symbolica-ai

symbolica-ai / arcgentica

Public

An ARC-AGI solution using Agentica from Symbolica

www.symbolica.aiblogarcgentica
157
14
100% credibility
Found Feb 13, 2026 at 112 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

ARCgentica is an open-source AI agent system that uses large language models to solve ARC-AGI abstract reasoning puzzles by analyzing grid examples and generating Python transformation programs.

How It Works

1
🔍 Discover ARCgentica

You stumble upon this clever AI tool that tackles brain-teasing grid puzzles and scores super high on tough challenges.

2
🛠️ Get your computer ready

You grab the simple setup files and prepare your machine so everything runs smoothly.

3
🔗 Link your AI thinker

You connect a smart AI service like Claude or GPT so the tool can borrow their reasoning power.

4
🚀 Fire up the helper

You start a quiet background helper that keeps everything organized and speedy.

5
🎯 Kick off puzzle solving

With one easy command, you unleash the AI agents to analyze examples and crack the puzzles automatically.

6
📊 Check your amazing results

You peek at the detailed logs, scores, and summaries to see how well it performed on each challenge.

🏆 Celebrate top scores!

You beam with pride at hitting record-high accuracy like 85% on public evals, ready to submit or share your wins.

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Star Growth

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AI-Generated Review

What is arcgentica?

ARCgentica is a Python toolkit for tackling ARC-AGI benchmarks like ARC-AGI-1 solutions on GitHub, ARC-AGI-2 GitHub evals, and daily ARC-AGI puzzles. It deploys LLM agents via the Agentica framework to analyze grid examples, generate Python transform functions, and evaluate them on hidden tests, reproducing 85% scores on ARC-AGI-2 public leaderboard. Run it via CLI with models like Claude Opus—clone, spin up a local server, and output scored runs or Kaggle submissions for ARC-AGI projects.

Why is it gaining traction?

It crushes ARC-AGI solver GitHub baselines with agentic workflows: sub-agents parallelize hypothesis testing, code gen, and validation, beating simple chain-of-thought by wide margins at $7/task. Reproducible high scores (85% on ARC-AGI-2) draw devs chasing ARC-AGI Python solutions, plus seamless integration with OpenAI, Anthropic, or OpenRouter APIs and detailed logs for debugging. No fluff setup with uv and async concurrency for 60+ problems.

Who should use this?

AI researchers benchmarking LLMs on ARC-AGI-1/2/3 GitHub datasets or ARC Prize competitors needing agentic ARC-AGI solutions GitHub baselines. Python devs prototyping Poetiq-style ARC-AGI puzzle solvers or automating ARC-AGI daily puzzle solutions. Teams evaluating frontier models on abstract reasoning without building agents from scratch.

Verdict

Grab it if you're deep in ARC-AGI—impressive agentic performance outweighs the 1.0% credibility score and 77 stars signaling early maturity. Docs shine for quick starts, but expect tweaks for production; strong foundation for custom ARC-AGI GitHub forks.

(198 words)

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