Graph-based data structures are prevalent in various real-world applications, for example, protein molecules and social connection networks, necessitating effective representation learning techniques. Graph Neural Networks (GNNs) have demonstrated significant advancements in tasks like node classification and social network analysis through recursive information aggregation. However, current GNN approaches are predominantly static, lacking adaptability to specific graph structures. Inspired by Neural Architecture Search (NAS) in designing dataset-specific architectures, we propose Context-Aware Structure Adaptive Graph Neural Networks (CAS-GNN). This framework is capable of automatically selecting the appropriate aggregator for each node which is determined by both node attributes and local contextual information. The selection is formulated as the Markov Decision Process (MDP) optimized via Deep-Q-Network (DQN) training. Our contributions include a flexible framework incorporating various aggregators for individual nodes based on their attributes and local context, improved performance through node-specific aggregator selection, and extensive experimental validation demonstrating the effectiveness of CAS-GNN on multiple real-world datasets.