GeniePy

GeniePy is a modern SaaS starter codebase designed specifically for building SaaS products using Python. It aims to significantly reduce development time by providing a comprehensive suite of features right out of the box. With GeniePy, users can expect a fully functional setup including user authentication, payments, an SEO-optimized blog, email integration, and database support for PostgreSQL, MySQL, and SQLite. Additionally, it offers integration with Stripe for subscriptions, social login capabilities, and even allows for the use of the React.js ecosystem without writing JavaScript. Tailwind CSS is used for front-end development, ensuring that the user interfaces are not only functional but also aesthetically pleasing. Moreover, GeniePy includes features for sending emails through various providers, integrating Large Language Models, and an admin interface for application data management. It also emphasizes ease of deployment with ready-to-use configuration files and documentation, alongside continuous testing support with pytest. The entire codebase is Python-based, catering to developers who prefer Python for both backend and frontend development. With lifetime support and free updates, GeniePy positions itself as a valuable tool for developers looking to launch their SaaS projects quickly and efficiently.

Features

• User Management: Secure user management with features like registration, login, and social login.
• Stripe Subscriptions: Easy integration of Stripe for managing recurring payments.
• Database Integration: Supports PostgreSQL, MySQL, and SQLite.
• Frontend Development: Utilizes Tailwind CSS and allows for React.js use without JavaScript.
• Email Integration: Supports sending emails through Mailjet, Mailgun, and Sendgrid.
• SEO-Optimized Blog: Built-in blog engine for content marketing.
• Admin Interface: Manage application data easily with an integrated admin interface.
• Continuous Testing: Supports pytest for continuous code verification.
• LLM Integration: Allows for the integration of Large Language Models into applications.