摘要: The integration of structured, long-term memory is critical for the development of autonomous Large Language Model (LLM) agents. Recent advancements, such as the Agentic Memory (A-MEM) framework, have achieved significant progress by dynamically constructing and evolving knowledge graphs. However, existing architectures inherently operate as synchronous, "append-and-evolve-all" systems. Processing every user utterance through a computationally expensive O(N²) memory evolution pipeline introduces severe write-latency, unbounded API token costs, and catastrophic context window pollution caused by conversational noise. To address this scalability bottleneck, we introduce D-MEM (Dopamine-Gated Agentic Memory), a biologically inspired architecture that decouples short-term interaction from long-term cognitive restructuring. Drawing inspiration from the Dopamine-driven Reward Prediction Error (RPE) gating mechanism in the mammalian Ventral Tegmental Area (VTA), D-MEM implements a highly efficient Fast/Slow routing system. We introduce a lightweight Critic Router that continuously evaluates the Information Entropy (Surprise) and Long-term Utility of incoming stimuli. Routine inputs with low RPE are either bypassed entirely or cached in an O(1) fast-access buffer, preserving computational resources. Conversely, inputs generating a high RPE—such as factual contradictions or paradigm-shifting preference changes—trigger a "dopamine release" that activates the slow, O(N) deep memory evolution pipeline, actively reshaping the agent's global knowledge graph. To enable rigorous evaluation under realistic conditions, we further introduce the LoCoMo-Noise benchmark, which systematically injects controlled conversational noise into long-term dialogue sessions to simulate real-world interaction dynamics. Extensive evaluations demonstrate that D-MEM reduces API token consumption by over 80% and eliminates O(N²) write-latency bottlenecks, all while strictly outperforming synchronous baselines in complex multi-hop reasoning and adversarial resilience. By selectively gating cognitive restructuring and leveraging zero-cost retrieval augmentations, D-MEM provides a highly scalable and cost-efficient foundation for lifelong agentic memory. To support reproducibility, we open-source our implementation at https://github.com/london-and-tequila/dmem.