A-Mem: Agentic Memory for LLM Agents
Authors: Wujiang Xu
Zujie Liang
Kai Mei
Hang Gao
Juntao Tan
Yongfeng Zhang
Venue: NeurIPS 2025 poster ()
Keywords: LLM Agents; Memory System
Primary area: applications

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 code is available at \url{https://anonymous.4open.science/r/AgenticMemory-76B4}.

A-Mem: Agentic Memory for LLM Agents
Wujiang Xu Zujie Liang', Kai Mei', Hang Gao', Juntao Tan', Yongfeng Zhang 1.3
Rutgers University
'Independent Researcher 3 "AIOS Foundation wuj iang. xu@rutgers.ce 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 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 added, we generate 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 similaritics exist. Additionally, this process enables memory evolution as new memories integrated, they 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.
O Code for Benchmark Evaluation:
https://githu..com wtt.ggiiwuuoooowjjanaagggnaC
Q Code for Production-ready Agentic Memory: http:////iiiiiioooooo///a/////oo
Wujjanggu//A-mmm-ssy
Introduction
Large Language Model (LLM) agents have demonstrated remarkable capabilities in various tasks, with recent advances enabling them to interact with environments, execute tasks, and make decisions autonomously [23, 33, 7]. They integrate LLMs with external tools and delicate workflows to improve reasoning and planning abilities. Though LLM agent has strong reasoning performance, it still needs a memory system to provide long-term interaction ability with the external environment [35].
Existing memory systems [25, 28, 21] for LLM agents provide basic memory storage functionality. These systems require agent developers to predefine memory storage structures, specify storage points within the workflow, and establish retrieval timing. Meanwhile, to improve structured memory organization, Mem0 [8], following the principles of RAG [9, 18, 30], incorporates graph databases for storage and retrieval processes. While graph databases provide structured organization for memory systems, their reliance on predefined schemas and relationships fundamentally limits their adaptability. This limitation manifests clearly in practical scenarios when agent learns novel mathematical
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Write
Write
Interaction
Interaction
Read
Read
Environment
LLM Agents
Memory
Environment
LLM Agents
Agentic Memory
(a) Traditional memory system.
(b) Our proposed agentic memory.
Figure 1: Traditional memory systems require predefined memory access patterns specified in workflow, limiting their adaptability to diverse scenarios. Contrastly, our A-MEM enhances the flexibility of LLM agents by enabling dynamic memory operations.
solution, current systems can only categorize and link this information within their preset framework, unable to forge innovative connections or develop new organizational patterns as knowledge evolves. Such rigid structures, coupled with fixed agent workflows, severely restrict these systems' ability to generalize across new environments and maintain effectiveness in long-term interactions. The challenge becomes increasingly critical as LLM agents tackle more complex, open-ended tasks, where flexible knowledge organization and continuous adaptation are essential. Therefore, how to design a flexible and universal memory system that supports LLM agents' long-term interactions remains crucial challenge.
In this paper, we introduce a novel agentic memory system, named as A-MEM, for LLM agents that enables dynamic memory structuring without relying on static, predetermined memory operations. Our approach draws inspiration from the Zettelkasten method [15, 1J. sophisticated knowledge management system that creates interconnected information networks through atomic notes and flexible linking mechanisms. Our system introduces an agentic memory architecture that enables autonomous and flexible memory management for LLM agents. For each new memory, we construct comprehensive notes, which integrates multiple representations: structured textual attributes including several attributes and embedding vectors for similarity matching. Then A- A-MEM analyzes the historical memory repository to establish meaningful connections based on semantic similarities and shared attributes. This integration process not only creates links but also enables dynamic evolution when new memories are incorporated, they can trigger updates to the contextual representations of existing memories, allowing the entire memories continuously refine and deepen its understanding over time. The contributions are summarized as:
We present A- A-MEM, an agentic memory system for LLM agents that enables autonomous generation of contextual descriptions, dynamic establishment of memory connections, and intelligent evolution of existing memories based on experiences. This system equips LLM agents with long-term interaction capabilities without requiring predetermined memory operations.
