Autonomous-Forge
This repo is automated and is fully AI-built and AI-maintained project that continuously plans, codes, tests, and improves itself.
A Python command line tool and honest research report on a three day experiment where AI agents were given free rein to build and maintain a repository.
Autonomous Forge is a Python command line tool that documents a three day experiment in which scheduled AI agents were given a GitHub repository and allowed to decide what to build and how to organize it. The README itself is written as a research report on what happened, and it is honest that the result is a useful pre-alpha safety and maintenance tool meant to keep a human in the loop, not a fully self running AI engineer. The main branch has failing tests, and the most recent automated test run failed across all three tested Python versions.
The tool itself does not call any AI model. Instead, it supplies a controlled local process that a maintainer or an AI assisted workflow can move through: reading repository files, planning and picking a task, creating a proposal that can be reviewed, checking that proposal against rules about which files can be touched, previewing what a change would do without running it, and only then allowing an explicitly confirmed change, commit, and push. After a change is made, it can record evidence of what happened and archive that evidence for later review.
Over the course of the experiment the AI agents grew the project from just a README and a license file into over 37,000 lines of code across 283 tracked files, with around 1,486 commits split into more than 120 numbered task groups. Roughly 650 automated tests exist covering safety cases like blocked file paths and missing confirmations, though at the point this README was written 82 of those tests were failing locally and CI was failing on all three supported Python versions.
The README describes two AI agent roles used during the experiment, one that added features and one focused on fixing tests and maintenance, working from a small set of markdown files that served as project memory. It notes plainly that the agents kept adding new features even while the test suite was broken, which it flags as the most important weakness of the experiment. No license terms are described in the portion of the README reviewed here.
Where it fits
- Read the README as a case study on what happens when AI agents are given autonomy over a repository for several days.
- Study the tool's controlled review and confirmation steps as a pattern for safer AI assisted code changes.
- Look at the included commit and CI history data to understand failure patterns in AI generated code.
- Use the local planning and review commands as a starting point for building your own AI assisted maintenance workflow.