Guide · Open source
How to use AutoResearchClaw: install, run and co-pilot mode
Updated · written with AI from the project's own documentation and checked against our catalogue data
AutoResearchClaw is an open-source Python pipeline that takes a research topic and works through a series of stages, including experiments and writing, to produce a draft paper. This guide shows how to install it from the repository, start a run in fully autonomous or co-pilot mode, and judge whether the output is useful for your work. It is aimed at researchers, students and developers who want to see how a multi-stage research agent handles an idea end to end.
AutoResearchClawaiming-lab/AutoResearchClaw · ★ 14.8k · MIT Install options & downloads Quick start
- 1Check Python. Confirm you have Python 3.11 or newer and access to an LLM API or compatible agent backend.
- 2Clone the repository. Download the AutoResearchClaw source from its GitHub repository and open the repository directory.
- 3Install and set up. Run pip install -e . followed by researchclaw setup and researchclaw init from the repo directory.
- 4Start a run. Start a pipeline with researchclaw run and your topic in quotes, adding --auto-approve for a fully autonomous run.
- 5Try co-pilot mode. Use --mode co-pilot to approve or guide key decisions during the run.
- 6Review the draft. Check the generated paper critically, verifying claims and results before using any of it.
What AutoResearchClaw does
You give AutoResearchClaw a research topic. The pipeline then moves through a sequence of stages, including experiments and writing, and produces a paper. The README describes the workflow as chatting an idea into a paper through OpenClaw, and it also provides a command-line interface.
It can run fully autonomously, or in a co-pilot mode where a person approves or guides key decisions.
- Input: a research topic passed with --topic
- Output: a generated paper draft
- Modes: fully autonomous or co-pilot
Check the requirements before you install
The install method is clone-and-build, and the prerequisite listed is Python 3.11 or newer. The pipeline depends on an LLM API or a compatible agent backend, so have one available before you start.
- Python 3.11+
- A clone of the AutoResearchClaw repository
- An LLM API or compatible agent backend
Install and set up AutoResearchClaw
Run these commands from the repository directory. The first command installs the package in editable mode, then the setup and init commands prepare the CLI.
pip install -e . && researchclaw setup && researchclaw initRun a fully autonomous pipeline
To run the whole pipeline without stopping for approval, pass your topic with the --auto-approve flag. Replace the placeholder text with your own research idea.
- Use a specific question or idea as the topic string
- Expect a long multi-stage run rather than a quick one-shot result
researchclaw run --topic "Your research idea here" --auto-approveRun in co-pilot mode
Co-pilot mode lets you collaborate with the AI at key decision points. This suits teams that want human control over experiment design and drafting. The project also links a Co-Pilot Guide in its README for more detail on this mode.
- Use co-pilot when you want to approve or steer decisions
- Use the autonomous command when you want the full run without intervention
researchclaw run --topic "Your research idea here" --mode co-pilotKnow the limits before you rely on the output
The README frames the output as generated and as a tool for testing and feedback. It is not described as ready to submit without expert review. Treat the draft as a starting point for your own checking, not a finished manuscript.
- Verify every claim and result yourself
- Get expert review before any submission
- Use it to explore an idea quickly and see a draft structure
What people use it for
- Exploring a research idea quickly and reading a generated draft
- Testing how a multi-stage LLM agent pipeline handles an idea from start to finish
- Students examining how autonomous research tools work end to end
- Teams that want human approval over experiment design and drafting in co-pilot mode
Frequently asked questions
Is AutoResearchClaw free to use?
The project is released under the MIT licence, as stated in the repository. Running it still depends on an LLM API or compatible agent backend, which the source does not price, so check that provider's terms.
What do I need to install AutoResearchClaw?
The listed prerequisite is Python 3.11 or newer. You clone the repository and install it from the repo directory with pip install -e . The pipeline also needs an LLM API or compatible agent backend.
How do I run AutoResearchClaw without approving each step?
Use researchclaw run with --topic and --auto-approve. This runs the pipeline fully autonomously, according to the README.
What is co-pilot mode?
Co-pilot mode is a run where a person approves or guides key decisions. You start it with researchclaw run --topic and --mode co-pilot.
Can I use AutoResearchClaw to produce a paper ready to submit?
The README frames the output as generated and as a tool for testing and feedback. It does not claim the paper is ready to submit without expert review, so plan for that review.
How does AutoResearchClaw compare to GPT Researcher or AI Scientist?
The project lists GPT Researcher and AI Scientist as comparable projects. The source does not include a direct feature comparison, so read each project's documentation to decide which fits your needs.
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Our verdict
AutoResearchClaw suits researchers, students and developers who want to explore a research idea quickly, see a draft, or study a multi-stage LLM agent pipeline. It is a poor fit if you need a paper ready to submit without expert review, have no LLM API or compatible agent backend, or want a simple one-shot writing assistant.
Install options & downloads