Neural Graph Memory: A Structured Approach to Long-Term Memory in Multimodal Agents

Long-term memory remains a critical challenge for AI agents operating in open-ended environments, particularly those that require multimodal understanding, temporal continuity, and associative reasoning. I introduce Neural Graph Memory (NGM), a biologically inspired memory architecture designed to address these needs by structuring episodic experiences as dynamic, sparse graphs. Each memory node encodes a modality-aligned latent embedding augmented with temporal and contextual metadata, while edges capture relationships such as temporal adjacency, semantic overlap, and learned associations. This structure enables robust memory retrieval through graph traversal and nearest-neighbor attention, allowing agents to perform episodic recall and cross-modal inference over long time horizons. I present the core architecture, training methodology, and a suite of experiments benchmarking NGM against key-value stores and transformer-based memory systems. The results demonstrate superior long-range retrieval and generalization performance, highlighting the advantages of structured, topologically aware memory over unstructured vector caches. NGM offers a flexible foundation for next-generation memory systems in grounded agentic AI.