MAHL is a hierarchical LLM-based chiplet design generation framework featuring six agents that enable AI algorithm-to-hardware mapping. It includes hierarchical explanation generation, search-augmented code generation, diverseflow-based verification, and multi-granularity design space exploration. MAHL efficiently generates chiplet designs by optimizing power, performance, and area (PPA). Experimental results show that MAHL significantly improves the generation accuracy of simple RTL designs, as well as the generation accuracy of real-world chiplet designs from 0 to 0.72 at Pass@5 compared to conventional LLM. Furthermore, MAHL achieves comparable or better PPA results under specific optimization objectives compared to state-of-the-art CLARIE (expert-based).