Enferno is a Flask-based web framework designed for rapid AI-assisted development, abstracting away complex frontend build configurations. It targets developers seeking to quickly build sophisticated applications, offering a streamlined experience with integrated AI tools and production-ready features out-of-the-box. The primary benefit is accelerated development cycles through reduced setup and configuration overhead.

How It Works

Enferno eliminates traditional frontend build steps (Webpack, Vite, npm) by serving Vue 3 and Vuetify 3 components directly to the browser. Its AI-native approach integrates Claude Code and Cursor rules, allowing AI models to understand and interact with the codebase. The framework defaults to SQLite for easy deployment but supports optional scaling with PostgreSQL, Redis, and Celery for background tasks, making complexity opt-in.

Quick Start & Requirements

• Primary install/run: Clone the repository, run ./setup.sh (installs dependencies, generates .env), uv run flask create-db, uv run flask install (create admin user), and uv run flask run to start the development server at http://localhost:5000.
• Prerequisites: Python 3.11+ is required.
• Docker: A full production stack (including Redis, PostgreSQL, Nginx, Celery) can be launched with docker compose up --build.
• Documentation: Available at docs.enferno.io.

Highlighted Details

• Frontend: Vue 3, Vuetify 3, and Axios are included, requiring no separate build process.
• Authentication: Production-ready features include 2FA, WebAuthn, and OAuth (Google/GitHub).
• AI Integration: Ships with Claude Code and Cursor rules for AI-assisted development.
• Database: SQLAlchemy ORM with migrations, defaulting to SQLite but supporting PostgreSQL.
• Optional Add-ons: ReadyKit provides Stripe payments, multi-tenancy, and team management. Background tasks are enabled via Redis and Celery.

Licensing & Compatibility

• License: MIT.
• Compatibility: Designed for broad deployment ("Deploy anywhere" with SQLite), with Docker support for production environments. The MIT license permits commercial use and integration into closed-source projects.

Limitations & Caveats

While designed for simplicity, advanced features like background task processing (Celery/Redis) and PostgreSQL require explicit configuration and setup beyond the default SQLite setup. The AI integration relies on external services (Claude Code, Cursor rules), necessitating appropriate API keys or access.