NeuroClaw FAQ

Yes. The NeuroClaw repository is publicly available and can be used according to the project license in the repository. You do not pay a platform fee to inspect the code or run it locally; your own compute, storage, and third-party service costs still apply.

Yes. NeuroClaw is self-contained, but its skill library remains compatible with OpenClaw-style skill organization. Existing skills/ content can be reused, and non-neuroscience connectors stay disabled by default unless you re-enable them.

NeuroClaw runs locally unless you explicitly connect it to external services. Your datasets, files, and outputs remain under your control, and any prompts or artifacts sent to a third-party model depend on the backend you configure.

NeuroBench evaluates executability, output validity, planning quality, tool/skill reasonableness, and reproducibility readiness. It is designed for realistic neuroimaging workflows, not just one-shot question answering.

Use the Web UI with python core/agent/main.py --web --benchmark, or run the CLI batch runner with python core/agent/main.py --benchmark. To compare skill-enabled and baseline runs, add --benchmark-compare-skills.

Yes. NeuroClaw is designed for iterative experiment loops and can sustain long-running sessions. You can run separate sessions in different terminals or browser tabs, but the actual capacity depends on your local resources and the model backend you choose.

No. NeuroClaw is self-contained. The bundled installer sets up the Python environment and related dependencies, so you do not need to install OpenClaw separately to use the published NeuroClaw stack.