Memory Architectures for Agentic AI: A Comparative Study of Vector and Graph Databases

ABSTRACT

Persistent and scalable memory is the foundational bottleneck preventing Agentic AI from achieving long-term autonomy. This study provides a comprehensive comparative analysis of state-of-the-art memory architectures, evaluating purely semantic vector databases against hybrid vector-graph solutions. Through extensive benchmarking on long-horizon reasoning tasks, the authors demonstrate that while vector databases excel at rapid episodic recall, graph-based memory structures are essential for multi-agent systems to maintain coherent, multi-day contextual awareness and avoid logical contradictions.