Key Takeaways
- AutoResearchClaw turns a one-line idea into a conference-level research paper autonomously
- 23-stage pipeline mirrors a human researcher's workflow: idea → literature → hypothesis → experiment → paper
- Multi-agent debate: one agent proposes, another challenges, a third mediates — reducing hallucination
- Citation verification: every reference is checked against real databases before inclusion
- 9,400+ stars, 8 showcase papers across math, biology, NLP, RL, and computer vision
- Compatible with OpenClaw and MetaClaw backends
What Is AutoResearchClaw?
AutoResearchClaw is the most ambitious paper-writing agent in the OpenClaw ecosystem. You give it a one-line idea. It gives you a conference-level paper — with literature review, experiments, analysis, and LaTeX formatting. No human in the loop.
Core attributes:
- Category: Autonomous research paper generation
- GitHub stars: 9,400+
- Showcase: 8 papers across math, biology, NLP, RL, computer vision
- Compatibility: OpenClaw, MetaClaw
- Pipeline: 23 stages from idea to PDF
How It Works: 23-Stage Pipeline
AutoResearchClaw runs a 23-stage pipeline that mirrors how a human researcher works:
1. Idea parsing → 2. Literature discovery → 3. Gap identification
→ 4. Hypothesis formulation → 5. Multi-agent debate
→ 6. Experiment design → 7. Code generation → 8. Self-debugging
→ 9. Experiment execution → 10. Result analysis → 11. Statistical testing
→ 12. Figure generation → 13. LaTeX writing → 14. Citation verification
→ 15. Anti-hallucination check → 16. Peer review simulation
→ 17. Revision → 18. Hardware-adaptive execution (GPU/CPU)
→ 19. Abstract generation → 20. Title optimization
→ 21. Final compilation → 22. PDF output → 23. NotificationKey innovations:
- Multi-agent debate — One agent proposes, another challenges, a third mediates
- Citation verification — Every reference checked against real databases
- Self-healing error correction — If code fails, it diagnoses and fixes automatically
- Hardware adaptation — Detects GPU/CPU and adjusts execution accordingly
Quick Start
Installation
git clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
pip install -r requirements.txtConfiguration
Create a .env file:
OPENAI_API_KEY=sk-xxx
ANTHROPIC_API_KEY=sk-ant-xxx
SEMANTIC_SCHOLAR_API_KEY=xxx # optional, for citation verificationRun Your First Paper
python run.py --topic "sparse attention in vision transformers"The agent will work through all 23 stages, outputting a complete LaTeX paper.
Anti-Hallucination System
What makes AutoResearchClaw different from "just asking GPT to write a paper":
| Feature | Generic LLM | AutoResearchClaw |
|---|---|---|
| Citation verification | ✗ | ✓ (checked against Semantic Scholar) |
| Multi-agent debate | ✗ | ✓ (propose → challenge → mediate) |
| Code execution | ✗ | ✓ (runs experiments, generates real figures) |
| Statistical testing | ✗ | ✓ (p-values, confidence intervals) |
| Self-debugging | ✗ | ✓ (automatic error diagnosis and fix) |
Who Should Use AutoResearchClaw?
Choose AutoResearchClaw if you:
- Want to explore multiple research directions quickly by generating initial paper drafts
- Need help with the mechanical parts of paper writing (literature review, LaTeX formatting)
- Are a PhD student looking to accelerate early-stage idea exploration
- Want a baseline paper to build upon and refine
Don't use it if you:
- Expect publication-ready output without human review
- Need domain-specific experimental protocols (wet lab, clinical trials)
- Want to submit AI-generated papers without disclosure
FAQ
Q1: Are the generated papers publishable as-is?
No. AutoResearchClaw generates high-quality drafts that handle 70–80% of the mechanical work. Human review, refinement, and validation are still required for publication quality.
Q2: Which LLM providers are supported?
OpenAI (GPT-4, GPT-4o) and Anthropic (Claude) are the primary providers. The multi-agent debate typically uses different models for the proposer and challenger roles.
Q3: How long does a full paper generation take?
Typically 30–90 minutes depending on topic complexity, number of experiments, and API response times. Hardware-adaptive execution adjusts based on available GPU/CPU resources.
Q4: Can I customize the pipeline stages?
Yes. Each stage can be configured, skipped, or replaced. For example, you can skip experiment execution if you only want a literature review and hypothesis paper.
Q5: How does citation verification work?
Every reference in the generated paper is checked against Semantic Scholar's database. Fake citations are flagged and removed. A Semantic Scholar API key is recommended for higher rate limits.
Summary
AutoResearchClaw is the most complete autonomous paper-writing agent in the OpenClaw ecosystem. Its 23-stage pipeline, multi-agent debate system, and citation verification make it significantly more reliable than asking an LLM to "write a paper." With 9,400+ stars and 8 showcase papers, it's proven across multiple research domains. Best used as a research accelerator — generating solid drafts that humans then refine.