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.

News

• 2026-08-17 — 🖥️ Runs without a screen. osd server starts the whole workbench — workspace, agent runtime, and the same web UI — on a machine with no display, and osd session send … --wait drives it from a script or another agent. One tarball, no installer. (unreleased)
• 2026-08-13 — 🔌 Speaks the Agent Client Protocol, both directions. Drive Codex, Gemini CLI, Claude Code, or any other ACP agent from inside this app — with its own models, history, and your MCP connectors — or drive Open Science itself from Zed, JetBrains, or Neovim. (v0.4.0)
• 2026-08-01 — 🗂️ Projects, memory, and full history. Group sessions into named projects (import an existing repo in place, no copying), give the agent persistent global and project memory, and reach every past conversation through a searchable history with archive, restore, and export. (v0.3.1)
• 2026-07-24 — 🪟 Split-pane tiling. Tile sessions side by side, drag panes to re-dock them, keep several independent Screens, and run a different model in each pane. (v0.3.0)
• 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).

Contents

• ✨ What it does
• 🎬 See it in action
• 🧪 Current capabilities
• 🔌 Skills and connectors
• 📦 Install
• 🖥️ Headless & CLI (osd)
• 🚀 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.
• Plan before it acts — /plan lays out an execution plan before touching a file, and /goal fixes the objective, constraints, and acceptance criteria the agent then works toward.
• Built for long projects — named projects group their sessions, two layers of persistent memory (global and per-project) carry what matters between them, and a long conversation compacts itself as it approaches the model's context window.
• Work several threads at once — tile panes side by side, keep independent Screens, and give each pane its own model.
• 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.

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: