Summary

DjangoStarter/web is an AI-native full-stack scaffold designed for independent developers and small teams, enabling the rapid transformation of ideas into deployable products with minimal boilerplate code. It provides a robust foundation for building AI-driven applications, offering built-in features and clear extension points for LLM integration, vector search, and function calls, thereby accelerating development cycles.

How It Works

This project is built upon Django 5, integrating Django-Ninja for type-safe API development, and utilizes HTMX, Alpine.js, and Tailwind CSS for a modern, interactive frontend. It includes essential features like authentication, security middleware, code generation, containerization, and observability. The architecture is explicitly designed for AI integration, providing well-defined extension points for LLM access, vector embeddings, function calling, asynchronous tasks, and streaming responses. Future versions (v4) are planned to modularize the project into distinct core, API-only, and full-stack distributions to further reduce coupling and cater to diverse user needs.

Quick Start & Requirements

• Primary Install/Run: Clone the latest production release (v3.3.0) using Git. The project utilizes uv for Python package management and pnpm for frontend dependencies. A command runner named just simplifies common development tasks.
• Prerequisites: Node.js environment, uv (Python package manager), just (command runner). Redis is recommended for caching and rate-limiting features. Python 3.14 is recommended for virtual environment creation with uv.
• Setup: Install uv and just. Create a virtual environment (uv venv), install Python dependencies (uv sync), install frontend dependencies (pnpm i), run database migrations (just mm or just migrate), and start the development server (just serve or just dev).
• Documentation: Links to official uv and just documentation are available. Specific blog posts detail v3 features and frontend integration. A docs/roadmap.md outlines future plans.

Highlighted Details

• Django Ninja Integration: Replaces traditional DRF for improved performance, automatic interactive API documentation generation via Python type hints, and enhanced code readability.
• AI-Ready Architecture: Features pre-defined extension points and best practices for integrating LLMs, vector databases, function calling, and asynchronous/streaming operations.
• Automated Code Generation: Generates RESTful APIs, schemas, and unit tests directly from model definitions, significantly boosting development efficiency.
• Modern Frontend Stack: Employs Tailwind CSS v4, DaisyUI v5, Alpine.js, and HTMX to deliver a responsive, dynamic, and SPA-like user experience with multi-theme support.
• Containerization: Includes Dockerfile and docker-compose.yml for simplified deployment.

Maintenance & Community

The master branch is under active development, with v3.3.0 being the latest stable release. The project benefits from the broader Django open-source community. Further details and updates are often shared via associated blog posts and articles.

Licensing & Compatibility

The project is licensed under the Apache License Version 2.0. This license is permissive, generally allowing for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

The master branch is designated for active development and may not be production-stable. Some security features, such as admin IP restrictions and non-debug error visibility, require manual activation. Internationalization support is currently in beta. The project's TODO list indicates several features still under development, including Celery integration, OpenAI API support, and payment gateway integration.Summary

DjangoStarter/web is an AI-native full-stack scaffold designed for independent developers and small teams, enabling the rapid transformation of ideas into deployable products with minimal boilerplate code. It provides a robust foundation for building AI-driven applications, offering built-in features and clear extension points for LLM integration, vector search, and function calls, thereby accelerating development cycles.

How It Works

This project is built upon Django 5, integrating Django-Ninja for type-safe API development, and utilizes HTMX, Alpine.js, and Tailwind CSS for a modern, interactive frontend. It includes essential features like authentication, security middleware, code generation, containerization, and observability. The architecture is explicitly designed for AI integration, providing well-defined extension points for LLM access, vector embeddings, function calling, asynchronous tasks, and streaming responses. Future versions (v4) are planned to modularize the project into distinct core, API-only, and full-stack distributions to further reduce coupling and cater to diverse user needs.

Quick Start & Requirements

• Primary Install/Run: Clone the latest production release (v3.3.0) using Git. The project utilizes uv for Python package management and pnpm for frontend dependencies. A command runner named just simplifies common development tasks.
• Prerequisites: Node.js environment, uv (Python package manager), just (command runner). Redis is recommended for caching and rate-limiting features. Python 3.14 is recommended for virtual environment creation with uv.
• Setup: Install uv and just. Create a virtual environment (uv venv), install Python dependencies (uv sync), install frontend dependencies (pnpm i), run database migrations (just mm or just migrate), and start the development server (just serve or just dev).
• Documentation: Links to official uv and just documentation are available. Specific blog posts detail v3 features and frontend integration. A docs/roadmap.md outlines future plans.

Highlighted Details

• Django Ninja Integration: Replaces traditional DRF for improved performance, automatic interactive API documentation generation via Python type hints, and enhanced code readability.
• AI-Ready Architecture: Features pre-defined extension points and best practices for integrating LLMs, vector databases, function calling, and asynchronous/streaming operations.
• Automated Code Generation: Generates RESTful APIs, schemas, and unit tests directly from model definitions, significantly boosting development efficiency.
• Modern Frontend Stack: Employs Tailwind CSS v4, DaisyUI v5, Alpine.js, and HTMX to deliver a responsive, dynamic, and SPA-like user experience with multi-theme support.
• Containerization: Includes Dockerfile and docker-compose.yml for simplified deployment.

Maintenance & Community

The master branch is under active development, with v3.3.0 being the latest stable release. The project benefits from the broader Django open-source community. Further details and updates are often shared via associated blog posts and articles.

Licensing & Compatibility

The project is licensed under the Apache License Version 2.0. This license is permissive, generally allowing for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

The master branch is designated for active development and may not be production-stable. Some security features, such as admin IP restrictions and non-debug error visibility, require manual activation. Internationalization support is currently in beta. The project's TODO list indicates several features still under development, including Celery integration, OpenAI API support, and payment gateway integration.