A-MEM: Agentic Memory for LLM Agents

• URL: http://arxiv.org/abs/2502.12110v5
• Date: Fri, 18 Apr 2025 17:26:57 GMT
• Title: A-MEM: Agentic Memory for LLM Agents
• Authors: Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, Yongfeng Zhang,
• Abstract summary: Large language model (LLM) agents require memory systems to leverage historical experiences.<n>Current memory systems enable basic storage and retrieval but lack sophisticated memory organization.<n>This paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way.
• Score: 42.50876509391843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current memory systems enable basic storage and retrieval but lack sophisticated memory organization, despite recent attempts to incorporate graph databases. Moreover, these systems' fixed operations and structures limit their adaptability across diverse tasks. To address this limitation, this paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way. Following the basic principles of the Zettelkasten method, we designed our memory system to create interconnected knowledge networks through dynamic indexing and linking. When a new memory is added, we generate a comprehensive note containing multiple structured attributes, including contextual descriptions, keywords, and tags. The system then analyzes historical memories to identify relevant connections, establishing links where meaningful similarities exist. Additionally, this process enables memory evolution - as new memories are integrated, they can trigger updates to the contextual representations and attributes of existing historical memories, allowing the memory network to continuously refine its understanding. Our approach combines the structured organization principles of Zettelkasten with the flexibility of agent-driven decision making, allowing for more adaptive and context-aware memory management. Empirical experiments on six foundation models show superior improvement against existing SOTA baselines. The source code for evaluating performance is available at https://github.com/WujiangXu/AgenticMemory, while the source code of agentic memory system is available at https://github.com/agiresearch/A-mem.

Related papers

• Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions [55.19217798774033]
  Memory is a fundamental component of AI systems, underpinning large language models (LLMs) based agents. We introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression. This survey provides a structured and dynamic perspective on research, benchmark datasets, and tools related to memory in AI.
  arXiv  Detail & Related papers  (2025-05-01T17:31:33Z)
• From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs [34.361000444808454]
  Memory is the process of encoding, storing, and retrieving information. In the era of large language models (LLMs), memory refers to the ability of an AI system to retain, recall, and use information from past interactions to improve future responses and interactions.
  arXiv  Detail & Related papers  (2025-04-22T15:05:04Z)
• MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory [49.96019697955383]
  We introduce MemLLM, a novel method of enhancing large language models (LLMs) by integrating a structured and explicit read-and-write memory module.<n>Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular.
  arXiv  Detail & Related papers  (2024-04-17T18:13:16Z)
• Memory Sharing for Large Language Model based Agents [43.53494041932615]
  This paper introduces the Memory Sharing, a framework which integrates the real-time memory filter, storage and retrieval to enhance the In-Context Learning process. The experimental results demonstrate that the MS framework significantly improves the agents' performance in addressing open-ended questions.
  arXiv  Detail & Related papers  (2024-04-15T17:57:30Z)
• Online Adaptation of Language Models with a Memory of Amortized Contexts [82.02369596879817]
  Memory of Amortized Contexts (MAC) is an efficient and effective online adaptation framework for large language models. We show how MAC can be combined with and improve the performance of popular alternatives such as retrieval augmented generations.
  arXiv  Detail & Related papers  (2024-03-07T08:34:57Z)
• Empowering Working Memory for Large Language Model Agents [9.83467478231344]
  This paper explores the potential of applying cognitive psychology's working memory frameworks to large language models (LLMs) An innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios.
  arXiv  Detail & Related papers  (2023-12-22T05:59:00Z)
• MemGPT: Towards LLMs as Operating Systems [50.02623936965231]
  Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows. We propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems. We release MemGPT code and data for our experiments at https://memgpt.ai.
  arXiv  Detail & Related papers  (2023-10-12T17:51:32Z)
• RET-LLM: Towards a General Read-Write Memory for Large Language Models [53.288356721954514]
  RET-LLM is a novel framework that equips large language models with a general write-read memory unit. Inspired by Davidsonian semantics theory, we extract and save knowledge in the form of triplets. Our framework exhibits robust performance in handling temporal-based question answering tasks.
  arXiv  Detail & Related papers  (2023-05-23T17:53:38Z)
• Enhancing Large Language Model with Self-Controlled Memory Framework [56.38025154501917]
  Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information. We propose the Self-Controlled Memory (SCM) framework to enhance the ability of LLMs to maintain long-term memory and recall relevant information.
  arXiv  Detail & Related papers  (2023-04-26T07:25:31Z)
• Neural Storage: A New Paradigm of Elastic Memory [4.307341575886927]
  Storage and retrieval of data in a computer memory plays a major role in system performance. We introduce Neural Storage (NS), a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. NS achieves an order of magnitude improvement in memory access performance for two representative applications.
  arXiv  Detail & Related papers  (2021-01-07T19:19:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.