We design an agentic memory update mechanism where new memories automatically trigger two key operations: link generation and memory evolution. Link generation automatically establishes connections between memories by identifying shared attributes and similar contextual descriptions. Memory evolution enables existing memories dynamically adapt as new experiences are analyzed, leading to the emergence of higher-order patterns and attributes.
We conduct comprehensive evaluations of system using long-term conversational dataset, com- paring performance across six foundation models using six distinct evaluation metrics, demonstrating significant improvements. Moreover, we provide T-SNE visualizations illustrate the structured organization of our agentic memory system.
2
Related Work
2.1 Memory for LLM Agents
Prior works on LLM agent memory systems have explored various mechanisms for memory man- agement and utilization [23, 21, 8, 39]. Some approaches complete interaction storage, which maintains comprehensive historical records through dense retrieval models [39] or read-write memory structures [24]. Moreover, MemGPT [25] leverages cache-like architectures to prioritize recent information. Similarly, SCM [32] proposes a Self-Controlled Memory framework that enhances LLMS' capability to maintain long-term memory through memory stream and controller mechanism. However, these approaches face significant limitations in handling diverse real-world tasks. While they can provide basic memory functionality, their operations are typically constrained by predefined

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structures and fixed workflows. These constraints stem from their reliance on rigid operational patterns, particularly in memory writing and retrieval processes. Such inflexibility leads to poor generalization in new environments and limited effectiveness in long-term interactions. Therefore, de- signing flexible and universal memory system that supports agents' long-term interactions remains crucial challenge.
2.2 Retrieval-Augmented Generation
Retrieval-Augmentedd Generation (RAG) has emerged powerful approach enhance LLMs by incorporating external knowledge sources [18, 6, 10]. The standard RAG [37, 34] process involves indexing documents into chunks, retrieving relevant chunks based on semantic similarity, and augmenting the LLM's prompt with this retrieved context for generation. Advanced RAG systems [20, 12] have evolved to include sophisticated pre-retrieval and post-retrieval optimizations. Building upon these foundations, recent researches has introduced agentic RAG systems that demonstrate more autonomous and adaptive behaviors in the retrieval process. These systems can dynamically determine when and what to retrieve [4, 14], generate hypothetical responses to guide retrieval, and iteratively refine their search strategies based on intermediate results [31, 29].
However, while agentic RAG approaches demonstrate agency in the retrieval phase by autonomously deciding when and what retrieve [4, 14, 38], agentic memory system exhibits ageney more fundamental level through the autonomous evolution of its memory structure. Inspired by the Zettelkasten method, our system allows memories to actively generate their own contextual descriptions, form meaningful connections with related memories, and evolve both their content and relationships as new experiences emerge. This fundamental distinction in agency between retrieval versus storage and evolution distinguishes our approach from agentic RAG systems, which maintain static knowledge bases despite their sophisticated retrieval mechanisms.
3
Methodolodgy
Our proposed agentic memory system draws inspiration from the Zettelkasten method, implementing a dynamic and self-evolving memory system that enables LLM agents to maintain long-tern memory without predetermined operations. The system's 's design emphasizes atomic note-taking, flexible linking mechanisms, and continuous evolution of knowledge structures.
3.1 Note Construction
Building upon the Zettelkasten method's principles of atomic note-taking and flexible organization, introduce an LLM--diven approach to memory note construction. When agent interacts with its environment, we construct structured memory notes that capture both explicit information and LLM- generated contextual understanding. Each memory note m, in our collection M {mi, m2y n is represented as:
m, {c,, K,, G X,, es, L,}
(1)
where represents the original interaction content, 6; is the timestamp of the interaction, K; denotes LLM-generated keywords that capture key concepts, G, contains LLM-generated tags for categorization, X; represents the 4LM-generated contextual description that provides rich semantic understanding, and L, maintains the set of linked memories that share semantic relationships. To enrich each memory note with meaningful context beyond its basic content and timestamp, we leverage an I.LM to analyze the interaction and generate these semantic components. The note construction process involves prompting the LLM with carefully designed templates Pat:
K,, G., X, + LLM(c; I| )!Pa.)
