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AIPOCH Open-Science is an AI research workbench for scientists and researchers, developed by AIPOCH with an open-source, local-first, model-agnostic approach. It enables reproducible, inspectable research with scientific AI agents, Python and R execution, scientific data connectors, and cross-platform support for macOS, Windows, and Linux. Create a project, describe your research goal in plain language, and let the agents read files, search the web, run code, query scientific data sources, and produce reports, tables, and figures with traceable provenance—all in one workspace.

AIPOCH 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.

💡 AIPOCH Open-Science v0.33.3 released (last updated September 2026). AIPOCH Open-Science v0.33.3 upgrades document preview with paginated Office reading and search plus paged PowerPoint review, adds Enrichr gene-set enrichment tools to the genes connector, and brings a compact library preview to the workspace. Windows signing now covers every bundled executable, Literature screening gains an abandoned-evaluation action, and session packages can transfer at adaptive speeds. See the latest release notes for full details.

Table of Contents

• Quick Start
• Product Tour
• Benchmark Performance
• Core Capabilities
• Model Providers
• Data, Permissions, and Trust
• Development & Packaging
• Frequently Asked Questions
• Get Involved
• License
• Star History

🚀 Quick Start

1. Download the app

Open the official download page and choose the installer for your computer:

Download from the official download page; see download verification if needed.

On macOS, you can also install with Homebrew:

brew install --cask open-science

Windows reinstalls preserve research data. For a full cleanup, see the data reset tool, which permanently deletes local data after confirmation.

2. Complete first-time setup

Follow the setup wizard: Environment → Data location → Agent runtime → Model provider → Notebook runtime.

Complete the required environment and agent-runtime checks and test your model connection. Python/R Notebook setup is optional; Notebook and data-location settings can be changed later.

3. Start a research project

1. Click New project, open a session, and describe your research goal, inputs, and expected outputs.
2. Attach files, select a model and approval mode, then send the task. Use @ to reference project files or / to choose a skill.
3. Review tool activity and any approval requests, preview the results, and check their available evidence in Provenance.

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

Product Tour

From a research request to a traceable result

Consider a representative bioinformatics task: reproduce a published differential-expression analysis, compare the regenerated results with the paper, and deliver the report, tables, and figures needed for review. The screenshots below are representative views from documented AIPOCH Open-Science workflows; they illustra