ScienceClaw

A self-evolving AI research colleague for scientists.

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Why ScienceClaw?

General-purpose AI assistants are built for everyone. ScienceClaw is built for researchers.

The core idea is simple: an AI that does real scientific work — searching literature, querying databases, running analyses — and gets better at it the more you use it. It remembers your research context across sessions, adapts its skills to your field, and never fabricates a citation.

ScienceClaw is built on the OpenClaw engine, but redesigned from the ground up for academic research.

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🧬 Core 1: Self-Evolving Skills

This is ScienceClaw's most important feature.

Most AI tools ship with a fixed set of capabilities. ScienceClaw's skills evolve with you. Every time you complete a research task, the system learns:

What this means in practice:

• Week 1: You study immunology. ScienceClaw learns that PubMed + Semantic Scholar works best for your queries, that you prefer forest plots over tables, and that you always need PMID + DOI in citations.
• Week 4: The system has created specialized skills for your subfield — optimized search templates, preferred statistical methods, database priority chains tuned to immunology literature.
• Month 3: ScienceClaw handles your domain like a trained research assistant. It knows which databases to hit first, which journals matter, and how you like your output formatted.

Compared to standard OpenClaw: OpenClaw ships with ~54 general-purpose skills that don't change. ScienceClaw starts with 285 skills and grows from there — the agent writes new SKILL.md files at runtime without any redeployment.

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🧠 Core 2: Research Memory That Persists

Standard AI assistants forget everything when the conversation ends. ScienceClaw doesn't.

What this enables:

• "Continue the literature review we started last Tuesday" — it remembers where you left off
• "Use the same search strategy that worked for the BRCA2 project" — it retrieves past patterns
• Cross-session knowledge accumulation — findings from project A can inform project B
• Smart context pruning — when the context window fills up, it preserves statistical results, effect sizes, and key citations while compacting intermediate steps

Compared to standard OpenClaw: OpenClaw has a basic memory plugin. ScienceClaw adds temporal decay weighting, LanceDB vector storage, and cross-session research pattern retrieval — specifically designed for long-running academic work.

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⏱️ Core 3: Built for Long-Duration Research

A real literature review takes hours, not seconds. Most AI tools time out after a few minutes. ScienceClaw is engineered for extended research sessions:

Capability	Standard OpenClaw	ScienceClaw
Agent timeout	600s (10 min)	3600s (1 hour+)
Session persistence	Ends with conversation	Heartbeat keeps sessions alive across interruptions
Research depth	Single-pass response	Multi-phase protocol with mandatory depth thresholds
Minimum effort	No guarantee	Quick=5, Survey=30, Review=60, Systematic=100+ tool calls
Early stopping	Common	Anti-premature-conclusion checklist blocks shallow answers
Context management	Basic truncation	Smart compaction preserves key findings when context fills up

The persistence protocol enforces real research depth. Before ScienceClaw concludes any task, it must verify:

• ✅ Searched at least 3 different databases/sources
• ✅ Retrieved full metadata (not just titles)
• ✅ Cross-referenced findings across sources
• ✅ Checked for contradictory evidence
• ✅ Verified key statistics against primary sources
• ✅ Organized results into a structured output file
• ✅ Met the minimum tool-call threshold for the task type

If any box is unchecked, it keeps working instead of giving you a half-baked answer.

Compared to standard OpenClaw: OpenClaw's default 10-minute timeout is fine for sending messages and setting reminders. ScienceClaw's 1-hour sessions with heartbeat monitoring and mandatory depth enforcement are built for real academic research.

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🚫 Core 4: Zero Hallucination

This is the highest-priority rule in the entire system. It's non-negotiable.

The problem: General AI assistants routinely fabricate citations — inventing DOIs, making up author names, citing papers that don't exist. In scientific work, this is catastrophic.

ScienceClaw's approach:

EVERY citation must come from a tool result in the CURRENT conversation.

If a database didn't return it → you can't cite it.
If you're not sure → say "not verified" explicitly.
If you can't find evidence → say so. Don't guess.