Following the Zettelkasten principle of atomicity, each note captures a single, self-contained unit of knowledge. To enable efficient retrieval and linking, we compute a dense vector representation via text encoder [27] that encapsulates textual components of the note:
e, [ concat(c,, K,, ]
(3)
By using LLMs to generate enriched components, we enable autonomous extraction of implicit knowledge from raw interactions. The multi-faceted note structure (K,, G₁, X,) creates rich rep- resentations that capture different aspects of the memory, facilitating nuanced organization and
3

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Note Construction
Link Generation
Memory Evolution
Memory Retrieval
Retrieve
Model
Environment
L.L Agents
Embedding
Query
Top-
Writs
The sick soica nucks mensayy
Cunvcrurtinn
Top-k
vec catit
dade
prometion makky exic
Memory
saange
Restiiee
LLM
LLM
Timeszupp
I.IM
Slore
Action
Commea
LLM Agents
Contest
Keynonle
+ Evolve
Tags
Note
Notd
Figure Our A-MEM architecture comprises three integral parts memory storage. During note construction, the system processes new interaction memories and stores them as notes with multiple attributes. The link generation process first retrieves the most relevant historical memories and then employs an LLM to determine whether connections should be established between them. The concept of 'box' describes that related memories become intervonnected through their similar contextual descriptions, analogous However, our approach allows individual memories to exist simultancously within multiple different boxes. During the memory retrieval stage, we extract query embeddings using text encoding model and search the memory datahase for relevant matches. When related memory is retrieved, similar memories that are linked within the box also automatically accessed.
Zettelkasten method.
retrieval. Additionally, the combination of L.L -MM-geeeraed semantic components with dense vector representations provides both context and computationally efficient similarity matching.
3.2 Link Generation
Our system implements an autonomous link generation mechanism that enables new memory notes form meaningful connections without predefined rules. When the constretd memory note m, is added to the system, we first leverage its semantic embedding for similarity-based retrieval. For each existing memory note m, M, we compute a similarity score:
En
Sn.j lenlle,
(4)
The system then identifies the top-k most relevant memories:
M Mear (m,l rank(s..;)) s k. m2 E M)
Based on these candidate nearest memories, we prompt the LLM to analyze potential connections based on their potential common attributes. Formally, the link set of memory m, update like:
L; <- LLM( II.M P22)))
(6)
Each generated link 4 is structured as: L; {m,,... mg). By using embedding- retrieval as an initial filter, we enable efficient scalability while maintaining semantic relevance. A A-MEM quickly identify potential connections even in large memory collections without exhaustive comparison. More importantly, the LLM-driven analysis allows for nuanced understanding of relationships that goes beyond simple similarity metrics. The language model identify subtle patterns, causal relationships, and conceptual connections that might not be apparent from embedding similarity alone. We implements the Zettelkasten principle of flexible linking while leveraging modern language models. The resulting network emerges organically from memory content and context, enabling natural knowledge organization.
3.3 Memory Evolution
After creating links for the new memory, A-MEM evolves the retrieved memories based on their textual information and relationships with the memory. For each memory m, in the nearest

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neighbor set M the system determines whether to update its context, keywords, and tags. This evolution process can be formally expressed as:
m (mM [| \ \m Imm, Pa3)
The evolved memory m, then replaces the original memory my in the memory set M. This evolutionary approach enables continuous updates and new connections, mimicking human learning processes. As the system processes more memories over time, it develops increasingly sophisticated knowledge structures, discovering higher-order patterns and concepts across multiple memories. This creates foundation for autonomous memory learning where knowledge organization becomes progressively richer through the ongoing interaction between new experiences and existing memories.
3.4 Retrieve Relative Memory
In each interaction, our A-MEM performs context-aware memory retrieval to provide the agent with relevant historical information. Given a query text from the current interaction, we first compute its dense vector representation using the same text encoder used for memory notes:
cq
(q)
(8)
The system then computes similarity scores between the query embedding and all existing memory notes in M using cosine similarity:
e where 6, m4 Vm, E M
lc,lle,1'
Then retrieve the k most relevant memories from the historical memory storage to construct contextually appropriate prompt.
