armgabrielyan

armgabrielyan / autoloop

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Agent-agnostic tools for intelligent iterative optimization loops. Works with Claude Code, Codex, Cursor, OpenCode, Gemini CLI and more. Inspired by Karpathy's autoresearch.

13
1
100% credibility
Found Apr 05, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

autoloop is an agent-agnostic CLI runtime enabling iterative optimization loops for coding agents on arbitrary codebases using repo-specific evaluations and guardrails.

How It Works

1
🔍 Discover autoloop

You learn about a friendly tool that lets smart helpers improve your code project one smart change at a time.

2
📥 Install easily

Put the tool on your computer in just a few moments.

3
📁 Add to your project

Place it in your code folder and let it scan what makes your project tick.

4
✅ Smart setup check

It figures out your project's success checks and measurements, then fixes any hiccups automatically.

5
📊 Save starting point

Record your project's current performance so you can track real gains.

6
🤖 Link your AI buddy

Pick your favorite AI coding friend and give it simple instructions to team up with the tool.

7
🚀 Watch magic happen

Tell your AI to tweak the code, test changes, keep winners, toss losers—limit to a handful of tries.

🎉 Enjoy better code

Wake up to faster or smarter code, a story of what worked, and neat bundles of changes ready to review.

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Star Growth

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AI-Generated Review

What is autoloop?

Autoloop is a Rust CLI for agent-agnostic iterative optimization loops, letting AI coding agents like Claude code, Codex, Cursor, or Gemini CLI autonomously tweak real codebases overnight. Inspired by Karpathy's autoresearch, it generalizes the idea: agents propose changes, run your repo's eval commands and guardrails, keep metric wins as git commits, and discard regressions with full history. Users get bounded runs that improve benchmarks like latency without breaking correctness, plus learnings.md summaries.

Why is it gaining traction?

It stands out by auto-detecting eval setups across Rust, Python, Node, Go, JVM, and .NET repos, with a doctor command that verifies and fixes configs on the fly. Agent wrappers make it drop-in for multiple tools—no hardcoded scripts or single-model lock-in. Developers hook on the "set it and forget it" flow: init, baseline, run up to 5 experiments, then review status, learnings, and clean branches.

Who should use this?

Backend engineers tuning API latency on real workloads, ML folks iterating autoresearch-style on benchmarks, or CLI hackers optimizing smoke tests in Python/Rust repos. Ideal for teams with evals but no time for manual agent wrangling, especially if you're already using Claude, Codex, Cursor, or Gemini.

Verdict

Early days at 13 stars and 1.0% credibility—docs and examples shine, but test it on fixtures first for maturity. Worth a spin for agent experimenters; pairs well with intelligent CLI workflows.

(178 words)

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