Hopf, T. A.; Gazizov, A.; Garcia Busto, S.; Eschbach, E.; Lee, S.; Mirdita, M.; Orenbuch, R.; Belahsen, K.; Ross, D.; Sander, C.; Steinegger, M.; d'Oelsnitz, S.; Marks, D.

Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most - conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where experimental data continuously refines successive design rounds - demand exactly the kind of flexible, composable infrastructure that no single tool provides. We present evedesign, a unified open-source framework that formalizes conditional biosequence design in a method-agnostic way, enabling complex multiobjective workflows combining supervised and unsupervised models from standardized specifications, and built from the outset to support iterative experimental integration. An interactive web interface facilitates end-to-end design for a broad scientific audience at https://evedesign.bio. We demonstrate evedesign's utility in antibody engineering, enzyme design, and natural enzyme discovery, and invite open-source community contributions.