ScienceClaw MCP Server by beita6969

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/banner-dark.svg"> <img src="assets/banner.svg" alt="ScienceClaw" width="640"> </picture> <p><b>Skills and Operators that grow from replay-verified executions.</b></p> <p> <a href="https://arxiv.org/abs/2610.08691"><img src="https://img.shields.io/badge/arXiv-2610.08691-b31b1b?style=flat-square&logo=arxiv&logoColor=white" alt="arXiv:2610.08691"></a> <a href="https://scienceclaw.science"><img src="https://img.shields.io/badge/website-scienceclaw.science-0891b2?style=flat-square&logo=googlechrome&logoColor=white" alt="Website"></a> <a href="https://huggingface.co/datasets/beita6969/scienceclaw-eval"><img src="https://img.shields.io/badge/benchmark%20data-Hugging%20Face-f59e0b?style=flat-square&logo=huggingface&logoColor=white" alt="Benchmark data on Hugging Face"></a> <a href="https://github.com/beita6969/ScienceClaw/stargazers"><img src="https://img.shields.io/github/stars/beita6969/ScienceClaw?style=flat-square&logo=github&color=4f46e5" alt="GitHub stars"></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/beita6969/ScienceClaw?style=flat-square&color=64748b" alt="License"></a> </p> <p> <a href="#quick-start"><b>Quick start</b></a> &nbsp;·&nbsp; <a href="#how-it-works">How it works</a> &nbsp;·&nbsp; <a href="#results">Results</a> &nbsp;·&nbsp; <a href="#use-it-from-the-gateway">Gateway</a> &nbsp;·&nbsp; <a href="#scienceclaw-eval">Benchmark</a> &nbsp;·&nbsp; <a href="https://arxiv.org/abs/2610.08691">Paper</a> &nbsp;·&nbsp; <a href="packages/scienceclaw/docs/DESIGN.md">Design</a> </p> <img src="assets/paper/overview.png" alt="Overview of ScienceClaw" width="100%"> <p>_{<i>ScienceClaw-Eval spans 23 disciplines across the natural and social sciences, while ScienceClaw turns verified execution evidence into persistent Skill–Operator program updates.</i>}</p> <table> <tr> <td align="center" width="25%"><h3>23</h3>_{disciplines across the<br>natural and social sciences}</td> <td align="center" width="25%"><h3>+16.45 %</h3>_{mean out-of-distribution gain<br>over the frozen agent}</td> <td align="center" width="25%"><h3>98.23 %</h3>_{of instances pass every<br>scientific hard constraint}</td> <td align="center" width="25%"><h3>91.30</h3>_{OOD macro success rate<br>after seven rounds (from 77.72)}</td> </tr> </table> </div>

ScienceClaw is an agent system for scientific work that gets better the more it is used. It solves each task as a typed, executable workflow. When a repair is reproduced under a clean replay, it becomes a linked Skill (strategy) and Operator (a typed, executable capability), and it is kept only if it still solves its source task and improves independent validation tasks.

[!NOTE] This repository is the agent system, built on the OpenClaw gateway. Its companion benchmark, ScienceClaw-Eval, keeps its evaluation data on Hugging Face.

✨ Highlights

Idea	In practice
An evolving program	What evolves is a versioned program of Skills (decomposition, workflow construction, recovery) and typed Operators with explicit input, output and domain contracts.
Typed, executable workflows	Ports carry a schema of type, shape, unit and provenance. Nodes are fingerprinted, so an edit reruns only what it affects.
Evidence you can replay	Every result is regenerated from a reset environment and checked against the task's hard scientific constraints.
Linked Skill–Operator updates	A reproduced repair becomes a strategy patch and a typed capability, committed together. No LLM judge is needed to form, rank or select candidates.
A gate, not a guess	An update persists only if source replay reproduces it and uses it, and independent validation tasks strictly improve within budget.
You stay in control	Updates are versioned snapshots that you promote, and that you can roll back.