No "I think." No "probably." No hallucinated PMIDs.

This is enforced at the protocol level in SCIENCE.md — the 629-line research protocol that governs all agent behavior. It's not a suggestion. It's a hard rule that applies before any other instruction.

Compared to standard OpenClaw: OpenClaw has no special hallucination controls. ScienceClaw's SCIENCE.md protocol treats every factual claim as requiring evidence — the same standard you'd apply to a manuscript under peer review.

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🌍 Core 5: All of Science, Not Just Biomedicine

ScienceClaw covers natural sciences AND social sciences across dozens of disciplines:

📋 Full discipline & database list

Natural Sciences

Domain	Key Skills & Databases
Biomedicine	PubMed, UniProt, KEGG, PDB, ClinicalTrials, gnomAD, scanpy, biopython
Chemistry	PubChem, ChEMBL, RDKit, drug-discovery, molecular-dynamics
Genomics	NCBI Entrez, Ensembl, ClinVar, GEO, phylogenetics
Materials Science	Materials Project, pymatgen, materials-screening
Physics	astropy, quantum-computing, physics-solver, simulation
Environmental Science	Copernicus climate data, geospatial analysis, GIS tools
Food Science	Specialized analysis pipelines

Social Sciences

Domain	Key Skills & Databases
Economics	World Bank, SSRN, census data, econometrics
Political Science	Policy analysis, legislative data
Psychology	Experimental design, statistical testing, meta-analysis
Linguistics	spaCy, NLTK, NLP analysis
Education	Research methodology, assessment analysis
Sociology	Network analysis, survey methods

Cross-Disciplinary Tools

Category	Capabilities
Statistics	SciPy, statsmodels, scikit-learn, effect sizes, confidence intervals, multiple comparison corrections
Visualization	matplotlib, plotly, seaborn, publication-quality figures
Writing	LaTeX papers, systematic reviews (PRISMA), grant proposals, patent drafting
Mathematics	SymPy symbolic computation, numerical methods, optimization

285 skills total — and growing, because the self-evolution system creates new ones as you work.

Compared to standard OpenClaw: OpenClaw has no scientific database integrations. No PubMed, no UniProt, no arXiv, no World Bank. ScienceClaw connects to 25+ academic databases with structured API query skills across all major scientific disciplines.

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Quick Start

# Clone
git clone https://github.com/beita6969/ScienceClaw.git
cd ScienceClaw

# One-click setup (installs everything: Node, Python, MCP servers, skills)
chmod +x setup.sh && ./setup.sh

# Or manual install
pnpm install && npx openclaw onboard

Enable Research Features

The setup.sh script automatically configures everything. For manual setup, edit ~/.openclaw/openclaw.json:

{
  "gateway": { "mode": "local" },
  "plugins": {
    "slots": { "memory": "memory-core" },
    "entries": {
      "memory-core": { "enabled": true },
      "memory-lancedb": { "enabled": true }
    }
  },
  "agents": {
    "defaults": {
      "heartbeat": { "interval": 1800 }
    }
  }
}

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Project Structure

ScienceClaw/
├── setup.sh                # 🦞 One-click setup (run this first!)
├── SCIENCE.md              # 629-line research protocol (the brain)
├── skills/                 # 285 skill definitions (and growing)
│   ├── skill-evolution/    # Self-improving skill system
│   ├── research-reflection/# Post-task learning & evaluation
│   ├── skill-creator/      # Runtime skill generation
│   └── ...
├── src/                    # Core engine
│   ├── memory/             # 4-layer memory (temporal decay, LanceDB)
│   ├── agents/             # Agent orchestration & persistence
│   └── skills/             # Skill loading & execution
├── ui/                     # Web-based research gateway UI
├── extensions/             # Plugin system
├── deploy/                 # Docker, Fly.io, Podman configs
├── config/                 # Vitest, build, lint configs
└── docs/                   # Documentation

Contact Us

📧 mingdazhang@ieee.org

License

MIT — see LICENSE.