arckit
Tools for working with the Abstraction & Reasoning Corpus (ARC-AGI)
Decision gist · record as of 2026-08-14
Yes, if you are working with ARC-AGI datasets for research or competition. The package eliminates the friction of manual dataset downloads and provides well-integrated visualization and evaluation tools. The aging maintenance status (357 days since release) is acceptable given the stable codebase and active repository, but monitor for breaking changes if you pin to a specific version long-term.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel and three lightweight runtime dependencies.
- Maintenance status is aging (357 days since last release), though the repository remains active with recent commits and no archived status.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; you must retain license and copyright notices in distributions.
last release 2025-08-22 (357 days) · last repo commit 2025-08-22 · 224 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 339,667 downloads/mo, #7,419 on PyPI
Alternatives
Verify before relying
pip install arckit
import arckit
train_set, eval_set = arckit.load_data()
task = train_set[0]
task.show()- Whether the package handles all edge cases in grid visualization across different terminal environments
- Performance characteristics when working with large submission files in score_submission()
What it is and what it does
Arckit is a Python toolkit for working with the ARC-AGI family of reasoning datasets. It provides three main capabilities: loading the datasets (ARC-AGI, ARC-AGI-2, and Kaggle competition variants) without requiring separate downloads; visualizing tasks and grids as colored terminal output or high-quality vector graphics via drawsvg; and scoring model submissions in Kaggle format with configurable evaluation parameters. The package wraps numpy arrays as Task and TaskSet objects that are indexable by task ID or position, and offers both a Python API and command-line utilities (arcshow, arcsave) for inspection and export.
The package depends on numpy for array handling, rich for terminal formatting, and drawsvg for vector graphics output. It supports Python 3.8 through 3.13 and is classified as production-stable. Dataset versions can be pinned to avoid changes during research, and the underlying data follows the Apache-2.0 license from the original ARC-AGI repositories.
Use it for
- Load and iterate over ARC-AGI-2 training and evaluation tasks for building reasoning models
- Visualize specific tasks to terminal or file (PDF/SVG/PNG) for papers, documentation, or manual inspection
- Score model predictions on Kaggle ARC competitions using the official evaluation metric
- Pin dataset versions in research environments to ensure reproducibility across time
- Extract task grids as numpy arrays for custom analysis or feature engineering
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with ARC-AGI datasets for research or competition.
The package eliminates the friction of manual dataset downloads and provides well-integrated visualization and evaluation tools. The aging maintenance status (357 days since release) is acceptable given the stable codebase and active repository, but monitor for breaking changes if you pin to a specific version long-term.
Install
arckit on PyPI
Before you install
Low install friction with a pure-Python wheel and three lightweight runtime dependencies. Maintenance status is aging (357 days since last release), though the repository remains active with recent commits and no archived status.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; you must retain license and copyright notices in distributions.
Quickstart
pip install arckit
import arckit
train_set, eval_set = arckit.load_data()
task = train_set[0]
task.show()Verify before relying
- Whether the package handles all edge cases in grid visualization across different terminal environments
- Performance characteristics when working with large submission files in score_submission()
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyrichdrawsvg |
| Maintenance | Aging 357 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 339,667 / month, #7,419 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: arckit-1.0.1-py3-none-any.whl
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