<a id="quick-start"></a>

🚀 Quick start

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

# One-click setup: Node, Python, the engine, its tools and weights, MCP servers, skills
chmod +x setup.sh && ./setup.sh

<details> <summary>Manual installation</summary>

pnpm install && npx openclaw onboard
pip install -e packages/scienceclaw          # Python >= 3.11; extras: [all], vision, audio, nlp, forecast, materials, retrieval
python -m scienceclaw.cli setup              # install and verify every tool and pretrained weight (--profile light skips assets > 1.5 GB)
python -m scienceclaw.cli doctor             # which tool modules can run on this machine, and why not

The engine refuses tasks until setup has completed (SCIENCECLAW_SKIP_SETUP_CHECK=1 is for development only), and the full profile downloads about 19 GB of weights. setup.sh runs it for you; SCIENCECLAW_SKIP_TOOLS=1 defers it to first use.

</details>

[!TIP] Bring your own model. The engine never assumes a model family and this repository names none. Point it at any OpenAI-compatible endpoint, a local program, or your own backend:

export SCIENCECLAW_API_BASE_URL=<your OpenAI-compatible endpoint>
export SCIENCECLAW_API_KEY=<your key>
export SCIENCECLAW_MODEL=<your model name>      # or SCIENCECLAW_<POLICY|EXECUTOR|PATCH>_MODEL per role

llm.backend can also be command (a local program reads the prompt and writes the completion) or package.module:factory. See configs/default.yaml.

<a id="how-it-works"></a>

🧠 How it works

The problem

LLM agents increasingly solve scientific tasks by connecting reasoning to data, domain tools and executable code. But a repair that works once rarely survives: it lives in a transient context, or in a single tool or prompt, so verified executions seldom become persistent improvements. Existing work evolves individual tools or Skills, and existing benchmarks treat tasks as independent episodes, so it has been hard to tell whether verified scientific executions turn into persistent, transferable improvements.

How can verified scientific executions drive persistent and transferable program-level self-evolution without updating foundation-model parameters?

<p align="center"><img src="assets/paper/task_formulation.png" alt="Task formulation" width="85%"></p> <p align="center">_{<i>Task formulation of verifiable program-level self-evolution for AI-for-Science agents.</i>}</p>

The answer: replay-gated, linked Skill–Operator evolution

1. A task for program-level self-evolution. The model Θ₀ is fixed. What evolves is an editable program A_r = (Skills, Operators). A task D_t = (D_T, D_V, D_E) specifies its objective and inputs, its evaluation protocol and hard constraints (units, feasibility, convergence, reproducibility), and its data, tools and environment.

2. Typed, execution-guided workflows. A solution is a directed graph whose ports carry a schema (type, shape, unit, provenance); an edge is valid only if types and shapes match and any unit conversion is explicit and recorded. The agent edits the graph one atomic action at a time, and the executor checkpoints every node by fingerprint and reruns only the affected descendants, so a long scientific workflow is repaired rather than regenerated.

3. Evidence you can replay. Every candidate result is regenerated from a reset environment and checked against the task's evaluator and hard constraints. A replay that fails and a later replay that passes bracket the shortest reproduced repair, e⁻ → e⁺. Replay alone never authorizes persistence.

4. Linked Skill–Operator candidates, with no LLM judge. The reproduced repair is split by edit type. Control edits (topology, routing, configuration) become a Skill patch. Generated or repaired executable nodes are grouped into convex components and abstracted into typed Operators, each checked by boundary replay in isolation. Both are committed as one atomic bundle, so a strategy never arrives without the capability behind it, nor a capability without a strategy that selects it.

5. A replay-and-validation gate. A candidate persists only if source replay reproduces the repair and actually uses every new component (R_src = Pass ∧ Use), every hard integrity and scientific check holds on independent validation tasks, its cost stays within budget, and it strictly improves the validation score over the incumbent. A noise guard requires at least two improved and at most one regressed validation episode. Otherwise the incumbent program is kept.

