- name
- researchclaw
- description
- OpenClaw integration for AutoResearchClaw - fully autonomous research from idea to paper. Use when user requests academic research, literature review, or paper writing such as: (1) "Research [topic]", (2) "Write a paper about [topic]", (3) "Find literature on [topic]", (4) "Analyze [research question]", (5) "Generate academic paper from [idea]". Auto-installs AutoResearchClaw, configures LLM backend, runs 23-stage pipeline, returns LaTeX paper + experimental code + real citations.
ResearchClaw
AutoResearchClaw is a fully autonomous 23-stage research pipeline that transforms a single research idea into a conference-ready academic paper with real literature from OpenAlex, Semantic Scholar, and arXiv.
Quick Start
Basic Usage
User says: "Research [topic]"
Agent workflow:
- Check if AutoResearchClaw is installed (
which researchclaw) - If not installed: clone, setup venv, install with
pip install -e . - Copy
config.researchclaw.example.yaml→config.arc.yaml - Ask user for LLM provider choice (OpenAI-compatible or ACP agent)
- Configure with API keys or ACP agent selection
- Run:
researchclaw run --topic "[topic]" --auto-approve - Monitor progress, return results from
artifacts/rc-*/deliverables/
Configuration
Ask user for LLM backend preference:
Option 1: OpenAI-compatible API
llm:
provider: "openai-compatible"
base_url: "https://api.openai.com/v1"
api_key_env: "OPENAI_API_KEY" # or ask for key
primary_model: "gpt-4o"
fallback_models: ["gpt-4o-mini"]Option 2: ACP Agent (Claude Code, Codex, Gemini)
llm:
provider: "acp"
acp:
agent: "claude" # or "codex", "gemini", etc.
cwd: "."Installation
Check Installation
which researchclaw || echo "Not installed"Install AutoResearchClaw
cd ~
git clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
python3 -m venv .venv
source .venv/bin/activate
pip install -e .Verify Installation
researchclaw --versionRunning Research
Basic Command
researchclaw run --topic "Your research idea" --auto-approveWith Specific Config
researchclaw run --config config.arc.yaml --topic "Your research idea" --auto-approveOutput Location
Results in: ~/AutoResearchClaw/artifacts/rc-YYYYMMDD-HHMMSS-<hash>/deliverables/
Deliverables
After completion, the agent should:
- Check
deliverables/directory contents - Present key outputs:
- paper.tex - Conference-ready LaTeX - paper_draft.md - Markdown paper - references.bib - Real citations - verification_report.json - Citation integrity check - runs/ - Experimental code and results - charts/ - Generated figures - reviews.md - Multi-agent peer review
- Copy/present relevant sections to user
Pipeline Stages (23 Total)
Phase A: Research Scoping
- Stage 1: TOPIC_INIT
- Stage 2: PROBLEM_DECOMPOSE
Phase B: Literature Discovery
- Stage 3: SEARCH_STRATEGY
- Stage 4: LITERATURE_COLLECT
- Stage 5: LITERATURE_SCREEN [gate]
- Stage 6: KNOWLEDGE_EXTRACT
Phase C: Knowledge Synthesis
- Stage 7: SYNTHESIS
- Stage 8: HYPOTHESIS_GEN
Phase D: Experiment Design
- Stage 9: EXPERIMENT_DESIGN [gate]
- Stage 10: CODE_GENERATION
- Stage 11: RESOURCE_PLANNING
Phase E: Experiment Execution
- Stage 12: EXPERIMENT_RUN
- Stage 13: ITERATIVE_REFINE
- Stage 14: RESULT_ANALYSIS
- Stage 15: RESEARCH_DECISION
Phase F: Analysis & Decision
- Stage 16: PAPER_OUTLINE
- Stage 17: PAPER_DRAFT
- Stage 18: PEER_REVIEW
- Stage 19: PAPER_REVISION
Phase G: Paper Writing
- Stage 20: QUALITY_GATE [gate]
- Stage 21: KNOWLEDGE_ARCHIVE
- Stage 22: EXPORT_PUBLISH
- Stage 23: CITATION_VERIFY
Hardware Awareness
AutoResearchClaw auto-detects:
- NVIDIA CUDA (GPU)
- Apple MPS (M1/M2/M3)
- CPU-only fallback
Adapts code generation, imports, and experiment scale accordingly.
Quality Features
- Real Citations: OpenAlex, Semantic Scholar, arXiv - no hallucinated references
- 4-Layer Verification: arXiv ID → CrossRef DOI → Semantic Scholar → LLM relevance
- Multi-Agent Debate: Hypothesis generation, result analysis, peer review
- Self-Healing: NaN/Inf detection, automatic code repair
- Conference Templates: NeurIPS, ICLR, ICML support
OpenClaw Bridge Integration (Optional)
Enable in config.arc.yaml:
openclaw_bridge:
use_cron: true # Scheduled research runs
use_message: true # Progress notifications (Discord/Slack/Telegram)
use_memory: true # Cross-session knowledge persistence
use_sessions_spawn: true # Parallel sub-sessions
use_web_fetch: true # Live web search during literature review
use_browser: false # Browser-based paper collectionMetaClaw Integration (Optional)
For cross-run learning:
metaclaw_bridge:
enabled: true
skills_dir: "~/.metaclaw/skills"
lesson_to_skill:
enabled: true
min_severity: "warning"
max_skills_per_run: 5Troubleshooting
Installation Issues
# Check Python version
python3 --version # Requires 3.8+
# Install dependencies
pip install -r requirements.txtLLM API Errors
- Verify
OPENAI_API_KEYis set - Check API endpoint is accessible
- Fallback models configured correctly
Sandbox Issues
- Ensure Python path is correct:
.venv/bin/python - Check allowed imports in config
- Adjust memory limits if needed
Literature Collection Failures
- Check internet connectivity
- Semantic Scholar API key optional (higher rate limits)
- OpenAlex should work without API key
Advanced Usage
Specify Research Domains
researchclaw run --topic "Your topic" --domains ml,nlp --auto-approveTarget Specific Conference
export:
target_conference: "neurips_2025" # neurips_2025 | iclr_2026 | icml_2026Custom Prompts
prompts:
custom_file: "custom_prompts.yaml"Resources
- GitHub: https://github.com/aiming-lab/AutoResearchClaw
- Integration Guide: See AutoResearchClaw docs/integration-guide.md
- Testing Guide: See AutoResearchClaw docs/TESTER_GUIDE.md
- Discord: https://discord.gg/u4ksqW5P
Comparison with Superpowers
- ResearchClaw: Academic research, literature review, paper writing, experimental validation
- Superpowers: Software development, TDD, code review, production code
Use ResearchClaw for research/paper generation. Use Superpowers for production software implementation. They complement each other when researching then implementing findings.