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PromptX: A Cognitive Agent Platform with Long-term Memory

  • Binhao Wang
  • , Jianglin Huang
  • , Xiao Hu
  • , Shan Jiang
  • , Maolin Wang*
  • , Ching Ho Yang
  • , Jian Jiang
  • , Junhao Ye
  • , Yaozu Cen
  • , Rui Zeng
  • , Yingtong Zhou
  • , Yingjie Luo
  • , Guanjie Wu
  • , Wangzhong Xu
  • , Feiyu Zhou
  • , Xiangyu Zhao
  • *Corresponding author for this work
  • City University of Hong Kong
  • Deepractice AI Limited
  • Ltd.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

While large language models (LLMs) demonstrate impressive contextual understanding, their limitations in long-term memory and personalized reasoning constrain their practical impact in industrial settings. To address these gaps, we introduce PromptX, a cognitive platform that enables AI agents to construct structured memory and develop their reasoning over time. PromptX integrates three core technologies: (1) A new prompt markup language to define agent personas and memory organization; (2) Engram-based activation-diffusion memory networks that unify raw experiences with conceptual sequences, enabling associative retrieval through graph network propagation; (3) a protocol-driven orchestration layer enabling dynamic tool discovery and coordination, inspired by HATEOAS principles from web-engineering. During five months of real-world deployment across a range of 15+ enterprises in 6 industries, PromptX has been validated in multiple industry domains (e.g., software engineering, education, healthcare), accumulating 50K+ downloads and 3K+ GitHub stars and evidencing practical feasibility and commercial value in production workflows. Our demo and initial product are available at https://promptx.deepractice.ai/. The source code and documentation are available online at https://github.com/Deepractice/PromptX. The supplementary materials are also available online.

Original languageEnglish
Title of host publicationWWW Companion 2026 - Companion Proceedings of the ACM Web Conference 2026
PublisherAssociation for Computing Machinery, Inc
Pages89-92
Number of pages4
ISBN (Electronic)9798400723087
DOIs
StatePublished - 28 May 2026
Externally publishedYes
Event35th ACM Web Conference, WWW Companion 2026 - Dubai, United Arab Emirates
Duration: 29 Jun 20263 Jul 2026

Publication series

NameWWW Companion 2026 - Companion Proceedings of the ACM Web Conference 2026

Conference

Conference35th ACM Web Conference, WWW Companion 2026
Country/TerritoryUnited Arab Emirates
CityDubai
Period29/06/263/07/26

Keywords

  • agent context protocol
  • ai agents
  • memory networks

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