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An open AI workbench for scientists. Your research partner for rigorous science.

An open-source, local-first, model-agnostic, reproducible AI research workbench — an open alternative to Claude Science and similar AI-for-science products. Not a chat box: a workbench that ties literature, code, figures, reports, and review into one auditable, reproducible workflow.

English · 中文

Contents

✨ Why it is different

🎬 See it in action

🧭 How it works

🧪 What's inside

🔌 Skills & connectors

🚀 Getting started

💬 Using it

🔒 Safety & privacy

🗂️ Repository layout

📌 Status & roadmap

🤝 Contributing

⚖️ License

🙏 Acknowledgments

✨ Why it is different

A workbench, not a chat box — plan → approve → execute → artifacts → review.

Traceable artifacts, not just text — every figure, table, and report links back to its code, data, environment, and the conversation that produced it.

Local-first — your data and compute stay on your machine; the app states plainly what (if anything) leaves it.

Model-agnostic — BYOK via OpenRouter, OpenAI-compatible, Anthropic, or local models; a free out-of-the-box model works with zero setup.

Reproducible — code, data, figures, reports, logs, and provenance.jsonl are kept, and every artifact version is recoverable.

Multi-domain — starting with AI4S, expanding to materials, chemistry, biology, medicine, and engineering.

🎬 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 — one click on a figure reveals the script that made it and the versions behind it.

Literature → verifiable report. Search papers, draft a manuscript rendered as a PDF, and audit it for citations, unsourced numbers, and figure↔code consistency.

More screenshots — notebooks, experiment sweeps, and the skills library

Conversation-first notebooks. The agent drives a real Jupyter kernel; cells and figures appear live beside the chat.

Run and track experiments. Sweep parameters, keep a persistent kernel, and surface results as traceable artifacts.

Pluggable scientific skills. Bundled skills for literature, experiments, figures, and integrity — plus one-click open-source connectors and bring-your-own.

🧭 How it works

your prompt │ ▼ [ plan ] ──▶ [ approve ] ──▶ [ execute ] local Python / Jupyter kernel, ▲ ▲ │ shell, MCP tools — on your machine │ │ ▼ │ you answer [ artifacts ] ──▶ figures · tables · notebooks · reports │ questions / │ each linked to code + data + env │ permissions ▼ └────────────────────── [ review ] citation audit · untraceable-number flags · figure ↔ code consistency

Everything runs through the bundled OpenCode agent runtime (a single-binary sidecar, pinned and managed by the app). The UI never talks to a model directly — it goes through a thin SDK, so skills, MCP servers, and model providers stay pluggable.

🧪 What's inside

Capability What it does

Full workflow One prompt drives data → code → figure → report → a reproducible record. One-click starters get you going.

Local compute A persistent local Python kernel and an optional isolated Jupyter environment (provisioned with a bundled uv — your system Python is untouched).

Artifact provenance Every agent write appends a version record to .openscience/provenance.jsonl; a History panel shows each version's code, model, and originating conversation.

Traceability reviewer Resolves citations (Crossref / arXiv / PubMed), flags numbers with no traceable source, and checks figures against the code that made them.

Native viewers Inline PDF, tables, images, HTML, and Office documents; matplotlib/plotly figures render publication-grade by default.

One design system A single validated chart palette shared by native UI and agent-generated matplotlib figures — correct in light and dark.

Keyboard-first A command palette (⌘K) reaches every primary action.

Model choice ~150 providers via OpenCode; BYOK, OpenAI/Anthropic-compatible endpoints, local Ollama, or the free built-in model.

🔌 Skills & connectors

Bundled scientific skills (agent playbooks the app ships and keeps in sync):

research-explorer, literature-survey, experiment-suite, paper-writer, mindmap-render, integrity-auditor, ai4s-agent — the ai4s-skills pack.

traceability-review and publication-figures — first-party skills for verifiable review and on-system figures.

One-click open-source connectors (provisioned into an isolated env via the bundled uv):

Literature — arXiv, PubMed, Crossref, Semantic Scholar, bioRxiv/medRxiv (paper-search-mcp).

Biomedical — PubMed, ClinicalTrials.gov, genomic variants (biomcp).

Bring your own — any MCP server (local command or remote URL) or skill; see docs/CONNECT_YOUR_TOOLS.md.