Mietriveed (m,/rankks,,))) $ k, my E M)
(10)
These retrieved memories provide relevant historical context that helps the agent better understand and respond to the current interaction. The retrieved context enriches the agent's 's reasoning process by connecting the current interaction with related past experiences stored in the memory system.
4 Experiment
4.1 Dataset and Evaluation
To evaluate the effectiveness of instruction-awan recommendation in long-term conversations, we utilize the LoCoMo dataset [22], which contains significantly longer dialogues compared to existing conversational datasets [36, 13]. While previous datasets contain dialogues with around IK tokens over 4-5 sessions, LoCoMo features much longer conversations averaging 9K tokens spanning up 35 sessions, making particularly suitable for evaluating models' ability handle long-range dependencies and maintain consistency over extended conversations. The LoCoMo dataset comprises diverse question types designed to comprehensively evaluate different aspects of model understanding: (1) single-hop questions answerable from a single session; (2) multi- hop questions requiring information synthesis across sessions; (3) temporal reasoning questions testing understanding of time-related information; (4) open-domain knowledge questions requiring integration conversation context with external knowledge; and (5) adversarial questions assessing models' ability to identify unanswerable queries. In total, LoCoMo contains 7,512 question-answer
pairs across these categories. Besides, we use a new dataset, named DialSim [16], to evaluate the effecttivnness of our memory system. It is question-answerin multi-party dialogues. The dataset derived from popular TV shows (Friends, The Big Bang Theory, and The Office), covering 1,300 sessions spanning five years, containing approximately 350,000 tokens, and including more than 1,000 questions per session from refined fan quiz website questions and complex questions generated from temporal knowledge graphs.
dataset derived from long-term
For comparison baselines, we compare to LoCoMo [22], ReadAgent [17], MemoryBank [39] and MemGPT [25]. The detailed introduction of baselines can be found in Appendix A. For evaluation, we employ two primary metries: the Fl score to assess answer accuracy by balancing precision and recall, and BLEU-1 [26] to evaluate generated response quality by measuring word overlap
5

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Table 1: Experimental results on LoCoMo dataset of QA tasks across five categories (Multi Hop, Temporal, Open Domain, Single Hop, and Adversial) using different methods. Results reported in FI and BLEU-1 (%) scores. The best performance is marked bold, and our proposed method A- A-EEM (highlighted gray) demonstrates competitive performance across six foundation language models.
Calegory
Average
Model
Method
Multi Hop
Temporal
Open Doniain BLEU
Single Hop
Adversial
Ranking BLEU
Token
F1
BLEU
F1
BLEU
F1
F1
ELEU
F1
BLEU
F1
Length
LoCoMo
15002
19.75
18.41
11.16
40.36
29.03
69.23
68.75
2.4
2.4
16,900
40-7aini
READAGENT
9.15
6.48
12.60
8.87
5.31
5.12
9.67
7.66
9.81
9.02
4.2
4.2
643
MANDRYBANK
5.00
4.77
9.68
6.99
5.56
5.94
6.61
5.16
7.36
6.48
4.8
4.8
432
MEMGPT
17.72
25.52
42.73
2.4
2.4
16,977
GPT
A-MEM
27.02
20.09
45.85
36.67
12.14
12.00
44.65
37.06
50.03
49.47
1.2
1.2
2,520
LoCoMo
18.00
18.47
9.09
5.78
16:47
14,80
61.56
54.19
52.61
51,13
2.0
2.0
16,910
READAGENT
4.0
4.0
MEMORYBANK
6.49
4.69
2.47
2,43
6.43
5.30
8.28
7.10
4.42
3.67
5.0
5.0
569
MEMGPT
30.36
22.83
17.29
13.18
12.24
11.87
60.16
53.35
34.96
34,25
2.4
2.4
16,987
A-MEM
32,86
23.76
31,23
17.10
15.84
48.43
42.97
36.35
35.53
1.6
1.6
1,216
LoCoMo
9.05
6.55
4.25
4,004
9.91
8.50
11.15
8.67
40.38
401.23
3.4
3.4
16,910
1.50
READAGENT
12.24
7.54
27.32
4.6
4.6
752
MEMORYBANK
11.14
8.25
4.46
2.87
8.05
6.21
13,42
11.01
36.76
34.00
2.6
2.6
284
Qwen2.
