Open Science

Open Science is an open-source, local-first, model-agnostic, self-hosted AI research workbench for reproducible scientific discovery. Built for researchers, it runs on your own computer (macOS, Windows, Linux). Create a project, describe a task in plain language, and let the AI agent read files, run Python and R code, search the web, call scientific data connectors, and return reproducible reports, tables, and figures linked to an inspectable activity history in one workspace.

Open Science supports computational and data-intensive research across disciplines, including machine learning, statistics, life sciences, chemistry, materials science, physics and environmental science. It supports the research process from literature review and hypothesis development to code execution, data analysis, simulation, visualization, and the production of traceable research outputs.

💡 Open Science v0.13.1 released (last updated August 11, 2026). Highlights include local folder access via @path mentions, bounded REPL inference, project-scoped frame reads, artifact lineage access, connector runtime status and recovery, refined session sidebar rows, notebook artifact read access, and project actions with agent context — alongside hardened database migrations, workspace interaction fixes, Windows upload repair, and connector login recovery. See the latest release notes for the full changelog.

Table of Contents

• Quick Start
• Product Tour
• Why Open Science
• Design Principles
• Core Capabilities
• Model Providers
• Data, Permissions, and Trust
• Project Status
• Development & Packaging
• Roadmap
• Relationship to the AIPOCH Ecosystem
• What This Is Not
• Frequently Asked Questions
• Get Involved
• License

🚀 Quick Start

1. Download the app

Open the latest release, expand Assets, and choose the installer for your computer:

Review the assets and verification information published on the release page. See Verifying your download before installation if you need to validate a package.

If macOS or Windows shows an unidentified-developer or unknown-publisher warning, verify that the package came from the official Releases page before continuing.

2. Complete first-time setup

The first launch has five guided steps:

1. Environment checks compatibility, app storage, secure credential storage, and network access.
2. Agent runtime selects and prepares Claude Code, OpenCode, or Codex. App-managed runtimes can be installed without requiring Node.js, npm, or an administrator password.
3. Model provider connects and tests the model you want to use. Choose a built-in provider, a custom gateway, or an existing Claude or Codex subscription login.
4. Notebook runtime optionally prepares app-managed Python and R environments or enables detected and manually registered interpreters for either language.
5. Data location chooses where large artifacts, notebooks, uploads, and environments are stored.

Notebook execution is optional. Every required environment and agent-runtime check must pass before Continue becomes available, and the model connection must pass before setup finishes. Notebook and data-location settings can keep their defaults and be changed later in Settings.

3. Start a research project

1. Click New project and give the project a stable research name and optional description.
2. Open a session and describe the goal, input data, constraints, desired outputs, and how the result should be checked.
3. Attach source files, select a verified model, and choose an approval mode.
4. Send the task. Inspect the agent's tool activity, approve sensitive actions, and open generated artifacts in the preview panel.
5. To explore a different direction, edit an earlier user message and resend it on a new branch; use the message revision controls to return to either path.
6. Open an artifact's Provenance view to inspect its versions and the available evidence behind the selected result.
7. Continue the work in later sessions. Use @ to reference an existing project file and / to explicitly select an enabled skill.

Screenshots in this README illustrate the workflow. Labels, catalogs, and other interface details may differ from the version you install.

Product Tour

Open Science organizes research into projects and sessions so that every result can stay connected to the evidence that produced it. The sections below walk through the workspace, artifact provenance, previews, scientific skills, and data connectors.

One workspace from task to traceable artifacts

Projects keep related sessions, uploads, generated files, and preview state together. The conversation records the agent's answer and the commands, file reads, edits, searches, and connector calls that produced it. Each generated artifact is stored as an immutable, checksummed version. Its Provenance view exposes the evidence Open Science could verify at creation time: producer code and execution history, referenced inputs, an observed environment inventory, the producing conversation branch, and any version-scoped reviewer findings. Missing evidence is shown as unavailable instead of being guessed.

Generated reports, figures, and tables remain attached to the session and are also collected in the project file library. Preview tabs keep the active result visible as the panel changes size, and long names preserve their identifying suffix and extension. Open Science previews common scientific data, PDFs, Office documents (DOCX, XLSX, PPTX), images (with zoom and pan), source code with syntax highlighting, molecular structures and reactions, and Notebook history. Preview limits do not truncate the underlying file—the full artifact stays available to the agent and external tools. Use Cmd/Ctrl+F to search transcripts, Notebook output, and rendered pages across the workspace, or Cmd/Ctrl+K to open the project-scoped command palette. A dark mode rounds out the workspace: toggle the theme in Settings → General and the whole shell, transcript, and renderer palette switch without a flash.

Branch a conversation without losing the original

Edit a completed user message to resend a revised prompt from that point. Open Science creates a new message branch instead of deleting the turns that followed, and revision controls let you move between the original and alternative paths. Branch selection, tool activity, attachments, and generated artifacts persist across project switches and restarts. Provenance remains tied to the exact branch that produced each artifact version, so exploring a different hypothesis does not blur the record of the earlier result.

Scientific skills and data connectors

Open Science includes a growing catalog of 18 featured, file-based research skills: AlphaFold2, Boltz, Borzoi, Chai-1, DiffDock, Environment & Packages, ESM-2, ESMFold2, Evo 2,