We present an approach that generates context-aware, optimized libraries of algorithms and data structures. The search space contains all combinations of implementation variants of algorithms and data structures including dynamically switching and converting between them. Based on profiling, the best implementation for a certain context is precomputed at deployment time and selected at runtime. In our experiments, the profile-guided composition outperforms the individual variants in almost all cases.