MaintainerOS

Overview

MaintainerOS is a workspace I built for a hackathon, aimed at the work that shows up after code is merged: issues, pull requests, documentation, and releases. It is an experiment. I do not present it as a launched product.

Problem

Maintainer work is a queue of small operational tasks. Generic chat tools can draft text. They do not know which pull request should go first, or whether a repository is ready for a release.

Why I built it

I wanted the model to sit inside a workflow: look at a repository, propose a plan, and stop for review before any change is applied.

My role

I built the project independently for the hackathon, including the Next.js application, the data model, and the action pipeline.

Technical architecture

• Next.js for the application.
• Prisma for application data, backed by PostgreSQL.
• The Vercel AI SDK for the model layer, so the app is not tied to a single provider.
• GitHub remains the source of truth for the repository. The database stores workspace state and execution history.

Important engineering decisions

The model does not write to the repository directly. A workflow moves through planner, validator, executor, and reporter. The maintainer can see what would change before it runs.

Challenges and trade-offs

The hard part was trust. A maintainer needs to know what changed, why, and how. Generating a plan was easier than making that plan safe to run.

Outcome

The project was a hackathon submission and a working prototype. It is not in production, and I do not claim users or usage for it.

What I learned

The useful part of an AI product, in this case, was the workflow around the model. Text on its own did not solve the maintainer's queue.