code2prompt solves one of the most practical challenges in AI-assisted development: getting relevant codebase context into an LLM prompt without exceeding token limits or drowning the model in irrelevant files. The CLI traverses a project directory, respects gitignore rules and custom exclusion patterns, and assembles the selected files into a structured prompt with directory tree visualization, file contents, and metadata. Token counting against configurable limits helps developers stay within context window budgets for their target model.
The Handlebars template system allows customizing the output format for different use cases. Developers can create templates that emphasize architecture documentation, code review context, bug investigation, or refactoring analysis, each surfacing different aspects of the codebase. Built-in templates cover common scenarios, while the extensible template system accommodates specialized workflows. The Rust implementation ensures fast traversal of large repositories without the startup overhead of interpreted languages.
With over 7,300 GitHub stars and 300+ forks, code2prompt has become a standard tool in the AI coding workflow alongside tools like Repomix. The MIT license enables unrestricted use, and the single-binary distribution means no runtime dependencies to manage. The tool integrates naturally into command pipelines, accepting input from standard repository structures and outputting formatted text that can be piped directly to clipboard managers or LLM API calls.
