openscience

What it is

OpenScience is a research agent with a workbench around it. You describe the task in plain language; it plans, gathers evidence, runs code and experiments, and hands back results you can check. It runs as a desktop app, a browser workspace, or a terminal command, on your machine, against your files.

It is built for the parts of research that are real work but not the idea: pulling and cleaning data, reproducing a claim, sweeping a parameter, drafting the methods section, checking a reference. You keep the idea and the judgment.

• Every step is visible. A turn reads as what happened: what it thought, what it searched, what it ran, what it wrote, then the answer. Nothing runs that you cannot see afterwards.
• Real tools, real files. Shell, Python and R kernels, notebooks, a file system with explicit read and write grants, remote compute when a laptop is not enough.
• Scientific reach. Hundreds of bundled skills across biology, chemistry, physics, ML and data engineering, plus connectors to databases such as ChEMBL, UniProt, PubMed and arXiv.
• Delegation when it helps. The lead agent can hand bounded work to workers, in parallel, and keeps the synthesis and the final say.
• Your model, your terms. Bring your own API keys, sign in to a supported provider, run a local model, or use Ace, the managed pay-as-you-go option.

Install

Desktop app. Download for macOS, Windows or Linux. It updates itself.

Command line and browser workspace.

npm install -g @synsci/openscience
openscience

Or run it without installing:

npx synsci

Or with the standalone installer on macOS and Linux:

curl -fsSL https://openscience.sh/install | bash

Then open Customize → Models and connect a provider, or from the terminal:

openscience keys add        # your own API key
openscience local add       # Ollama, LM Studio, or another local endpoint

The installation guide covers platform details, updates and uninstalling.

First task

Open a project folder and describe the work:

openscience ~/research/my-project

Inspect data/samples.csv for missing values and inconsistent labels.
Keep the original data unchanged. Save a quality report and a plot
in results/, with the code needed to reproduce them.

Start with /plan when you want to agree on the method first. For a single turn from a script or a pipeline:

openscience run "Review the analysis plan in this project"
openscience run --continue "Suggest checks for the assumptions you identified"

Review sources, assumptions, code and outputs before relying on a scientific conclusion. The agent shows you what it did so that you can.

What you can do

Task	What happens
Review literature	Search scientific sources, compare findings, save cited evidence.
Analyze data	Inspect inputs, write and run analysis code, produce figures and reports.
Reproduce experiments	Agree on a claim, prerequisites and budget, then compare measured results.
Run compute	Local kernels for everyday work; Modal for GPUs and long jobs, each dispatch approved before it runs.
Reuse procedures	Browse the bundled skills or add a workflow specific to your lab.
Extend it	MCP servers, custom agents and commands, plugins, or the TypeScript SDK.

A skill describes a procedure; it does not mean every tool or service it references is installed. Check availability in Customize before a substantial task.

NVIDIA BioNeMo

OpenScience ships ten bring-your-own-key adapters for NVIDIA BioNeMo NIM endpoints: Boltz-2, DiffDock, Evo 2, GenMol, MolMIM, MSA Search, OpenFold2, OpenFold3, ProteinMPNN and RFdiffusion. Each has a strict request schema, one approval per dispatch, and hashed artifacts written into the session; they are marked experimental and need your own NVIDIA API key under NVIDIA's service terms. The protein-binder-design skill is adapted from the NVIDIA BioNeMo Agent Toolkit (CC-BY-4.0 skills, Apache-2.0 code), pinned at commit 0e67a61. See Scientific tools and Service credentials.

How it works

your request
  → Research agent plans, then works step by step
      → tools: shell, Python/R kernels, files, search, connectors, compute
      → workers for bounded parallel tasks (explore, execute)
  → answer, with the trace and the files it produced

• Permissions. Choose how much to ask: always, only for risky actions, or full access. Network commands ask once per destination host. Files outside the project are read or written only with an explicit grant.
• Working folder. A conversation works in the project's connected folder; caches and throwaway output stay in a per-session scratch space.
• Publishing stays with you. git push, releases and uploads run from the lead session with this machine's own GitHub and Hugging Face logins; no token is ever asked for in chat.

The capability map, Explore tools and the skills directory list what is available and how to set it up.

Model access

Option	Setup	Cost
Your provider	An API key or a supported sign-in.	Your provider's billing.
Local model	Ollama, LM Studio or any compatible endpoint.	Your hardware.
Ace	Sign in, choose a workspace, fund its wallet.	Provider cost plus a 5.5% fee, per request.

An account is optional for your own keys and local models. Details are in Models, Local models and Pricing.

Documentation

Topic	Guides
First use	Quickstart, Workspace, Workflow cookbook
Research	Literature reviews, Data analysis, Reproduction, Writing
Capabilities	Skills, Databases, Connectors
Control	Permissions, Files, Project instructions, Configuration
Automate	CLI reference, JSON output, SDK and editors
Help	Troubleshooting, FAQ

The documentation is also available as plain text for agents: llms.txt and llms-full.txt.

Repository

backend/cli          The openscience CLI and local server: sessions, tools, providers, skills
frontend/workspace   The browser workspace (SolidJS), embedded into the CLI at build time
frontend/ui          Shared components, themes and icons
frontend/desktop     The Electron shell and its signed self-updater
frontend/docs        The documentation site
tooling/sdk          The TypeScript SDK, generated from the server's OpenAPI contract
tooling/plugin       The plugin runtime
docs/notes           Engineering notes: verification, releases, how to add a skill, tool or connector

bun run setup        # verify Bun, install, embed the workspace UI
bun dev              # run from source
bun run check        # format, typecheck and every unit suite

ARCHITECTURE.md explains how the pieces fit. CONTRIBUTING.md has the development loops, the checks that gate a pull request, and how to add a skill, connector, tool or plugin. AGENTS.md holds the conventions the code follows.

Releases

Stable releases are cut from main by the publish workflow after a full rehearsal at the same commit: packaged end-to-end tests, operating-system smokes and scientific capability canaries on every native platform. GitHub Releases carries the desktop installers, CLI archives and checksums; the changelog records what changed for users.

The desktop app updates itself. For the CLI, run openscience upgrade, or npm install -g @synsci/openscience@latest for an npm installation.

Community and support

• Bugs and feature requests: GitHub Issues. Use the templates; a good report has a reproduction.
• Security: SECURITY.md. Please report vulnerabilities privately.
• Conduct: CODE_OF_CONDUCT.md.
• Billing and account questions: use the account support channel rather than a public issue.

Acknowledgements

OpenScience is inspired by OpenCode by Anomaly and shares its commitment to excellent open-source agents. We want to bring strong, open-source scientific agents to everyone.

Most of the bundled skills come from open collections written by other people:

• Scientific Agent Skills and