Auto Research Claw: Free AI Research Paper Tool

Auto Research Claw turns one chat message into a full research paper — fully automated, free, and runs through OpenClaw.

If you do market research, competitor analysis, or any deep-dive work, Auto Research Claw is the new tool to know.

This post covers:

What Auto Research Claw is.
How the 23-stage research pipeline works.
How to install in OpenClaw.
Real use cases.

The Quick Pitch

Auto Research Claw is a free open-source tool.

You give it one sentence:

"Research AI agency marketing."

You walk away.

It comes back with a full paper.

Real sources.

Charts.

Experiments.

Fully formatted.

Already 1,500+ stars on GitHub.

Why This Matters

Most "research" tools are weak.

They:

Hallucinate sources.
Miss key data.
Need constant prompting.
Produce generic output.

Auto Research Claw fixes those by running a 23-stage research pipeline through OpenClaw.

Real sources.

Verified citations.

Multiple AI agents debating findings.

The output rivals what a human researcher would produce.

How To Install

Genuinely simple.

Inside OpenClaw, just say:

"Can you install this?"

Then paste the GitHub link.

Within ~2 minutes, OpenClaw installs Auto Research Claw.

You can start using it immediately.

For broader OpenClaw setup, see Build Your Own OpenClaw and OpenClaw Computer Use.

How To Use It

Once installed, just say in OpenClaw:

"Research [topic]."

Examples:

"Research Nemo Claw."
"Research the latest trends in AI SEO."
"Research how AI is changing small business marketing."

Auto Research Claw takes over.

Runs the 23-stage pipeline.

Comes back with the paper.

The 23-Stage Research Pipeline

Broken into 8 phases.

Highlights:

Stage 1 — Topic ingestion

Auto Research Claw reads your topic.

Plans the research approach.

Stage 2 — Source hunting

Searches academic archives for real papers.

Finds primary sources, not just blog posts.

Stage 3 — Quality screening

Each paper screened for relevance and quality.

Only the good ones make it through.

Stage 4 — Hypothesis generation

Multi-agent debate on the best hypothesis.

Stage 5 — Experiment design + run

Designs experiments based on the topic.

Writes Python code, runs it in a sandbox, collects data.

Stages 6-22 — Multi-agent analysis

Multiple agents argue:

Best analysis approach.
Whether findings make sense.
What's missing.
What should be challenged.

This multi-agent debate makes output way smarter than single-agent.

Stage 23 — Final paper assembly

5,000-6,500 words.

Formatted.

Every source verified.

Folder of deliverables ready.

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Key Features

What makes Auto Research Claw different.

4-layer citation integrity

Checks if content is hallucinated or not.

Verifies sources.

This is the biggest issue with most AI research tools.

Auto Research Claw solves it.

Proceed or pivot logic

Mid-research, the system can decide:

Continue with the current angle.
Pivot to a different angle if findings warrant it.

This mimics how a real researcher thinks.

Multi-agent debate

For:

Hypothesis generation.
Result analysis.
Peer review.

Agents argue with each other.

Output is sharper because of it.

Self-improvement loop

Extracts lessons from each run.

30-day time decay (older lessons fade out).

System gets smarter with use.

Real Use Cases

Six things to build with Auto Research Claw.

1 — Lead magnets / white papers

Generate professional white papers as lead magnets.

Used to take weeks of work.

Now takes hours.

2 — Content authority

Position yourself as the expert in your niche.

Publish well-researched, deep content.

Compete with bigger sites on quality.

3 — Internal strategy reports

Research markets, competitors, trends.

Inform your business decisions with real data.

4 — Client deliverables

Use Auto Research Claw to produce client-facing research reports.

Charge premium prices for what used to be expensive consulting work.

5 — Product validation

Research the market for a new product idea.

Get real data before investing.

6 — Scheduled monthly research

Pair with OpenClaw scheduling.

"Each month, generate a research report on AI agent market."

Automated competitive intelligence.

What You'll Need

To use Auto Research Claw:

OpenClaw installed — see Build Your Own OpenClaw.
OpenAI API key (or compatible).
Compute — experiments need more than chat.
Time — first run is slow, then speeds up.

Cost Reality

The tool itself is free (MIT license).

What you pay for:

LLM API costs (depends on model).
Compute for experiments.

Total cost per research paper: ~£1-10 depending on depth.

Compared to hiring a researcher (£500-£5,000), this is dramatic savings.

Quality Reality Check

Be honest.

Auto Research Claw output is solid but:

Always verify the final paper.
Check sources manually for high-stakes work.
Don't publish without human review.

The 4-layer citation integrity helps a lot.

But humans should still QC for production work.

How It Compares To Other Research Tools

Auto Research Claw:

Free, open source.
Runs on OpenClaw.
23-stage pipeline.
Multi-agent debate.
Self-improvement.

Perplexity Pro:

Paid.
Faster lookups.
Less depth.
Single-agent.

Manual research:

Highest quality.
Days of work.
Expensive.

For depth + cost trade-off, Auto Research Claw is unique.

Pause-For-Review Stages

Auto Research Claw lets you pause at three key stages for human review:

Hypothesis stage.
Mid-research stage.
Pre-finalisation stage.

Or set auto-approve and let it run end-to-end.

For high-stakes work, use human-in-the-loop.

For exploration, auto-approve is fine.

Multi-Language Support

Auto Research Claw works in multiple languages.

Useful for:

International market research.
Multi-language client work.
Comparing how a topic is discussed in different regions.

Daily Reality

What it looks like running Auto Research Claw:

Drop a topic in OpenClaw.
Let it run for ~1 hour.
Come back to a 5,000-word research paper.
Review.
Use.

Used to take weeks.

Now takes a meeting.

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FAQ — Auto Research Claw

Is Auto Research Claw really free?

Yes — MIT licensed, open source.

How long does a research run take?

About an hour for a full 23-stage pipeline.

Can I run it without OpenClaw?

Designed for OpenClaw — runs best there.

Will the output be accurate?

The 4-layer citation integrity helps.

But always review high-stakes work manually.

Can I schedule research?

Yes — pair with OpenClaw scheduling.

What models does it work with?

OpenAI API by default; other LLMs work via OpenClaw.

Will it replace research consultants?

For routine market research, partially.

For specialised expertise, no.