MEXGPP
10.44
7.61
4.21
3.89
13:42
11.64
9.56
7.34
31.51
28.90
3,4
3.4
16,953
A-MEM
43.26
1.0
L,300
LoCoMo
4.61
4.29
3.11
2.71
4.55
3.97
7:03
5.69
16.95
14.31
3:2
3.2
16,910
READAGENT
2.47
1,78
3.01
3.01
5:57
5.22
3.25
2:51
15.78
14,01
4.2
4.2
776
MEMORYBANK
3.60
4.2
4.2
MEN:GPT
5.07
4.31
2.94
2.95
7.04
7.10
7.26
5.52
14.47
12,39
2.4
2.4
16,961
A-MEM
12.57
9.01
27.59
25.07
7.12
7.28
17.23
13.12
27.91
25.15
1.0
1.0
1.137
LoCoMo
11.25
9:18
1.38
6.82
11,50
10.38
12.86
10:50
51.39
48.27
3.4
3.4
16,900
READAGENT
5.96
5.12
1.93
2.30
12.46
11.17
7.75
6,03
44.64
40.15
4.6
4.6
665
BANK
13.18
10.03
7.61
6.27
15.78
12.94
17.30
14.03
52.61
47.53
2.0
2.0
274
Liana 3.2
MENGPT
9.19
6.96
4.02
4,79
11.14
8.24
10.16
7.68
49.75
45.11
4.0
4.0
16,950
A-MFM
19.06
11.71
17,80
10.28
17.55
14.67
28.51
24,13
58.81
54,28
1.0
1.0
1,376
LoCoMo
28.46
2.8
2.8
16,100
READAGENT
2.47
1.78
3.01
3.01
5.57
5.22
3.25
2.51
15.78
14.01
4.2
4.2
461
MEMORYBANK
6.19
4.47
3.49
3.13
4.07
4.57
7.61
6.03
18.65
17.05
3.2
3.2
263
MENGPT
3.8
3.8
16,956
A-MEM
17.44
11.74
26.38
19.50
12.53
11.83
28.14
23.87
42.04
40.60
1.0
1.0
1,126
with ground truth responses. Also, report the average token length for answering one question.
Besides reporting experiment results with four additional metrics (ROUGE-L, ROUGE-2, METEOR, and SBERT Similarity), also present experimental outcomes using different foundation models including DeepSeek-RR R1-32B [1 [11], Claude 3.0 Haiku [2], and Claude 3.5 Haiku [3] in Appendix A.3.
4.2 Implementation Details
For all baselines and our proposed method, we maintain consistency by employing identical system prompts as detailed in Appendix B. The deployment of Qwen-1.5B/3B and Llama 3.2 IB/3B models is accomplished through local instantiation using Ollama with LiteLLM 2 managing structured output generation. For GPT models, we utilize the official structured output API. In our memory retrieval process, we primarily employ k=10 for top-k memory selection to maintain computational efficiency, while adjusting this parameter for specific categories to optimize performance. The detailed configurations of can be found in Appendix A.5. For text embedding. we implement the all- all-minilm-lee 16-v222--662221-111 model across all experiments.
4.3 Empricial Results
Performance Analysis. In our empirical evaluation, we compared A A-MEM with four competitive baselines including LoCoMo [22], ReadAgent [17], MemoryBank [39], and MemGPT [25] on the LoCoMo dataset. For non-GPT foundation models, our A-MEM consistently outperforms all baselines across different categories, demonstrating the effectiveness of our agentic memory approach. For GPT-based models, while LoCoMo and MemGPT show strong performance in certain categories like Open Domain and Adversial tasks due their robust pre-trained knowledge in simple fact retrieval, our A-MEM demonstrates superior performance in Multi-Hop tasks achieves at least two times better performance that require complex reasoning chains. In addition experiments on the LoCoMo dataset, we also compare our method on the DialSim dataset against LoCoMo and MemGPT. A-MEM consistently outperforms all baselines across evaluation metrics, achieving an Fl
baccper/ggthaub.coo/ollmm////// htttp::/ggitabb..om///r//t//////

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Table 2: Comparison of different memory mechanisms across multiple evaluation metrics on DialSim [16].
