aicodinggym-cli 1.0.0

CLI tool for AI Coding Gym platform

aicodinggym-cli

CLI tool for the AI Coding Gym platform. Supports three benchmarks: SWE-bench (code bug fixes), MLE-bench (ML competitions), and Code Review challenges.

Install: pip install aicodinggym-cli Entry point: aicodinggym

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Quick Start

# 1. Configure (one-time setup)
aicodinggym configure --user-id YOUR_USER_ID

# 2. SWE-bench: fetch, solve, test, submit
aicodinggym swe fetch django__django-10097
# ... edit code to fix the issue ...
aicodinggym swe test django__django-10097    # run tests locally (requires Docker + act)
aicodinggym swe submit django__django-10097

# 3. MLE-bench: download, train, submit
aicodinggym mle download spaceship-titanic
# ... train model, generate predictions ...
aicodinggym mle submit spaceship-titanic -F predictions.csv

# 4. Code Review: fetch, review, submit
aicodinggym cr fetch keycloak-0008
# ... read diff.patch, write your review in review.md ...
aicodinggym cr submit keycloak-0008 -f review.md

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Commands

aicodinggym configure

One-time setup. Generates SSH key, registers with server.

aicodinggym configure --user-id USER_ID [--workspace-dir DIR]

Option	Required	Description
--user-id	Yes	Your AI Coding Gym user ID
--workspace-dir	No	Default workspace directory (default: cwd)
--upload-logs / --no-upload-logs	No	Pre-set consent for uploading de-identified AI session logs for research. If unset, you're asked once on your first submit.

On first run, if the Entire CLI isn't installed, configure offers to install it (used for AI workflow logging).

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aicodinggym swe — SWE-bench Commands

aicodinggym swe fetch PROBLEM_ID

Fetch a problem and clone the repo locally.

aicodinggym swe fetch PROBLEM_ID [--user-id ID] [--workspace-dir DIR]

aicodinggym swe submit PROBLEM_ID

Commit all changes and push to remote. Notifies backend.

aicodinggym swe submit PROBLEM_ID [--message MSG] [--force] [--user-id ID] [--workspace-dir DIR]

Option	Description
--message, -m	Commit message (auto-generated if omitted)
--force	Force push with --force-with-lease
--upload-logs / --no-upload-logs	Override consent for uploading de-identified AI session logs
--logs-remote	Git URL to push AI session logs to (defaults to this problem's repo)

aicodinggym swe test PROBLEM_ID

Run the SWE-bench evaluation tests locally using nektos/act. Executes the GitHub Actions workflow from the problem repo on your machine via Docker.

aicodinggym swe test PROBLEM_ID [-W WORKFLOW] [--act-args ARGS] [--user-id ID] [--workspace-dir DIR]

Option	Description
-W	Specific workflow file in .github/workflows/ (default: all)
--act-args	Extra arguments passed to act (e.g. '--container-architecture linux/amd64')

Prerequisites:

• Docker — must be installed and running (install)
• act — must be installed (install)
  • macOS: brew install act
  • Windows: choco install act-cli or winget install nektos.act
  • Linux: curl -s https://raw.githubusercontent.com/nektos/act/master/install.sh | sudo bash

Notes:

• On Apple Silicon, x86_64 emulation is auto-enabled when the workflow requires it (e.g. old Python or platform-specific conda packages). This adds overhead (~4-5 min vs ~2.5 min on native x86_64).
• Output is filtered to show step progress and test results only. Full setup logs (conda, pip) are suppressed.
• A test summary with pass/fail status and elapsed time is printed at the end.

aicodinggym swe reset PROBLEM_ID

Reset repo to original setup commit. Destructive — discards all local changes.

aicodinggym swe reset PROBLEM_ID [--user-id ID] [--workspace-dir DIR]

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aicodinggym mle — MLE-bench Commands

aicodinggym mle download COMPETITION_ID

Download dataset files as a zip archive.

aicodinggym mle download COMPETITION_ID [--user-id ID] [--workspace-dir DIR]

Option	Description
--workspace-dir	Workspace directory (default: configured workspace)

Files are saved to <workspace>/<competition_id>/data/<competition_id>.zip.

aicodinggym mle submit COMPETITION_ID -F FILE

Upload prediction CSV for scoring.

aicodinggym mle submit COMPETITION_ID -F FILE [--user-id ID] [--message MSG]

Option	Required	Description
-F	Yes	Path to prediction CSV file
--message, -m	No	Submission description
--upload-logs / --no-upload-logs	No	Override consent for uploading de-identified AI session logs
--logs-remote	No	Git URL to push AI session logs to (defaults to your submission repo)

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aicodinggym cr — Code Review Commands

aicodinggym cr fetch PROBLEM_ID

Download the PR diff and create a review.md template.

