MemFactory: Unified Inference & Training Framework for Agent Memory

arXiv cs.CL / 4/1/2026

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Key Points

MemFactory is introduced as a unified, modular training and inference framework tailored for memory-augmented LLM agents, aiming to reduce fragmented, task-specific implementations of memory pipelines.
The framework abstracts the memory lifecycle into “Lego-like” plug-and-play components so researchers can more easily build custom memory agents.
It includes native integration of Group Relative Policy Optimization (GRPO) to fine-tune internal memory management policies using multi-dimensional environmental rewards.
MemFactory is validated on the open-source MemAgent architecture, showing consistent performance improvements over base models on both in-domain and out-of-distribution evaluations, with gains up to 14.8%.
The authors position MemFactory as a standardized infrastructure that lowers the barrier to entry for future research and innovation in long-term, memory-driven AI agents.

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-box support for recent cutting-edge paradigms, including Memory-R1, RMM, and MemAgent. We empirically validate MemFactory on the open-source MemAgent architecture using its publicly available training and evaluation data. Across both in-domain and out-of-distribution evaluation sets, MemFactory consistently improves performance over the corresponding base models, with relative gains of up to 14.8%. By providing a standardized, extensible, and easy-to-use infrastructure, MemFactory significantly lowers the barrier to entry, paving the way for future innovations in memory-driven AI agents.