Open Science Desktop

Local-first, model-agnostic AI research workbench for macOS, Windows & Linux.

Formerly Open Science. An open-source desktop alternative to Claude Science and similar AI-for-science workbenches — built with Tauri, MCP, agent skills, and reproducible artifacts. It connects agents, notebooks, files, figures, reports, runs, and review into one auditable desktop workflow.

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News

• 2026-07-21 — 🌐 Access from anywhere — even your phone. A token-authenticated gateway serves the real desktop UI to a CLI, a browser on your LAN, or your phone (loopback by default; LAN is opt-in). Start a run at your desk and read the finished figure and report on your phone. (v0.2.3)
• 2026-07-21 — 🧭 Browser control. The agent can drive your own Chrome — profile and logins intact — to read the live web the way you do, or an isolated private browser on demand. (v0.2.3)
• 2026-07-09 — 🎉 #1 on ResearchClawBench. Open Science Desktop ranks #1 by scored-task average on ResearchClawBench, an end-to-end benchmark for autonomous scientific research agents (Pass@1 leaderboard).

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Contents

• ✨ What it does
• 🎬 See it in action
• 🧪 Current capabilities
• 🔌 Skills and connectors
• 📦 Install
• 🚀 Build from source
• 🔒 Safety and privacy
• 🗂️ Repository layout
• 📌 Status
• 🤝 Contributing
• 📖 Citation
• ⚖️ License

What it does

Runs the whole research loop — from a broad direction to a finished paper: exploration, literature survey, hypothesis, experiment code, analysis, figures, and write-up, in one continuous, auditable session.

• Autonomous research agents — the bundled ai4s-agent chains specialist skills end to end (explore → survey → experiment → write), and each stage drops a real, inspectable artifact into your workspace, not just a chat reply.
• Everything traces back — figures, tables, reports, notebooks, and run outputs link to the exact code, inputs, environment, model output, and conversation that produced them.
• Local-first and yours — sessions, data, provenance, notebooks, and run records live in local folders on your machine. Nothing leaves by default.
• Model-agnostic runtime — the UI talks through packages/sdk to a bundled, pinned OpenCode sidecar. Bring your own model; providers, skills, and MCP servers stay pluggable.
• Reproducible by construction — local, SSH/Slurm, Modal, and notebook-batch runs are captured as reproducible run records, not loose terminal scrollback.
• Reach it from anywhere — a built-in, token-authenticated gateway serves the real desktop UI to a browser on your LAN or phone (or, with a tunnel, from anywhere) — kick off a run at your desk and check on it from your phone over lunch. Off by default; loopback-only until you opt in, and API keys never leave the machine.
• Drives your own browser — the agent can control your real Chrome, with your profile and logins intact, to read the live web the way you do — or an isolated private browser when you'd rather it not.
• Extensible — agent skills, MCP servers and one-click science connectors, / commands, ! shell mode, and a model-agnostic SDK.

See it in action

One prompt -> a publication-grade figure, and every point traces to the exact code and inputs that made it. No black boxes: open any artifact to see its generating script, its data files, and the conversation that produced it.

Literature -> a verifiable report. Fan the search out across sources, draft a manuscript rendered as a PDF, and gate it on a citation review — DOIs resolved, unsourced numbers and figure/code inconsistencies flagged — before anything ships.

Drives your own Chrome. The agent reads the live web through your real browser profile — logins and all — then turns what it finds into a figure and a sortable CSV.

Research from anywhere — even your phone. A built-in authenticated gateway serves the real desktop UI to a browser on your LAN (or a tunnel), so you can kick off a run at your desk and read the finished figure and report on your phone.

New session

A finished analysis

A reproduced benchmark

More screenshots

Current capabilities

The research loop, as skills. One meta-skill runs the full pipeline; each stage is a self-contained skill that produces a real, gradeable artifact — runnable on any model OpenCode supports:

Skill	Role	Primary output
ai4s-agent	Runs the four skills below, in order	The full research package
research-explorer	Turn a broad direction into concrete topics	research_exploration.md, topic_matrix.md, literature_pre_survey.md
literature-survey	Write a literature survey	6–20 pp PDF, 60+ real citations, LaTeX source, taxonomy figures
experiment-suite	Build an experiment package	Design doc, runnable code, results.json with provenance, figures, report
paper-writer	Write a research paper	8–14 pp PDF, 200+ citations, 4–8 figures, tables
mindmap-render	Render a mindmap	Image generated from a topic_matrix.md
integrity-auditor	Audit a paper's integrity	Image / numerical / logical findings, 4-level evidence grading, audit_report.md

These ship in the ai4s-skills pack alongside first-party review skills and the office/document skills below.

