Streaming Recommendation (SRec) aims to capture evolving user preferences in the streaming scenarios. Recently, Graph Prompt Learning (GPL) methods have demonstrated their effectiveness and adaptability within SRec. However, existing graph prompt solutions rarely consider the evolution of multi-hop cascading relationships between users and items, which are crucial for modeling the shifts in user preferences. To address this problem, we propose a novel Graph Multi-Level Aware Prompt Learning for Streaming Recommendation, named GPL4SRec. Specifically, a graph encoder is first pre-trained on extensive historical data to capture user long-term preferences. Then, we design three types of prompts, namely node-aware, structure-aware, and layer-aware prompts, which are used to guide the pre-trained encoder to better capture user short-term preferences. This is accomplished by accounting for both the incremental changes in users and items, as well as the cascading evolution in multi-hop relationships. Furthermore, we provide a theoretical analysis showing that our prompt templates are critical to achieving superior performance. Finally, experimental results also prove that our model significantly outperforms the state-of-the-art approaches in SRec.