smallnest

smallnest / autoresearch

Public

autoresearch for software development

95
1
100% credibility
Found Apr 20, 2026 at 95 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

Autoresearch is a tool that automatically implements features from GitHub issues using multiple AI agents for iterative coding, reviewing, testing, and merging pull requests.

How It Works

1
📖 Discover autoresearch

You hear about a friendly tool that turns your simple task ideas into real working features while you sip tea.

2
💡 List your ideas

Jot down what you want built as clear tasks in your project's shared notebook online.

3
🚀 Start the magic

Tell the tool which task to tackle, and it jumps in to plan and build everything for you.

4
🔄 Watch it improve

Smart helpers take turns checking the work, fixing issues, and making it better until it's perfect.

5
✅ Quality check passes

Once everything tests great and scores high, it packages up the changes neatly.

🎉 Feature lives!

Your new piece of software is added to the project automatically – task done, celebrate!

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Star Growth

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AI-Generated Review

What is autoresearch?

Autoresearch is a shell-based CLI tool for fully automated software development, turning GitHub issues into merged PRs with minimal human input. Point it at any Git + GitHub repo in Go, Node.js, Python, Rust, or Java, run `./run.sh `, and it uses AI agents like Claude, Codex, and OpenCode to plan subtasks, implement code, run build/lint/test gates, score quality, and auto-merge if it passes 85+. Inspired by Andrej Karpathy's autoresearch ideas, it handles the full loop from issue to closure.

Why is it gaining traction?

Unlike single-agent tools like ralph, autoresearch rotates multiple agents for cross-review and fixes, adding UI browser verification and context overflow handoffs for reliability. Devs love the end-to-end GitHub automation—subtask splitting, resume-from-fail, and custom agent order via `-a claude,codex`—making auto research AI feel production-ready without babysitting. Its quickstart and language-agnostic gates hook experimenters chasing hands-off development.

Who should use this?

Backend devs maintaining Go or Rust repos with repetitive issues, like adding endpoints or fixes. Solo full-stack builders prototyping Node/Python apps via GitHub issues. Teams testing auto research Claude/Copilot flows before scaling to trading bots or pi projects.

Verdict

Worth a spin for GitHub-heavy workflows if you have the AI CLIs; 95 stars and solid docs show promise, but 1.0% credibility flags it as early-stage—expect tweaks for complex UIs. Pair with good tests to avoid merge regrets.

(198 words)

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