Additionally, the presence of dummy atoms necessitates the addition of constraints, as previously described. However, constrained calculations often fail to converge and are an impediment to automation. To address this challenge, we have implemented constrained saddle point optimization using sequential quadratic programming (SQP), resulting in reliable and efficient constrained optimization trajectories. In the following, we present our methodology to largely overcome the problems mentioned above. In section 2, we describe our saddle point optimization algorithm. This algorithm features the ability to automatically construct a basis of redundant internal coordinates from the Cartesian molecular structure, including the addition of dummy atoms when necessary. Furthermore, we have extended several methods to a basis of redundant internal coordinates, including iterative diagonalization of the Hessian, SQP-based constrained optimization, and restricted-step partitioned rational function optimization (RS-PRFO) for saddle point structures. In section 3 we introduce a new benchmark, demonstrate the performance of our algorithm, and give guidance about its use. We conclude in section 4.