@inproceedings{zhao-etal-2026-memtools,
title = "{M}em{T}ools: A Unified Research Framework for Interoperable Agent Memory",
author = "Zhao, Chengfeng and
Chen, Jinhui and
Liang, Sirui and
He, Shizhu and
Wang, Yequan and
Zhao, Jun and
Liu, Kang",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.41/",
pages = "424--434",
ISBN = "979-8-89176-461-3",
abstract = "While memory systems are essential for agent architectures, pervasive architectural fragmentation restricts systematic research. Existing implementations typically couple different stages of the memory lifecycle, entangle evaluation logic with specific datasets, and provide limited support for the management of heterogeneous memory types. We introduce MemTools, an interoperability research framework that decouples memory system components from their underlying deployment environments. MemTools standardizes the memory lifecycle through declarative data contracts, enabling the interchangeable assembly of components across different systems. It orthogonally separates benchmark datasets from execution protocols to facilitate controlled assessments. Furthermore, MemTools provides a unified computational interface for coordinating symbolic, neural, and multimodal memory representations within a shared runtime. Empirical evaluations on cross-system component integration, evaluation protocol reconfiguration, and heterogeneous memory coordination demonstrate that MemTools enables systematic isolation and analysis of memory design variables. These findings suggest that MemTools provides a practical and extensible infrastructure for advancing principled research on agent memory."
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%0 Conference Proceedings
%T MemTools: A Unified Research Framework for Interoperable Agent Memory
%A Zhao, Chengfeng
%A Chen, Jinhui
%A Liang, Sirui
%A He, Shizhu
%A Wang, Yequan
%A Zhao, Jun
%A Liu, Kang
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F zhao-etal-2026-memtools
%X While memory systems are essential for agent architectures, pervasive architectural fragmentation restricts systematic research. Existing implementations typically couple different stages of the memory lifecycle, entangle evaluation logic with specific datasets, and provide limited support for the management of heterogeneous memory types. We introduce MemTools, an interoperability research framework that decouples memory system components from their underlying deployment environments. MemTools standardizes the memory lifecycle through declarative data contracts, enabling the interchangeable assembly of components across different systems. It orthogonally separates benchmark datasets from execution protocols to facilitate controlled assessments. Furthermore, MemTools provides a unified computational interface for coordinating symbolic, neural, and multimodal memory representations within a shared runtime. Empirical evaluations on cross-system component integration, evaluation protocol reconfiguration, and heterogeneous memory coordination demonstrate that MemTools enables systematic isolation and analysis of memory design variables. These findings suggest that MemTools provides a practical and extensible infrastructure for advancing principled research on agent memory.
%U https://aclanthology.org/2026.emnlp-demos.41/
%P 424-434
Markdown (Informal)
[MemTools: A Unified Research Framework for Interoperable Agent Memory](https://aclanthology.org/2026.emnlp-demos.41/) (Zhao et al., EMNLP 2026)
ACL
- Chengfeng Zhao, Jinhui Chen, Sirui Liang, Shizhu He, Yequan Wang, Jun Zhao, and Kang Liu. 2026. MemTools: A Unified Research Framework for Interoperable Agent Memory. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 424–434, Budapest, Hungary. Association for Computational Linguistics.