open-science

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.

English · 简体中文 · 日本語 · Español · Deutsch · Français · 한국어

🎉 Recognition: Open Science Desktop ranks #1 by scored-task average on ResearchClawBench, an end-to-end benchmark for autonomous scientific research agents (Pass@1 leaderboard, July 9, 2026).

Contents

• ✨ What it does
• 🎬 See it in action
• 🧪 Current capabilities
• 🔌 Skills and connectors
• 📦 Install
• 🚀 Build from source
• 🔒 Safety and privacy
• 🗂️ Repository layout
• 📌 Status
• 🤝 Contributing
• ⚖️ 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.
• 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 complete, traceable analysis. Simulate data, fit a model, save a publication-grade figure, and write a report where every number traces to the code.

Every artifact traces back to its code, inputs, and conversation.

Literature -> verifiable report. Search papers, draft a manuscript rendered as a PDF, and audit citations, unsourced numbers, and figure/code consistency.

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.
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.