file—such as XML, Markdown, or JSON—it enables developers to feed large-scale projects into Large Language Models (LLMs) like Claude, GPT-4, and Gemini without losing context. This tool is essential for engineers performing deep codebase analysis, security audits, architecture reviews, or debugging sessions where a holistic view of the project structure is required.

  • Multi-format output support: Generate context in XML, Markdown, JSON, or plain text to suit different LLM input preferences.

  • Git-aware processing: Automatically honors .gitignore rules to ensure build artifacts and sensitive configuration files are excluded by default.

  • Intelligent filtering: Offers granular control via include and exclude patterns, allowing users to focus on specific modules, directories, or file types.

  • Token management and visualization: Built-in token counting and a 'token-count-tree' feature help users identify high-cost files and optimize content before ingestion.

  • Security-first design: Integrates with Secretlint to detect potential credentials, API keys, or sensitive data before the package is finalized.

  • Comment stripping: Capability to remove source code comments to reduce token consumption and improve the signal-to-noise ratio for AI processing.

  • Remote repository support: Process GitHub repositories directly via URL without the need for a local clone, perfect for analyzing third-party libraries or unfamiliar projects.

  • Use Repomix when preparing feature branches for AI-assisted code reviews or documentation generation.

  • Always review the generated output file to verify that no unintended sensitive files or environment variables (e.g., .env) have been included.

  • Utilize the --remove-comments flag for large codebases to maximize the amount of logic captured within target LLM context windows.

  • Monitor the token count tree output to strategize compression for projects that exceed current model limits.

  • The tool is designed for seamless CLI integration and is compatible with CI/CD pipelines, making it a staple for automated security audit workflows and technical documentation maintenance.

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