<p align="center"><img src="assets/paper/method_overview.png" alt="Definition and overview of the ScienceClaw task"></p> <p align="center">_{<i>Given task specification D_t and agent program A_r, ScienceClaw produces scientific solution Z_t and retains a candidate update only after source-task replay and independent program validation.</i>}</p>

<a id="results"></a>

📊 Results

Seven evolution rounds over a common stream of 23 disciplines, with 64 IID and 64 OOD instances per discipline, one fixed foundation model, and the same tools, source stream, validation data and update budget throughout. OOD means an independently sourced dataset of the same discipline; OOD results never generate or select updates.

• Consistent gains. ScienceClaw improves on the frozen agent in every discipline: +16.45 % on average out-of-distribution (+11.57 % to +23.34 %) and +12.25 % in-distribution. Its OOD score falls 11.73 % below its IID score on average, against 16.06 % for the frozen agent.
• It keeps improving. The OOD macro success rate rises from 77.72 to 91.30 over seven rounds (+13.59 pp). 70 of 161 submitted candidates are promoted (43.48 %), so the gate is selective without stalling.
• It transfers and retains. 18 of 20 cross-family pairs transfer positively (mean +1.58 pp, against +14.69 pp within a family). Average forgetting is 0.13 pp (maximum 0.51 pp), with 11.89 % negative transfer.
• It is reliable. Hard constraints pass on 98.23 % of instances and only 1.23 % of promotions are erroneous.
• The linkage and the gate are what matter. Committing Skills and Operators separately keeps only 60 % of the gain; removing Operator evolution keeps 59 %. Without independent IID selection only 15 % remains; without scientific constraints, source replay or the independent validator, 46 %, 55 % and 59 %.
• Execution structure is the base. The full system runs 4.84 planner rounds and 4.24 distinct Operators per task, repairs 71 % of failures from feedback, recovers 89 % after interruption and replays 96 % cleanly (a single-turn agent: 0 %, 31 %, 78 %).

<p align="center"><img src="assets/paper/ablation_heatmap.svg" alt="Ablation heatmap across 23 disciplines"></p> <p align="center">_{<i>Mean of IID and OOD task-native scores of the ablation variants across 23 disciplines. Colours are normalised within each discipline (darker is better); the two groups follow higher-is-better and lower-is-better metrics.</i>}</p> <p align="center"><img src="assets/paper/mechanisms.svg" alt="Linked mechanism, workflow mechanics, reliability and cost, and promoted candidates" width="100%"></p> <p align="center">_{<i>(f) Gain over the frozen agent (%) of the linked-mechanism variants on Commerce (MASE) and Law (mAP). (g) Workflow mechanics: planner rounds, distinct Operators, Operator diversity, feedback repair, checkpoint recovery and clean replay. (h) Hard-constraint pass rate against cost per OOD gain (ScienceClaw = 1), coloured by planner wall-time share. (i) Promoted candidates and promotion rate.</i>}</p> <details> <summary>Per-discipline scores: ScienceClaw against the frozen agent</summary>

Task-native scores, compared only within a discipline (metrics differ in units and direction); arrows give the direction of "better". Gains are the relative improvement in the better direction, computed from the rounded scores shown.