aicodinggym cr fetch PROBLEM_ID [--user-id ID] [--workspace-dir DIR]

Creates in <workspace>/<problem_id>/:

• diff.patch — the full diff between base and head branches
• review.md — template to fill in your review (only created if not already present)

aicodinggym cr submit PROBLEM_ID

Submit your code review.

aicodinggym cr submit PROBLEM_ID -f review.md [--user-id ID]
aicodinggym cr submit PROBLEM_ID -m "Inline review text"
echo "My review" | aicodinggym cr submit PROBLEM_ID

Option	Description
-f, --file	Path to a file containing your review (e.g. review.md)
-m, --message	Inline review text
stdin	Pipe review text from stdin
--upload-logs / --no-upload-logs	Override consent for uploading de-identified AI session logs
--logs-remote	Git URL to push AI session logs to (defaults to your submission repo)

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AI Workflow Logging (Entire)

AI Coding Gym can optionally capture how a solution was produced — the AI agent prompts, responses, tool calls and files touched — for research. This is powered by the Entire CLI, which hooks into git and stores sessions on a separate entire/checkpoints/v1 branch (it never adds commits to your working branch).

Consent first. Capture is local only until you opt in. The first time you submit (unless already configured), you're asked once:

AI Coding Gym can upload this AI coding session (prompts, responses, files changed) for research only. Data is de-identified/anonymized before use.

Your choice is saved in ~/.aicodinggym/config.json. Set it ahead of time with aicodinggym configure --upload-logs / --no-upload-logs, or override per submit with --upload-logs / --no-upload-logs. In non-interactive sessions with no recorded choice, nothing is uploaded.

One repo, many branches. All three benchmarks log to your single repo (the writable per-user repo — one repo, many branches), so a log is identified by its branch name even though the repo holds many problems. SWE fetch records that repo URL (submission_repo_url) so CR/MLE reuse it; the cloned CR PR repo is read-only and is never used as a target. Override with --logs-remote or the AICODINGGYM_LOGS_REMOTE env var.

Nothing is overwritten. Every submission gets its own unique branch (…/<submission-id>, a UTC timestamp + random suffix). Re-submitting the same problem — or submitting it from a different directory or machine — adds a new branch and never deletes or clobbers a previous one.

How it works

1. swe fetch / cr fetch / mle download installs Entire's hooks so your session is captured locally as you work. (MLE workspaces aren't git repos, so a lightweight one is initialised — this also lets your solution code be pushed on submit. The dataset under data/ plus common model/checkpoint/cache artifacts are gitignored.) If the entire CLI isn't installed, you're pointed at aicodinggym configure (which offers to install it) instead of being silently skipped.
2. On submit, after consent, the captured entire/checkpoints/v1 branch is pushed to your repo on a unique per-submission branch aicodinggym-logs/<benchmark>/<problem_id>/<submission-id>, with an aicodinggym-meta.json file at the tip (problem id, benchmark, user, tool, submission id, timestamp).
3. MLE also pushes your solution code (notebooks/scripts/CSV; dataset and heavy artifacts excluded) to <competition_id>/<submission-id>, gated by the same consent. The two share a submission id so code and logs correlate. The prediction CSV still goes to the scoring API as before.

Privacy. Uploaded data is used solely for research and is de-identified/anonymized before use. Entire also redacts detected secrets when writing to the checkpoints branch (best-effort). Logging is entirely optional — if the entire binary isn't installed, fetch/submit work unchanged.

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File Structure

aicodinggym-cli/
├── __init__.py      # Version
├── cli.py           # Click CLI commands (entry point)
├── config.py        # Config + credentials persistence (~/.aicodinggym/)
├── api.py           # HTTP client for aicodinggym.com/api
├── git_ops.py       # SSH key generation, git clone/commit/push/reset
├── entire_logging.py # Optional AI-session capture/upload via the Entire CLI
├── tests/           # pytest suite (logging, consent, remote resolution)
└── pyproject.toml   # Package metadata and build config

Development

pip install -e ".[dev]"   # installs pytest
pytest                    # run the test suite

Configuration Files

File	Purpose
~/.aicodinggym/config.json	Global config (user_id, repo_name, key path, workspace, log-upload consent, submission repo URL)
~/.aicodinggym/credentials.json	Per-problem credentials (repo_url, branch, cached after fetch)
~/.aicodinggym/{user_id}_id_rsa	SSH private key
~/.aicodinggym/{user_id}_id_rsa.pub	SSH public key

Backend API Summary

Endpoint	Method	Used By
/api/configure	POST	configure
/api/fetch-problem	POST	swe fetch
/api/submissions	POST	swe submit
/api/competitions/<id>/download	GET	mle download
/api/competitions/<id>/submit	POST	mle submit