ai4s-research/open-science

Open Science Desktop — local-first, model-agnostic AI research workbench for macOS, Windows & Linux. Open-source Claude Science desktop alternative built on...

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Summary Information

Updated 55 minutes ago
Added to GitGenius on August 13th, 2026
Created on July 3rd, 2026
Open Issues & Pull Requests: 23 (+0)
GitHub issues: Enabled
Number of forks: 150
Total Stargazers: 1,358 (+0)
Total Subscribers: 6 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 6.7 hours
Mean response time: 18.2 hours
90th percentile: 2.2 days
Tracked items: 70

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 13% of issues opened in the past year have been closed. Three people close 94% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 18
New in 7 days: 10
Closed in 7 days: 7
Avg open age: 12 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 6
Closed in 7 days: 7
Comments in 7 days: 11
Events in 7 days: 17

Top labels

  • enhancement (5)

Detailed Description

Open Science Desktop is a local-first, model-agnostic AI research workbench for macOS, Windows, and Linux.

The tool addresses the need for reproducible, auditable AI-assisted scientific research by integrating agents, notebooks, files, figures, reports, runs, and review into a single desktop workflow. It operates as an open-source alternative to proprietary AI-for-science platforms, built on Tauri for cross-platform desktop delivery, the Model Context Protocol for extensibility, and agent skills for automating research tasks. The local-first architecture means research data and computations remain on the user's machine rather than being sent to external services.

Researchers conducting computational science, data analysis, or experimental work who want to maintain control over their data and avoid vendor lock-in should consider this tool. It suits projects where reproducibility and auditability matter—where the ability to review and verify every step of an AI-assisted analysis is important. The tool is model-agnostic, meaning it can work with different AI models rather than being tied to a single provider, which distinguishes it from proprietary alternatives that depend on specific commercial services.

The project maintains active development across its core functionality, with ongoing work to expand agent capabilities and improve the research workflow integration. The tool supports seven interface languages, indicating sustained effort toward accessibility for international research communities. The codebase is built with TypeScript, Tauri 2, and React, reflecting a modern approach to desktop application development.