Optimagic provides a unified interface to optimization algorithms from various packages while adding convenience features like optimizer histories, error handling, and flexible parameter formats — all in a relatively small code base and without modifying the source code of optimizers. In this talk, we'll build a simplified version of optimagic to demonstrate the core architectural principles that make this possible. By exploring these ideas, we'll show how they can be applied beyond optimization to simplify and enhance other scientific Python projects.