FoR	Discipline (metric)	Frozen IID	ScienceClaw IID	Gain	Frozen OOD	ScienceClaw OOD	Gain
30	Agricultural sci. (PQ+) ↑	69.4761	76.9612	+10.77 %	64.6829	75.7629	+17.13 %
31	Biological sci. (Spearman) ↑	0.5057	0.5816	+15.01 %	0.3745	0.4234	+13.06 %
32	Biomedical sci. (DSC) ↑	0.7816	0.8556	+9.47 %	0.7088	0.8226	+16.06 %
33	Built env. (NRMSE %) ↓	51.0997	45.0065	+11.92 %	53.5009	45.0970	+15.71 %
34	Chemical sci. (ROC-AUC) ↑	0.6518	0.7545	+15.76 %	0.6205	0.7478	+20.52 %
35	Commerce (MASE) ↓	1.6009	1.4653	+8.47 %	1.8740	1.5986	+14.70 %
36	Creative arts (SDR (dB)) ↑	8.9208	10.1867	+14.19 %	8.0993	9.5619	+18.06 %
37	Earth sci. (RMSE (K)) ↓	1.0562	0.9721	+7.96 %	1.1364	0.9580	+15.70 %
38	Economics (sMAPE (%)) ↓	12.7394	11.3320	+11.05 %	18.1343	15.5025	+14.51 %
39	Education (10-mask acc.) ↑	0.5965	0.6878	+15.31 %	0.6202	0.7087	+14.27 %
40	Engineering (DCASE score) ↑	0.5709	0.6535	+14.47 %	0.4663	0.5269	+13.00 %
41	Environmental sci. (CRPS) ↓	0.8104	0.7164	+11.60 %	0.6504	0.5751	+11.58 %
42	Health sci. (Clin. utility) ↑	0.4971	0.5514	+10.92 %	0.6834	0.7796	+14.08 %
43	History (cMER-micro) ↓	0.0185	0.0166	+10.27 %	0.0296	0.0251	+15.20 %
44	Human society (nRMSE) ↓	0.0113	0.0100	+11.50 %	0.0213	0.0181	+15.02 %
45	Indigenous (chrF++) ↑	15.4451	17.4351	+12.88 %	13.7205	15.9778	+16.45 %
46	Computing sci. (pass@1) ↑	0.7656	0.8750	+14.29 %	0.3750	0.4531	+20.83 %
47	Language & culture (LAS) ↑	0.7288	0.8138	+11.66 %	0.5599	0.6497	+16.04 %
48	Law (mAP) ↑	0.7513	0.8554	+13.86 %	0.6918	0.8289	+19.82 %
49	Mathematics (Oracle acc.) ↑	0.8438	0.9531	+12.95 %	0.7344	0.8750	+19.14 %
50	Philosophy (F1) ↑	0.4178	0.4866	+16.47 %	0.4166	0.5138	+23.33 %
51	Physical sci. (MAE) ↓	36.6271	32.1581	+12.20 %	44.6757	37.1306	+16.89 %
52	Psychology (Micro acc.) ↑	0.6088	0.6618	+8.71 %	0.5615	0.6586	+17.29 %

</details> <details> <summary>Continual evolution: OOD macro success rate by round</summary>

OOD macro success rate (%) of each snapshot after round r, with the gain over the initial program and the candidates promoted and rejected out of 161. RuleEvo is a ScienceClaw variant whose evolution uses deterministic trace projection alone, with no generative evolution roles.

Method	0	1	2	3	4	5	6	7	Gain (pp)	Promoted	Rejected	Rate (%)
Frozen	77.72	77.72	77.72	77.72	77.72	77.72	77.72	77.72	0.00	0	0	–
RuleEvo	77.72	78.40	79.21	78.87	79.62	80.37	80.03	81.32	3.60	37	124	22.98
ScienceClaw	77.72	80.43	82.68	82.47	86.14	87.57	89.81	91.30	13.59	70	91	43.48

</details>

Reported trajectories are final snapshots, not uncertainty estimates over source orders or model configurations, and cost comparisons are relative to the protocol, not absolute. The paper has the full tables, ablations and the limitations discussion.

