Scientific teams often struggle with tools that lock them into a single vendor, force data to leave their secure environment, and make it hard to switch models or reproduce results. OpenScience solves these problems by providing an open‑source AI workbench that runs on your own infrastructure, lets you plug in any language model you already have access to, and keeps every step of the research loop—literature search, hypothesis generation, coding, experimentation, analysis, and write‑up—inside a single browser‑based session. Because the agent routes each request to the model you select, you can compare Claude, GPT, Gemini, a local fine‑tune, or any other provider without rewriting code or changing your workflow. All skills, databases, and agents are editable under the Apache 2.0 license, so you can inspect, modify, or extend them to fit your domain. The tool ships with over 250 ready‑to‑use skills covering machine‑learning training, cheminformatics, molecular biology, and more, and it connects directly to UniProt, PDB, ChEMBL, arXiv and dozens of other scientific repositories as callable tools. Your API keys never leave your machine, and session data, provenance, and artifacts are stored locally on disk, giving you full control over privacy and reproducibility. If you need isolation, run OpenScience inside a container or VM. The project is young, so expect occasional rough edges, but the open model‑agnostic design removes vendor lock‑in, keeps data local, and lets you choose the best model for each task while managing your own costs.
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