NeurIPS 2025

A-Mem: Agentic Memory for LLM Agents
Wujiang Xu1, Zujie Liang2, Kai Mei1, Hang Gao1, Juntao Tan1, Yongfeng Zhang1,3
1Rutgers University
2Independent Researcher
3AIOS Foundation
wujiang.xu@rutgers.edu
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.
Code for Benchmark Evaluation:
https://github.com/WujiangXu/AgenticMemory
Code for Production-ready Agentic Memory:
https://github.com/WujiangXu/A-mem-sys
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Introduction
Large Lang...