Higher scores indicate better performance, with A-MEM showing superior results across all metrics.
Method
F1
BLEU-1
ROUGE-L
ROUGE-2
METEOR
SBERT Similarity
LoCoMo
2.55
3.13
2.75
0.50
1.64
15.76
MemGPT
1.18
1.07
0.96
0.42
0.95
8.54
A-MEM
3.45
3,37
3,54
3.60
2.05
19.51
Table 3: An ablation study was conducted to evaluate our proposed method against the GPT-4o-mini base model.
The notation 'w/o' indicates experiments where specific modules were removed. The abbreviations LG and ME denote the link generation module and memory evolution module, respectively.
Category
Method
Multi Hop
Temporal
Open Domain
Single Hop
Adversial
BLEU-1
F1
BLEU-1
F1
BLEU-1
BLEU-1
F1
BLEU-1
w/o LG & ME
9.65
7.09
24.55
19.48
7.77
6.70
13.28
10.30
15,32
18.02
w/o ME
21.35
15.13
31.24
27.31
10.13
10.85
39.17
34.70
44.16
45.33
A-MRM
27.02
20.09
45.85
36.67
12.14
12.00
44.65
37,06
50.03
49.47
score of 3.45 (a 35% improvement over LoCoMo's 2.55 and 192% higher than MemGPT's 1.18). The effectiveness of A-MEM stems from its novel agentic memory architecture that enables dynamic and structured memory management. Unlike traditional approaches that use static memory operations, our system creates interconnected memory networks through atomic notes with rich contextual descriptions, enabling more effective multi-hop reasoning. The system's 's ability to dynamically establish connections between memories based on shared attributes and continuously update existing memory descriptions with new contextual information allows it to better capture and utilize the relationships between different pieces of information.
Cost- Eafteffficncy Analysis. A-MEM demonstrates significant computational and cost efficiency along- side strong performance. The system requires approximately 1,200 tokens per memory operation, achieving an 85-93% reduction in token usage compared to baseline methods (LoCoMo and MemGPT with 16,900 tokens) through our selective top-k retrieval mechanism. This substantial token reduc- tion directly translates to lower operational costs, with each memory operation costing less than $0.0003 when using commercial API -aariinssmmkinng large-scale deployments economically viable. Processing times average 5.4 seconds using GPT-4o-mini and only 1.1 seconds with locally-hosted Llama 3.2 IB on a single GPU. Despite requiring multiple LLM calls during memory processing, A-MEM maintains this cost-effective resource utilization while consistently outperforming baseline approaches across all foundation models tested, particularly doubling performance on complex multi-hop reasoning tasks. This balance of low computational cost and superior reasoning capability highlights A-MEM's 's practical advantage for deployment in the real world.
4.4 Ablation Study
To evaluate the effectiveness of the Link Generation (LG) and Memory Evolution (ME) modules, we conduct the ablation study by systematically removing key components of our model. When both LG and ME modules are removed, the system exhibits substantial performance degradation, particularly in Multi Hop reasoning and Open Domain tasks. The system with only LG active (w/o ME) shows intermediate performance levels, maintaining significantly better results than the version without both modules, which demonstrates the fundamental importance of link generation in establishing memory connections. Our full model, A-MEM, consistently achieves the best performance across all evaluation categories, with particularly strong results in complex reasoning tasks. These results reveal that while the link generation module serves critical foundation for memory organization, the memory evolution module provides essential refinements to the memory structure. The ablation study validates our architectural design choices and highlights the complementary nature of these two modules in creating an effective memory system.
