Turn ‘Reading Papers’ Into ‘Running Projects’ — OpenResearch

Shared pain: read a paper, think ‘I want to try this experiment’ — and then… nothing. Because reproducing means environments, code, tuning — a huge manual gap between ‘reading’ and ‘doing.’ OpenResearch fills it: hand it the paper, it runs the experiment.

An AI Research Assistant + Lab

OpenResearch is a local-first workflow where AI agents ‘do research’ — each direction gets an isolated session (git worktree), AI edits code, runs experiments, logs results; every run is an immutable commit. Reproducible, traceable — academic-grade rigor.

Why the Author Built It

The alphaXiv team (paper knowledge-base builders). OpenResearch extends ‘open research’: not summaries — actually doing the research. ‘Research in public’ — open, replicable, checkable.

Jupyter+ChatGPT or This?

Formal research → OpenResearch’s commit records are the only guarantee. ‘AI doesn’t just chat — it experiments’ — currently the most complete open option.

Trust Check

Academic-born team (alphaXiv has a good name). Active issues, open design docs — a learnable open system, not a black box.

Where It’s Headed

Stronger autonomous agents, more models, better paper workflows — research for everyone, not just university labs.

Install

git clone https://github.com/alphaXiv/OpenResearch.git
cd OpenResearch && uv sync
cp .env.example .env   # fill in model API key
uv run main.py --task "your research question"

Gotchas: first sync downloads a lot; quality depends on model strength; parallel sessions eat disk.

FAQ

• Q: Need coding skills? A: No — AI codes; reading diffs helps you supervise.
• Q: Data safe? A: Local-first — only your model API sees prompts.
• Q: Relation to alphaXiv? A: Sibling projects — alphaXiv reads papers, OpenResearch runs experiments.

Researchers who want AI to run experiments? Comment which paper you’d reproduce first.