open-science

AIPOCH Open-Science

AI research workbench for reproducible science — open-source, local-first, and model-agnostic.

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.

Completed research sessions can also be exported as portable .science packages for review, handoff, and archiving, with selected conversation branches, file versions, Notebook records, and verification evidence.

💡 AIPOCH Open-Science v0.35.1 released (last updated October 2026). AIPOCH Open-Science v0.35.1 brings GWAS summary statistics discovery, InterProScan sequence submission, and IEDB receptor evidence searches to the connector catalog, refreshes the new-conversation start experience with a common-actions session menu, and adds the Requesty provider. Fixes preserve composer context across session binding, keep imported sessions read-only, protect replay follow-ups, and harden PDF, notebook, and workspace reliability. See the latest release notes for full details.

Table of Contents

• Quick Start
• Product Tour
• AIPOCH Open-Science .science Research Packages
• 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:

Your computer	Choose
macOS 12+ — Apple Silicon (M1 or newer)	The macOS DMG for Apple Silicon / ARM64
macOS 12+ — Intel	The macOS DMG for Intel / x64
Windows x64	The Windows x64 installer (code-signed)
Linux x64	The Linux x64 AppImage or Debian package

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.

Host compatibility, storage, and network checks
Provider, API Key, endpoint, and model validation

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 illustrate each stage rather than one continuous session.

1. Define the research task and evidence

Describe the research question, source paper and datasets, required methods or thresholds, expected outputs, and acceptance criteria. Upload supporting files or reference an existing project artifact with @, so the agent starts from explicit inputs instead of hidden context.

2. Execute with inspectable scientific tools

The agent can combine scientific skills, permissioned research connectors, searches, file operations, and Python or R code in the shared Notebook. Generated figures can be reviewed beside the research summary, while the artifact record exposes captured producer code and execution evi