MemOrb: A Plug-and-Play Verbal-Reinforcement Memory Layer for E-Commerce Customer Service

Large Language Model-based agents(LLM-based agents) are increasingly deployed in customer service, yet they often forget across sessions, repeat errors, and lack mechanisms for continual self-improvement. This makes them unreliable in dynamic settings where stability and consistency are critical. To address the limitations of existing approaches, we propose MemOrb, a lightweight and plug-and-play verbal reinforcement memory layer that distills multi-turn interactions into compact strategy reflections. These reflections are stored in a shared memory bank and retrieved to guide decision-making, without requiring any fine-tuning. Experiments show that MemOrb significantly improves both success rate and stability, achieving up to a 63 percentage-point gain in multi-turn success rate and delivering more consistent performance across repeated trials. Our results demonstrate that structured reflection is a powerful mechanism for enhancing long-term reliability of frozen LLM agents in customer service scenarios. Large Language Model-based agents (LLM-based agents) are increasingly adopted in large-scale customer service systems, where they act as interactive assistants for diverse users (Brown et al., 2020). Despite their rapid deployment, these agents face persistent challenges: they often lose critical information across sessions, repeat errors without systematic correction, and struggle to adapt to rapidly changing product catalogs. Such limitations undermine their reliability in dynamic environments such as e-commerce. Existing memory solutions typically rely on short-term caching or user-specific profiles (Chhikara et al., 2025; Zhong et al., 2023). Consequently, purely per-user or short-horizon memories are insufficient for robust long-term improvement.