Autonomous Forge is an open-source Python tool built by AI agents as a three-day experiment in safe repository maintenance, resulting in a pre-alpha human-in-the-loop safety framework with many features but a failing main branch.

Stars

251

7-day growth

No data

Forks

10

Open issues

3

License

Apache-2.0

Last updated

2026-07-12

AI repository intelligence
FR-AI / ANALYSIS

Why it is worth attention

This experiment showcases the ability of AI agents to autonomously grow a repository from two files into a structured project with extensive tests, safety boundaries, and engineering memory, while also revealing critical weaknesses in maintaining test stability and production readiness.

Who it is for

  • AI and software engineering researchers studying autonomous development
  • Software engineers interested in human-in-the-loop maintenance automation
  • Maintainers of AI-assisted workflows and CI pipelines
  • Developers evaluating safety frameworks for repository automation

Use cases

  • As a case study for AI software stewardship and self-referential project building
  • Reference implementation for human-in-the-loop maintenance gates and safety controls
  • Starting point for building safer repository automation tools
  • Testbed for experimenting with evidence bundling, replay, and preservation designs

Strengths

  • Safety-aware design with path and symlink containment, explicit confirmation gates, and no telemetry or AI API code
  • Extensive test coverage with 614 named functions covering path escapes, hash drift, missing files, and other safety cases
  • Clear separation of read-only review commands from side-effect commands (writes, commits, pushes)
  • Comprehensive evidence and preservation layer with hash-linked run history, bundles, archives, and verification

Considerations

  • Not production-ready: final main branch has 82 failing tests and GitHub Actions CI fails on Python 3.10–3.12
  • No lint, type-check, coverage, or release workflows; no tags or releases; main branch not protected
  • Safety is not proven end-to-end: evidence provenance is not fully trusted, and some commands can cause real side effects after confirmation

README quick start

Autonomous Forge

The three-day Autonomus AI experiment

Autonomous Forge is an open-source Python tool built and maintained by scheduled AI agents. The agents were given a GitHub repository and permission to decide what to build, how to structure it, and how to improve it.

This README documents what happened during the experiment.

Short result

The experiment was successful as a research test, but the project is not production-ready.

The AI grew a small repository into a large safety and maintenance framework. The code installs, compiles, and many individual commands work. However, the final main branch has failing tests. The latest GitHub Actions run failed on Python 3.10, 3.11, and 3.12.

The best description is:

A useful pre-alpha, human-in-the-loop safety framework — not a self-running AI engineer.

What the project does

Autonomous Forge is a local-first command-line tool. It helps a maintainer or AI-assisted workflow move through a controlled maintenance process:

flowchart LR
    A[Repository files] --> B[Plan and select a task]
    B --> C[Create a reviewable proposal]
    C --> D[Review paths, diffs, status, and risks]
    D --> E[Preview validation]
    E --> F[Optional confirmed change]
    F --> G[Commit and push checks]
    G --> H[Evidence bundle and replay]
    H --> I[Archive and preservation checks]
    D --> J[Human review]
    J --> F

The repository does not call an AI model. The external scheduler and AI agents supplied the autonomy. Forge supplies local planning, safety checks, side-effect gates, and evidence records.

What happened

The repository started with only a README and a license. By the final commit, it contained:

  • Total lines added across all Git history: 49,200
  • Total lines deleted across history: 11,616
  • Actual lines currently present: 37,584 lines
  • 283 tracked files.
  • 112 Python source files.
  • 90 Python test files.
  • 68 documentation files.
  • 6 .ai planning and memory files.
  • 1,486 commits by the same Git author.
  • 123 numbered AUTO-### task groups, reaching AUTO-140.
  • No runtime dependencies.

Commit activity

Local dateCommits
7 July 2026194
8 July 2026658
9 July 2026572
10 July 202661
xychart-beta
    title "Commits by local date"
    x-axis ["Jul 7", "Jul 8", "Jul 9", "Jul 10"]
    y-axis "commits" 0 --> 700

Description

This repo is automated and is fully AI-built and AI-maintained project that continuously plans, codes, tests, and improves itself.

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