readme ai

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Basic Information

ReadmeAI is a developer tool that automatically generates project README files by analyzing a repository and using language models. It accepts a URL or local path and produces a well-structured README with sections such as project introduction, features, project structure, installation, usage, testing, and contribution guides. The project is model agnostic and can run with cloud LLM services or local models, and it supports an offline mode for local-only operation. ReadmeAI is designed to streamline documentation maintenance across languages and frameworks, apply best practices, and enable customization through templates, header styles, badges, logos, and emojis. It extracts metadata like dependencies and directory trees from the codebase using built-in parsers and supports common hosting platforms. The tool is distributed for Python 3.9+ with multiple installation options and is licensed under MIT.

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Features
ReadmeAI provides a CLI and an optional Streamlit web interface to generate READMEs with numerous customization options. Supported LLM backends include OpenAI, Anthropic, Google Gemini, Ollama and an offline mode; optional extras enable provider-specific clients. Styling options include header and banner styles, badge color and style, logos, emoji packs, navigation layouts, tree depth and template selection. The engine generates sections such as project introduction, features table, project index, directory tree, installation and usage instructions, and contribution guides. Preprocessing extracts dependencies and system requirements from the codebase using parsers and a tree generator. It supports local and remote repositories, Docker usage, and multiple install methods including pip, pipx, uv, poetry, and building from source. Tests use pytest and nox. Prompts and templates are configurable and stored in the repository settings.
Use Cases
ReadmeAI speeds up and standardizes documentation creation for developers and maintainers by automating README generation and reducing manual writing. It produces consistent, customizable README content based on the actual repository, extracting dependencies, project structure, and usage examples to create accurate getting started and installation guides. Multi-provider model support and an offline mode let teams choose cloud or local LLMs to manage cost, privacy, and availability. Styling and template options let projects match branding and documentation standards. The generated output helps onboard contributors, supports CI workflows through planned integrations, and provides example READMEs across languages to guide real projects. Built-in parsers and prompt templates make the results reproducible and tweakable, while testing and examples demonstrate integration options for diverse projects.

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