New preprint: BioEngine — running bioimage AI through agent-readable interfaces

We’re excited to share a new preprint describing BioEngine — the platform behind the “test run” feature on the BioImage Model Zoo and a big step toward making AI for bioimage analysis genuinely usable.

Read it on bioRxiv: BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces (Mechtel, Dettner Källander, Cheng, Zhang, the AI4Life Horizon Europe Program Consortium, and Ouyang).

What BioEngine does

The community has produced an enormous number of deep-learning models for microscopy — but actually running the right one, at scale, has remained hard for the biologists who need them. BioEngine is our answer: an agent-first infrastructure platform that connects browsers, microscopes, and AI agents to GPU compute, so a scientist can describe a goal in plain language and have the right model found, run, and adapted for them — no programming required.

A few ideas we’re particularly happy with:

• Agent-readable interfaces. Models and services expose themselves in a way that both people and AI agents (like Agent-Lens) can discover and operate — turning a model zoo into something an autonomous system can actually use.
• Scales without rewrites. Built on Hypha for serverless connectivity and Ray for distributed orchestration, BioEngine runs the same way from a single laptop to multi-node GPU clusters.
• FAIR by design. It integrates with the BioImage Model Zoo so the models you run are standardized, validated, and reusable across tools.

This work grew out of the AI4Life project and is part of the lab’s broader push to build the AI infrastructure for data-driven cell biology — the same backbone our Alpha Cell work relies on. Huge thanks to the team and collaborators who made it happen.

Want to try it? Explore the BioImage Model Zoo or read the BioEngine project page.

Competing interests: W. Ouyang is a co-founder of Amun AI AB.