Platform

Area	Current state
Desktop shell	Tauri 2 + React + TypeScript + Vite, with macOS, Windows, and Linux desktop builds.
Runtime	Bundled OpenCode sidecar, auto-started by the app, isolated from the user's own OpenCode config/data.
Sessions	Multi-session chat/history, dated workspace folders, global history across workspaces, / commands, and ! shell mode.
Files	Global and per-session file browsing, context menu actions, external open/reveal, copy path, and local preview server.
Remote access	Token-authenticated gateway that serves the real UI to a CLI, a LAN web browser, or your phone (loopback by default, LAN opt-in); read-only vs full access modes; copy a link with the token embedded to connect in one tap. API keys never cross the wire.
Browser control	The agent drives your own Chrome — profile and login state preserved — reading pages through the accessibility tree, or an isolated/private browser on demand.
Notebooks	Real .ipynb files, Python and R notebook creation, local kernel execution, managed Jupyter environment via bundled uv, and an Open JupyterLab action.
Runs	Append-only run logs, global SQLite run index, search/facets/pagination, local/remote surfaces, output links, logs, and reproduce prompts.
Provenance	.openscience/provenance.jsonl tracks file versions and links produced artifacts back to the run or edit that created them.
Review	Traceability, statistics-integrity, domain-check, large-file, publication-figure, remote-compute, and Modal run skills are bundled as first-party skills.
Viewers	PDF, image, video, HTML, Markdown, code, CSV/TSV tables with charts, DOCX, XLSX, PPTX, molecules, 3D meshes, genome tracks, FITS, DOS/DOSCAR, EIGENVAL bands, qcode, anomaly maps, and phase files.
Models	OpenCode provider catalog, OAuth/API-key provider flows, custom OpenAI-compatible endpoints, and local/provider-specific options supported by OpenCode.
Interface languages	English, Simplified Chinese, Japanese, Spanish, German, French, and Korean. Portuguese (Brazil) and Arabic are registered but not selectable yet.

Skills and connectors

Bundled skills are fetched for builds and releases instead of being committed into git history:

• ai4s-skills pack from ai4s-research/ai4s-skills.
• Office/document skills from the Apache-2.0 anthropics/skills repository: docx, pdf, pptx, and xlsx.
• First-party core skills in runtime/skills/core/: traceability-review, stats-integrity, domain-check, large-file, publication-figures, remote-compute, and modal-run.

One-click science MCP connectors currently include:

• Literature search: arXiv, PubMed, Crossref, Semantic Scholar, bioRxiv/medRxiv.
• Biomedical databases: PubMed, ClinicalTrials.gov, MyVariant/ClinVar.
• Materials Project.
• FRED economic data.
• Space weather.
• Open-Meteo weather and climate.
• USGS water data.

You can also add any local or remote MCP server from Settings. See docs/CONNECT_YOUR_TOOLS.md.

For a neutral positioning note, see Open Science Desktop vs OpenScience.

Install

Download the latest installer from the Releases page.

• macOS: .dmg / .app, Apple Silicon and Intel, macOS 13 Ventura or later.
• Windows: NSIS .exe and .msi, Windows 10/11 x64.
• Linux: .deb and .rpm on x86_64 Linux.

Builds are not code-signed or notarized yet.

macOS: if Gatekeeper says the app is damaged or from an unidentified developer, install it into Applications and run:

xattr -cr "/Applications/Open Science.app"

Windows: if SmartScreen appears, choose More info -> Run anyway.

Linux:

sudo apt install ./OpenScience_*.deb
# or
sudo rpm -i OpenScience_*.rpm

Build from source

Prerequisites:

• Node.js >= 20
• pnpm 9
• Rust toolchain
• macOS, Windows, or Linux system dependencies required by Tauri

git clone https://github.com/ai4s-research/open-science
cd open-science
pnpm install

# Fetch pinned sidecars and bundled skills. These are git-ignored.
bash scripts/dev/fetch-opencode.sh
bash scripts/dev/fetch-uv.sh
bash scripts/dev/fetch-skills.sh

# Run in development or build installers.
pnpm --filter @ai4s/desktop tauri dev
pnpm --filter @ai4s/desktop tauri build

Useful checks:

pnpm test
pnpm typecheck
pnpm lint

Safety and privacy

• Workspace files, raw data, session history, provenance, notebooks, and run records stay local by default.
• Command execution, file deletion, dependency installation, and remote connections are human-approved flows in the desktop app.
• Provider credentials are written to app-private runtime config, not to the workspace, provenance, git, exports, or global OpenCode config.
• Settings includes a plain-language data-flow view explaining what can be sent to the selected model provider.

Repository layout

Path	Purpose
apps/desktop/	Tauri + React desktop app.
packages/sdk/	OpenCodeClient; keeps the UI from calling OpenCode directly.
packages/shared/	Shared domain types and chart palette.
packages/ui/	Shared UI package.
runtime/skills/core/	First-party scientific skills.
runtime/skills/external/	Build-fetched external skills.
runtime/harness/	Runtime harness knowledge and operator context.
runtime/mcp/	MCP runtime notes/configuration.
examples/	Built-in example workspaces.
scripts/dev/	Sidecar, uv, skill fetchers, and focused regression probes.
docs/	Product, technical, operator, connector, and research notes.

Status

The project is a working desktop MVP in active development. The most reliable current implementation log is PROGRESS.md. Product and architecture notes live in docs/PRD.md and docs/TECHNICAL_DESIGN.md, but those documents include target design as well as historical status notes.

Near-term work is focused on signed/notarized releases, broader Windows/Linux verification, auto-update, richer connector hardening, and continued reproducibility review.

Contributing

Issues and PRs are welcome. Keep changes minimal and verifiable, follow AGENTS.md, and run the checks before opening a PR. For discussion, join the Open Science Discord or the linux.do community.

Citation

If you use Open Science Desktop in your research, please cite it:

@software{open_science_desktop,
  author  = {{The Open Science Desktop Contributors}},
  title   = {Open Science Desktop: a local-first, model-agnostic AI research workbench},
  year    = {2026},
  version = {0.2.4},
  doi     = {10.5281/zenodo.21501479},
  url     = {https://github.com/ai4s-research/open-science},
  license = {MIT}
}

GitHub's "Cite this repository" button (top of the repo page, generated from CITATION.cff) provides the same reference in APA and BibTeX.

License

MIT. Bundled third-party skills and connectors keep their own licenses.

Open Science Desktop is beta research tooling. Treat outputs as drafts: verify numbers, citations, code, and conclusions before publication or decision-making.