The proposed Agent Memory Distillation (AMD) is a training-free framework designed to enhance the performance of small language model agents through hierarchical memory. By transferring structured knowledge from large teacher agents, AMD utilizes three complementary memory types derived from successful trajectories to overcome the limitations of small models. This approach enables small agents to leverage complex workflows without the need for extensive autonomous trajectory generation.