aiming-lab

aiming-lab / AutoResearchClaw

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Fully autonomous research from idea to paper. Chat an Idea. Get a Paper. Fully Autonomous. 🦞

autonomous-research citation-verification llm-agents multi-agent-debate openclaw
37
2
100% credibility
Found Mar 15, 2026 at 37 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AutoResearchClaw is an open-source tool that automates generating complete academic papers from a research topic, handling literature review, sandboxed experiments, peer review simulation, and LaTeX export.

How It Works

1
📖 Discover AutoResearchClaw

You find this helpful tool on GitHub that promises to turn your research idea into a full academic paper just by chatting.

2
💻 Set it up in minutes

Follow simple steps to install it on your computer and connect a smart AI helper so it can think and create.

3
💡 Share your research idea

Simply tell it your topic, like 'new ways to train AI faster', and it starts working right away.

4
🔬 Watch magic happen

It gathers real studies, runs safe tests on your computer, analyzes results, and writes a complete paper with charts and references.

5
✅ Review and tweak

Check the draft, approve important parts like the plan, and let it polish everything to perfection.

📄 Get your ready-to-submit paper

Enjoy your full academic paper in perfect format for top conferences, complete with real citations and experiment proofs.

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AI-Generated Review

What is AutoResearchClaw?

AutoResearchClaw turns a research idea into a full academic paper via a single Python CLI command: `researchclaw run --topic "your idea" --auto-approve`. It pulls real papers from arXiv and Semantic Scholar, runs hardware-aware sandbox experiments on your local GPU/CPU, performs multi-agent analysis with self-healing loops, and outputs conference-ready LaTeX (NeurIPS/ICML/ICLR templates), BibTeX refs, charts, and peer reviews. No manual lit reviews, coding, or drafting—it's pitched as fully autonomous research, dodging debates on whether fully autonomous AI agents should be developed.

Why is it gaining traction?

It stands out by delivering end-to-end autonomy: real citations verified in 4 layers, pivot/refine decisions without human input, and artifacts like `paper.tex` ready for Overleaf, unlike fragmented tools for lit search or code gen alone. The OpenClaw integration lets AI assistants like Claude trigger full runs via chat, and evolution tracking learns from failures across runs. Developers dig the no-babysitting hook amid hype around fully autonomous systems like Tesla driving or qubit tuning.

Who should use this?

ML researchers prototyping hypotheses before big experiments, academics rushing conference deadlines, or PhD students exploring "what if" ideas in optimization or NLP. Ideal for remote GitHub fully remote jobs where quick paper drafts impress collaborators, but skip if you need production-grade reliability over sandbox toys.

Verdict

Promising for sparking ideas into drafts, but at 37 stars and 1.0% credibility, it's raw—1039 tests pass, docs are multilingual, yet expect bugs in complex runs. Try on toy topics first; don't bet your NeurIPS submission on it yet.

(198 words)

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