<a id="use-it-from-the-gateway"></a>

🔌 Use it from the gateway

Enable the plugin in ~/.openclaw/openclaw.json (see extensions/scienceclaw/README.md). It registers four agent tools:

Tool	Operations
scienceclaw_canvas	open, act, render, replay, finish, status, list
scienceclaw_tools	search, show, status, weights, setup
scienceclaw_program	summary, skills, operators, show, history, rollback
scienceclaw_evolve	val_add, val_list, val_remove, propose, gate, run, status, candidates, show

A session, end to end

1. Declare the task. scienceclaw_canvas(operation=open, task={objective, inputs, required_output, constraints}). Each input becomes a read-only load_<name> tool restricted to the configured input roots; constraints (finite, shape, type, range, nonempty, len_eq_input, or an evaluator-only metric bar) are the acceptance test.
2. Build the workflow. The agent finds tools with scienceclaw_tools, then adds, modifies or removes one node or edge per act, reading the typed feedback after each edit.
3. Verify. replay re-executes the whole graph from a reset state; finish returns the verified deliverable.
4. Evolve (optional). Register independent validation tasks with val_add (the default noise guard needs at least two), then propose candidates from a finished, verified session and gate them. A session that contained a repair yields a linked Skill and Operator; one without a failed replay yields an Operator only. A candidate that passes becomes ready.
5. You decide. Review and promote with the CLI; every promotion is a new program version that can be rolled back.

python -m scienceclaw.cli live candidates
python -m scienceclaw.cli live show <candidate>
python -m scienceclaw.cli live promote <candidate>
python -m scienceclaw.cli live rollback <version>

The same engine speaks line-delimited JSON on python -m scienceclaw.rpc if you want to drive it from your own host.

🧰 Skills and tools

Component	What it provides
300+ skills	skills/ holds the skill library. The 36 scienceclaw-* skills form the seed program: 12 general ones (canvas orchestration, evolution, and task patterns such as retrieval, prediction and verification) and the discipline skills below. Evolved Skills are written back as versioned records of the same program.
Discipline skills	scienceclaw-benchmark-for30 … for52, indexed by scienceclaw-benchmark, describe the task family of each benchmark discipline: inputs, deliverable, how quality is judged, and the tools and weights that fit. They are retrieved like any other skill when a task matches.
scilib	Classical toolkits and frozen pretrained models for science: forecasting, segmentation, source separation, protein and molecular models, causal inference, parsing, formal solvers and more. Heavy models run on a GPU-tool broker when they cannot run locally. scienceclaw_tools and python -m scienceclaw.cli tools search, describe and probe them.
Research protocol	SCIENCE.md governs literature work in the gateway: every citation must come from a tool result in the current conversation, searches cross several sources, and results are written to a file before an answer is final.

🛡️ Safety and governance

[!WARNING] Persistent executable updates can reuse errors and widen the attack surface. Code nodes run in a sandbox (separate process, scrubbed environment, static scan, runtime audit guard, and user/network/pid namespaces where the host supports them), but run untrusted workloads in a container as well.

• Nothing persists without evidence. Source replay, validation, budget and strict improvement all have to hold, and the gate fails closed when the model is unavailable.
• Updates are your decision, and reversible. Candidates wait as ready until promoted; every version is a snapshot with a receipt and can be rolled back.
• Provenance everywhere. Ports record units and upstream transformations; operators record their source episode, steps and parent version.

ScienceClaw should support, not replace, experts. High-stakes use needs provenance, licensing and privacy safeguards, and independent review.

<a id="scienceclaw-eval"></a>

🧪 ScienceClaw-Eval

ScienceClaw-Eval benchmarks continual self-evolution rather than single-shot ability. Systems share the foundation model, the initial program, the source order, the tools and the budget, and are compared on:

• a source stream that supplies the only evolution evidence,
• an independent validation set used for candidate selection,
• held-out ID and same-discipline cross-dataset OOD sets,
• a replay set of earlier source tasks that measures retention.

An instance counts as solved only if execution completes within budget, the task-native metric meets its acceptance rule, and every scientific hard constraint holds; success is macro-averaged over disciplines. Each task has an isolated environment and a task-specific evaluator, reviewed by domain experts and verified by reset replay.

