DockForge — AI-Powered Dockerfile Generator

A developer built DockForge, an AI-powered tool that analyzes a GitHub repository and generates a tailored Dockerfile, then builds the image and iterates on failures. The Next.js application uses open-weight models such as Qwen3-27B through the OpenRouter API, following a generate → build → verify → fix loop in which Docker's build output feeds back to the model for corrections. The project was created for a friend whose projects ran locally but were difficult to containerize.

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend https://dev.to/challenges/hacktoberfest-weekend-2026-10-01 I built DockForge , an AI-powered Docker engineer that turns a software repository into a working Docker setup. I built it for a friend who often works on projects that run perfectly on their machine but become painful to containerize. Instead of manually figuring out the runtime, dependencies, package manager, ports, build commands, system packages, and startup configuration, DockForge analyzes the repository and generates a Docker setup tailored to the project. The goal isn't just to generate a Dockerfile that looks correct . DockForge is designed around a generate → build → verify → fix workflow. It can: In other words: Repository → Analyze → AI → Dockerfile → Build The demo shows DockForge taking an existing repository and automatically producing a Docker configuration instead of requiring the developer to manually write one from scratch. AI-Powered Dockerfile Generator for Modern Applications Analyze any GitHub repository and get an optimized, production-ready Dockerfile in seconds. 🚀 Quick Start https://github.com/Gavinduachintha/Dockforge -quick-start • 📖 Setup Guide https://github.com/Gavinduachintha/Dockforge/SETUP GUIDE.md • 🎮 Demo https://github.com/Gavinduachintha/Dockforge -demo • 🛠️ Features https://github.com/Gavinduachintha/Dockforge -features • 🤝 Contributing https://github.com/Gavinduachintha/Dockforge -contributing DockForge is an intelligent tool that analyzes your GitHub repository and automatically generates production-ready Dockerfiles tailored to your project's specific needs. No more copy-pasting generic Dockerfiles or spending hours optimizing Docker configurations | ⚡ Fast Generate Dockerfiles in seconds, not hours | 🧠 Smart AI-powered analysis and optimization | 🎯 Accurate Framework and dependency detection | ✨ Modern Best practices and security built-in | Get DockForge running in 5 minutes : 1️⃣ Clone the repository git clone https://github.com/yourusername/dockforge.git cd dockforge 2️⃣ Install dependencies npm install 3️⃣ Set up environment variables cp .env.example .env Add your OpenRouter API key to .env 4️⃣ Start the development server … I built the whole application with Next.js and used the OpenRouter API to connect it with an LLM. For the AI side, I'm using Qwen3.8-27B whick is free. DockForge takes the project information and asks the model to figure out what kind of Docker setup the project needs and generate the Dockerfile. I wanted to keep the idea simple: instead of spending time figuring out how to Dockerize a project manually, you can give it to DockForge and let the AI handle the first draft for you. OpenRouter also made it really easy to experiment with different models without having to change the whole application. The process is roughly: Repository ↓ Repository Analysis ↓ Project Detection ↓ AI Planning ↓ Dockerfile Generation ↓ Docker Build ↓ Build Errors / Verification ↓ AI Fixes ↓ Working Container The AI is responsible for reasoning about the project and generating the configuration, while Docker provides the real-world feedback. This distinction is important. A language model can generate a Dockerfile that sounds right while still being completely broken. By actually attempting to build the image, DockForge can use Docker's output as feedback and iterate on the configuration. For the AI layer, I used open-weight models through OpenRouter , allowing the underlying model to be replaced without redesigning the application around a single proprietary AI provider. This also means the architecture can evolve toward local inference and self-hosted models instead of being permanently tied to one closed AI API. Dockerization is one of those problems where there are many different valid solutions depending on the project. A Python application, a Node.js application, a Go service, and a project with native system dependencies all require different decisions. Open AI models make it possible to build tools around that reasoning without treating a single proprietary AI provider as the foundation of the product. For DockForge, open innovation matters in a few important ways. The application isn't fundamentally tied to one model. As better open-weight coding models become available, DockForge can adopt them without rebuilding the entire product around a proprietary API. The same architecture can evolve toward local inference and self-hosted models. That opens the door to using DockForge in environments where source code cannot or should not be sent to an external AI provider.