Skip to content
Get packs

AutoResearchClaw

Runs a 23-stage pipeline that turns a research idea into a draft paper, with optional human check-ins.

GitHub Download v0.5.0
Stars
14.8k
Forks
1.7k
Licence
MIT
Language
Python
Last update
3 months ago
Type
AI model / agent

What it is

AutoResearchClaw is a Python pipeline that takes a research topic and works through a series of stages, including experiments and writing, to produce a paper. It can run fully autonomously or in a co-pilot mode where a person approves or guides key decisions. The README describes it as chatting an idea into a paper through OpenClaw, and also offers a command-line interface.

  • Start a fully autonomous run from a single topic string using the run command
  • Run in co-pilot mode to review hypotheses, baselines, and paper drafts at chosen points
  • Use the bundled skills library to add domain references such as chemistry or biology
  • Benchmark or evaluate autonomous research using the ARC-Bench topics in the repository

Great for

  • Researchers who want to explore a research idea quickly and see a draft paper
  • Developers experimenting with multi-stage LLM agent pipelines
  • Students testing how autonomous research tools handle an idea end to end
  • Teams that want human-in-the-loop control over experiment design and drafting

Not ideal for

  • Anyone who needs a paper that is ready to submit without expert review, since the output is generated and the README frames it as a tool for testing and feedback
  • Users without an LLM API or compatible agent backend, because the pipeline depends on one
  • Users who want a simple one-shot writing assistant rather than a long multi-stage run
Step-by-step guide: how to use AutoResearchClawInstall, first run, examples, tips and FAQs

How to get it

  1. 1

    Clone and build from source

    You'll need: Python 3.11+

    Install and run fully autonomous pipeline (from the repo directory)

    pip install -e . && researchclaw setup && researchclaw init && researchclaw run --topic "Your research idea here" --auto-approve

    Run in co-pilot mode

    researchclaw run --topic "Your research idea here" --mode co-pilot
  2. 2

    Run it

    • Clone the repository and install it in editable mode with pip install -e .
    • Run researchclaw setup to configure the environment
    • Run researchclaw init to initialize the project
    • Start a run with researchclaw run --topic "your idea", adding --mode co-pilot for check-ins
  3. 3

    Get the most out of it

    • Use co-pilot mode to review hypotheses and baselines at key decision points, per the HITL guide
    • Load extra domain skills with researchclaw skills install or by placing SKILL.md files in .claude/skills/
    • Use the checkpoint or step-by-step HITL modes if you want more control than full-auto
    • Read the Integration Guide to connect it with OpenClaw and messaging platforms

Downloads

Source code (.zip)main branch · straight from GitHubRelease v0.5.0Release notes and source

Strengths

  • Clear staged design: the README lists 23 stages and documents intervention modes such as gate-only and step-by-step
  • Offers human-in-the-loop modes rather than only full automation
  • Open license (MIT) and an active project with recent releases and a published arXiv paper

Trade-offs

  • Output quality and factual accuracy depend on the underlying models; the README mentions anti-hallucination checks but does not prove they are complete
  • Several features are optional or still being tested, and the README asks for tester feedback
  • Setup involves environment configuration (setup and init steps) and possibly Docker for sandboxed experiments

How it compares

ProjectStarsTypeLevelUpdatedBest for
AutoResearchClaw (this)14.8kAI model / agentintermediate3 months agoResearchers who want to explore a research idea quickly and see a draft paper
nanobot49.8kAppintermediate4 days agoPeople who want a self-hosted personal assistant they can inspect and customize
awesome-openclaw-skills53.5kCurated listbeginner7 days agoPeople who already use OpenClaw and want to find extra skills
awesome-openclaw-usecases31.6kCurated listbeginner8 months agoPeople who already have OpenClaw and want ideas for everyday uses
nanoclaw31.1kAI model / agentintermediate4 days agoDevelopers who want a small codebase they can read and change
edict17kAI model / agentadvanced1 months agoDevelopers already running OpenClaw who want a multi-agent workflow with visible task history

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.