<p align="center"><img src="assets/paper/benchmark_construction.png" alt="Construction of ScienceClaw-Eval"></p> <p align="center">_{<i>Construction of ScienceClaw-Eval: scientific-task collection, executable instantiation, validation and reproduction, and lineage-aware evaluation splits.</i>}</p>

The evaluation data (64 IID and 64 OOD records for each discipline) is on Hugging Face: beita6969/scienceclaw-eval.

<details open> <summary>The 23 disciplines, their tasks and metrics</summary>

Code	Discipline (ANZSRC division)	Task	Metric	IID source	OOD source
FoR30	Agricultural, veterinary and food sciences	plant and leaf panoptic segmentation	PQ+ ↑	PhenoBench	CropAndWeedAndLeaf
FoR31	Biological sciences	protein variant fitness ranking	Spearman ↑	ProteinGym	TAPE fluorescence
FoR32	Biomedical and clinical sciences	hippocampus segmentation in MRI	DSC ↑	MSD Task04	UCL/Dryad hippocampus
FoR33	Built environment and design	building load forecasting	NRMSE (%) ↓	BuildingsBench	EULP
FoR34	Chemical sciences	molecular activity classification	ROC-AUC ↑	OGB ogbg-molhiv	BACE
FoR35	Commerce, management, tourism and services	tourism series forecasting	MASE ↓	Monash Tourism Monthly	Monash Tourism Quarterly
FoR36	Creative arts and writing	music source separation	SDR (dB) ↑	MUSDB18	MoisesDB
FoR37	Earth sciences	2 m temperature forecasting	RMSE (K) ↓	WeatherBench 2 (ERA5, 2019)	WeatherBench 2 (ERA5, 2020)
FoR38	Economics	macroeconomic forecasting	sMAPE (%) ↓	World Bank WDI	World Bank WDI
FoR39	Education	adaptive educational testing	10-mask accuracy ↑	Eedi Task 4	EdNet-KT1
FoR40	Engineering	anomalous sound detection	DCASE score ↑	DCASE 2024 Task 2	DCASE 2023 Task 2 ToyNscale
FoR41	Environmental sciences	probabilistic aquatic forecasting	CRPS ↓	NEON aquatics	USGS river metabolism
FoR42	Health sciences	sepsis early warning	clinical utility ↑	PhysioNet/CinC 2019	SepsisExp
FoR43	History, heritage and archaeology	OCR post-correction	cMER-micro ↓	HIPE-OCRepair-2026	ICDAR 2019 POCR
FoR44	Human society	causal treatment-effect estimation	nRMSE ↓	ACIC 2016	IHDP
FoR45	Indigenous studies	Indigenous-language captioning	chrF++ ↑	AmericasNLP 2026	Bloom Captioning (Mam)
FoR46	Information and computing sciences	code generation	pass@1 ↑	HumanEval	SWE-bench Verified
FoR47	Language, communication and culture	dependency parsing	LAS ↑	UD: Marathi, Italian, Arabic, Croatian	UD: Indonesian, Swedish Sign Language, French, Hindi
FoR48	Law and legal studies	contract evidence retrieval	mAP ↑	ContractNLI	ACORD
FoR49	Mathematical sciences	SMT satisfiability prediction	oracle-agreement accuracy ↑	SMT-LIB 2025	SMT-LIB 2024
FoR50	Philosophy and religious studies	human-value detection	F1 ↑	Touché23-ValueEval	ETHICS
FoR51	Physical sciences	phonon property prediction	MAE ↓	Matbench phonons	Kyoto PhononDB
FoR52	Psychology	human choice prediction	micro accuracy ↑	Psych-201	Psych-101

Every row of the dataset records its own source, license and URL; the dataset card on Hugging Face has the details.