🚀 Getting started

Prerequisites: Node.js ≥ 20, pnpm 9, and the Rust toolchain (for Tauri). macOS or Windows.

Build the desktop app from source:

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

Fetch the pinned sidecars and bundled skills (kept out of git):

bash scripts/dev/fetch-opencode.sh # the OpenCode agent runtime bash scripts/dev/fetch-uv.sh # uv, for isolated Python/Jupyter envs bash scripts/dev/fetch-skills.sh # the ai4s-skills pack

Develop, or build an installer (.dmg / .app / NSIS / .msi):

pnpm --filter @ai4s/desktop tauri dev pnpm --filter @ai4s/desktop tauri build

On first launch the app starts the bundled runtime automatically and works out of the box with a free model — pick your own provider anytime in Settings.

Common checks:

pnpm test # unit tests (Vitest) pnpm typecheck # TypeScript pnpm lint # ESLint

💬 Using it

Start from a workflow — the empty session offers one-click starters (run a demo analysis, analyze your data, audit a report), or just type what you want.

Answer when asked — when the agent needs a decision it asks inline with options; when it wants to run a command or write a file it asks permission (allow once / always / reject). Manual approval is the default.

Inspect any artifact — click a figure, report, or notebook to open it in the right pane; open its History to see every version and jump back to the conversation that produced it.

Reach anything with ⌘K — the command palette runs every primary action.

Add data — drop files into the workspace (~/Documents/OpenScience) or attach them in the composer; the agent reads and writes there.

🔒 Safety & privacy

Local by default — your workspace files, raw data, code execution, session history, and provenance stay on your machine. Settings shows, in plain language, exactly what is sent to your chosen model provider (your messages and the file / command output the agent reads for the task) and what never leaves.

Human-in-the-loop — command execution, file deletion, dependency installs, and remote connections require approval; the app ships in manual-approval mode.

Credentials — provider keys live in an app-private file, never in the workspace, provenance, logs, or exports.

🗂️ Repository layout

Path Purpose

apps/desktop/ Tauri 2 + React + TypeScript + Vite desktop shell (src/ frontend, src-tauri/ Rust)

packages/shared/ Shared domain types and the chart design system

packages/sdk/ OpenCodeClient SDK wrapper (isolates the UI from the runtime)

packages/ui/ Shared UI component library

runtime/skills/core/ First-party scientific skills (traceability-review, publication-figures)

runtime/skills/external/ The bundled ai4s-skills pack (fetched by script)

runtime/ manager, opencode-profile, mcp configuration

docs/ PRD.md, TECHNICAL_DESIGN.md, REQUIREMENTS.md, CONNECT_YOUR_TOOLS.md

examples/bci-trends/ A built-in end-to-end demo project workspace

scripts/ release/ and dev/ scripts (sidecar + skills fetchers)

📌 Status & roadmap

v0.1, in active development — a working desktop MVP on macOS. See PROGRESS.md for the log and docs/REQUIREMENTS.md / docs/PRD.md for the full spec.

✅ End-to-end workflow, artifact provenance, traceability reviewer, local Python kernel + Jupyter, one-click science connectors, plain-language data-flow, chart design system, command palette.

🚧 Next: domain renderers (protein / chemical structures), an R kernel, a Windows installer, larger multi-file projects, and HPC / Slurm compute.

🤝 Contributing

Issues and PRs are welcome. Keep changes minimal and verifiable, follow the design principles in AGENTS.md (simple · explicit · clear · complete), and run pnpm test, pnpm typecheck, and pnpm lint before opening a PR.

⚖️ License

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

This is beta research tooling. Outputs are drafts — verify numbers, citations, and claims, and have a domain expert review before any submission or decision.

🙏 Acknowledgments

Built on Tauri, OpenCode, and the ai4s-skills pack. Thanks to linux.do — a vibrant tech community where this project is shared and discussed.

About

Open Science — an open AI workbench for scientists. Open-source alternative to Claude Science: local-first, model-agnostic, reproducible AI research desktop (macOS & Windows), built on Tauri + MCP + agent skills.

Topics

desktop-app

reproducible-research

mcp

open-science

scientific-research

tauri

local-first

ai-agent

research-tools

llm

ai-for-science

ai-scientist

ai4s

claude-science

claude-science-alternative

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