Run an AI research agent that writes and executes Python or R code for real data analysis and simulation.
Use bundled scientific skills for tasks like protein structure prediction or single-cell biology analysis.
Connect to a low-cost model provider like Doubao instead of an expensive frontier model API.
Dispatch heavier computation jobs to a remote GPU machine over SSH when configured.
| pku-yuangroup/openai4s | aripath/arimando | bingook/bingo | |
|---|---|---|---|
| Stars | 127 | 128 | 128 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | moderate |
| Complexity | 4/5 | 3/5 | 4/5 |
| Audience | researcher | general | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires an AI model provider account or key, a one-click Mac app bundles Python and core libraries.
OpenAI4S is an open source AI research agent built for scientific work, positioned as a much cheaper alternative to Claude Science. It can run on Doubao, a low-cost Chinese AI model service, for as little as about 9.9 yuan, roughly one and a half US dollars, per month, though it also supports ChatGPT, Claude, Gemini, and several other model providers through one shared interface. The project combines two ways of working. Structured tool calls handle predictable steps like permissions, approvals, and connecting to outside services. Separately, the agent can write and run Python or R code in a persistent session, which is where the actual scientific computation, data analysis, and simulation happen. The idea is that some tasks are better handled as clean, auditable steps, while open-ended analysis is better handled as real code that can loop, branch, and use libraries, rather than being forced through many small back and forth tool calls. It ships with a web based workbench showing live output, saved results, and a history of what the agent did, plus a notebook interface that is read-only by default unless a developer explicitly turns on manual editing. It includes 34 built in skills, many of them scientific tools for tasks like protein structure prediction and single-cell biology analysis, alongside general research workflow helpers. For heavier computation, it can dispatch jobs to remote GPU machines over SSH or through an NVIDIA integration, when one is configured. Security features include restricted environments for anything the agent runs locally, required approval steps, and operating system level sandboxing on Mac and Linux. A one click Mac app is available for Apple Silicon, bundling Python and the core scientific libraries, and it can also be installed as a Python package. The project is aimed at scientists and researchers who want an AI assistant that can genuinely run analysis code, not just describe what to do, and it is released under the MIT license.
OpenAI4S is a low-cost open source AI research agent that mixes structured tool calls with live Python and R code execution to run real scientific analysis.
Mainly Python. The stack also includes Python, R, WebSocket.
Permissive open-source license, use freely including for commercial purposes, with attribution.
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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