Resume Matcher is an AI-powered application designed to help job seekers create tailored resumes and cover letters for specific job applications. Built primarily in TypeScript with Python components, the project supports over 100 large language models including Claude, ChatGPT, DeepSeek, Kimi, GLM, and Gemma, with the flexibility to run models both locally through Ollama and remotely through cloud providers like OpenAI, Anthropic, Google Gemini, and OpenRouter.
The core workflow of Resume Matcher involves uploading a master resume in PDF or DOCX format, pasting a target job description, and receiving AI-generated improvements and tailored content suggestions. Users can then customize the generated resume by modifying suggested content, adding or removing sections, and rearranging sections through drag-and-drop functionality before exporting as a professional PDF. The application includes a cover letter generator that creates tailored cover letters based on the job description and user's resume, along with resume scoring and keyword highlighting features that analyze how well a resume matches a job description with a match score and improvement suggestions.
The project offers multiple resume templates including Classic Single Column, Modern Single Column, Classic Two Column, and Modern Two Column designs, each available as viewable PDFs. Resume Matcher supports internationalization with a multi-language user interface available in English, Spanish, Chinese, Japanese, and Portuguese (Brazilian), and can generate resume and cover letter content in users' preferred languages. The roadmap includes planned features such as an AI Canvas for crafting metric-driven resume content, an email template generator for job applications, and multi-job description optimization capabilities.
The project is built on a Next.js frontend with Python backend components and requires Python 3.13 or higher, Node.js 22 or higher, and the uv package manager for installation. Resume Matcher is free and open-source under the Apache 2.0 license, supported by corporate sponsors including Apideck, Vercel, Cubic.dev, Kilo Code, and ZanReal, as well as individual donations through GitHub Sponsors and Buy Me a Coffee.