</details>

🗂️ Repository

<details> <summary>Module map: every paper object and where it lives in the code</summary>

Paper	Code (packages/scienceclaw/)
Task D_t = (D_T, D_V, D_E)	scienceclaw/task.py (Episode); a live task declaration becomes one in canvas/live.py
Program A_r = (Skills, Operators)	core/program.py, versioned by program/store.py; seed Skills from skills/scienceclaw-* (program/seed.py); 112 typed library Operators in program/specs/
Typed workflow graph, Compat	core/schema.py, core/graph.py, core/actions.py
Execution-guided orchestration	agent/ (policy, prompts, solver) and canvas/session.py, where the gateway agent is the policy
Checkpointed execution, reset replay	runtime/executor.py, runtime/replay.py
Sandbox and integrity	runtime/sandbox.py, runtime/integrity.py, runtime/node_worker.py
Retrieval of Skills and Operators	core/retrieval.py (BM25 plus metadata match)
Repair attribution, edit split	evolution/attribution.py, evolution/split.py
Skill patch, Operator abstraction, boundary replay	evolution/skill_patch.py, evolution/operator_abstraction.py
Linked bundle, source-replay check, gate	evolution/bundle.py, evolution/validation.py
Strict-improvement update	evolution/evolver.py (batch), evolution/live.py (gateway, user-promoted)
Scientific tools	scilib/ (42 modules), a catalog of 292 tool functions, 28 pretrained-weight assets (scienceclaw/tools/weights.json)
Gateway integration	extensions/scienceclaw/ plugin and python -m scienceclaw.rpc

</details>

ScienceClaw/
├── packages/scienceclaw/    # the engine: typed workflows, runtime, Skill/Operator program, evolution, tool library
│   ├── scienceclaw/         #   core, runtime, agent, canvas, program, evolution, llm, tools, rpc, cli
│   ├── scilib/              #   42 scientific tool modules
│   ├── configs/             #   default run configuration (no model, no paths)
│   ├── scripts/             #   LLM serving, GPU-tool broker and environment scripts
│   └── docs/                #   DESIGN.md (the contract) and INTEGRATION.md
├── extensions/scienceclaw/  # gateway plugin: canvas, tools, program, evolve
├── skills/                  # 300+ skills, including the seed program and the 23 discipline skills
├── mcp-servers/             # arXiv-LaTeX and ChEMBL MCP servers
├── assets/                  # banner and the paper's figures
├── SCIENCE.md               # research protocol for the gateway agent
├── setup.sh                 # one-click setup
├── src/, ui/, apps/, ...    # the OpenClaw gateway, web UI and apps
└── docs/                    # gateway documentation

Documentation: DESIGN.md (the full contract, including the decisions the paper leaves open) · INTEGRATION.md (gateway, plugin, skill and evolution map) · plugin guide · SCIENCE.md

🛠️ Reporting a problem

For bugs or setup problems, open an issue with:

• The repository revision (git rev-parse HEAD), operating system, and relevant Python, Node.js, and pnpm versions.
• The command or gateway tool operation you ran, the expected result, and the actual result.
• The smallest reproducible example and relevant error output. For tool setup problems, include the relevant output from python -m scienceclaw.cli doctor.

Remove API keys, access tokens, private data, and local credentials from examples and logs before posting.

📖 Citation

If you use ScienceClaw or ScienceClaw-Eval, please cite the paper (arXiv:2610.08691):

@misc{zhang2026scienceclaw,
  title         = {ScienceClaw: Benchmarking Continual Self-Evolution of AI-for-Science Agents Across the Natural and Social Sciences},
  author        = {Zhang, Mingda and Liu, Wenjin and Shen, Tiesunlong and Xiao, Zikai and Lin, Zhenghong and Xu, Qing and Cambria, Erik and Tang, Xiaoying and Luo, Haoran},
  year          = {2026},
  eprint        = {2610.08691},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2610.08691}
}

📬 Contact and license

mingdazhang@ieee.org · MIT, see LICENSE.