[2603.29493] MemFactory: Unified Inference & Training Framework for Agent Memory

Computer Science > Computation and Language arXiv:2603.29493 (cs) [Submitted on 31 Mar 2026] Title:MemFactory: Unified Inference & Training Framework for Agent Memory Authors:Ziliang Guo, Ziheng Li, Zhiyu Li View a PDF of the paper titled MemFactory: Unified Inference & Training Framework for Agent Memory, by Ziliang Guo and 1 other authors View PDF HTML (experimental) Abstract:Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged as a highly promising research direction. However, existing implementations remain highly fragmented and task-specific, lacking a unified infrastructure to streamline the integration, training, and evaluation of these complex pipelines. To address this gap, we present MemFactory, the first unified, highly modular training and inference framework specifically designed for memory-augmented agents. Inspired by the success of unified fine-tuning frameworks like LLaMA-Factory, MemFactory abstracts the memory lifecycle into atomic, plug-and-play components, enabling researchers to seamlessly construct custom memory agents via a "Lego-like" architecture. Furthermore, the framework natively integrates Group Relative Policy Optimization (GRPO) to fine-tune internal memory management policies driven by multi-dimensional environmental rewards. MemFactory provides out-of-the-...