The open-source AI workbench for scientific research.

Give it a goal. It reads the literature, writes and runs the code, runs the experiments, and writes up what it found, with every step on the record.

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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

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

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

Documentation