4.5 Hyperparameter Analysis
We conducted extensive experiments to analyze the impact of the memory retrieval parameter which controls the number of relevant memories retrieved for each interaction. As shown in Figure 3, we evaluated performance across different values (10, 20, 30, 40, 50) on five categories of tasks using GPT-4o-mini as our base model. The results reveal an interesting pattern: while increasing generally leads to improved performance, this improvement gradually plateaus and sometimes slightly decreases at higher values. This trend is particularly evident in Multi Hop and Open Domain
7

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25.0
27.5
45.0
01.03
12.00
22.
10
10.79
31.8
37.5
R-- Re-cak
15.0
35.0
1.1
22.5
vollar
30
00
50
20
90
50
(a) Multi Hop
(b) Temporal
(c) Open Domain
41.55
2112
30
30
30.7
20
10
22
(d) Single Hop
(e) Adversarial
Figure 3: Impact of memory retrieval parameter k across different task categories with GPT-4o-mini the base model. While larger values generally improve performance by providing richer historical context, the gains diminish beyond certain thresholds, suggesting trade-off between context richness and effective information processing. This pattern is consistent across all evaluation categories, indicating the importance of balanced context retrieval for optimal performance.
Table 4: Comparison of memory usage and retrieval time across different memory methods and scales.
Memory Size
Method
Memory Usage (MB)
Retrieval Time (18)
A-MEM
1.46
0.31 0.30
1,000
MemoryBank [39]
1.46
0.24 t 0,20
ReadAgent [17]
1.66
43.62 8.47
A-MEM Memory Bank [39]
14.65
0.25
10,000
14.65
0.26 0,13
ReadAgent [17]
14.65
484.45 + 93.86
A-MEM
146.48
1.40 + 0.49
100,000
MemoryBank [39]
146.48
0.78 0.26
ReadAgent
146.48
6,682.22 111.63
A-MEM MemoryBank Bank [39]
1464.84
3,70 0.74
1,000,000
1464.84
1,91 + 0.31
ReadAgent [17]
1464.84
120,069.68 + 1.673.39
tasks. The observation suggests delicate balance memory retrieval while larger values provide richer historical context for reasoning, they may also introduce noise and challenge the model's s capacity to process longer sequences effectively. Our analysis indicates that moderate k values strike an optimal balance between context richness and information processing efficiency.
4.6 Scaling Analysis
To evaluate storage costs with accumulating memory, we examined the relationship between storage size and retrieval time across our A-MEM system and two baseline approaches: MemoryBank [39] and Read/geet [17]. We evaluated these three memory systems with identical memory content across four scale points, increasing the number of entries
factor of 10 at each step (from 1,000 to 10,000, 100,000, and finally 1,000,000 entries). The experimental results reveal key insights about our A-MEM system's scaling properties: In terms of space complexity, all three systems exhibit identical linear memory usage scaling (O(N)) as expected for vector-based retrieval systems. This confirms that A-MEM introduces additional storage overhead compared to baseline approaches. For retrieval time, A-MEM demonstrates excellent efficiency with minimal increases as memory size grows. Even when scaling million memories, A-MEM's retrieval time increases only from 0.3 to 3.70ps, representing exceptional performance. While MemoryBan Bank shows slightly faster retrieval times, A-MEM maintains comparable performance while providing richer memory representations and functionality. Based on our space complexity and retrieval time analysis, we conclude that A-MEM's 's retrieval mechanisms maintain excellent efficiency even at large scales. The minimal growth in retrieval time across memory sizes addresses concerns about efficiency in large-scale memory systems, demonstrating that A-MEM provides a highly scalable solution long-term conversation management. This unique combination of efficiency, scalability, and enhanced memory capabilities positions A-MEM as significant advancement building powerful and long-term memory mechanism for LLM Agents.

---

Arrem Ease
A-mem
8ase
20
10
10
-10
-20
-20
-10
10
20
-20
-10
20
(a) Dialogue 1
(b) Dialogue
Figure T-SNE Visualization of Memory Embeddings Showing More Organized Distribution with A-MEM (blue) Compared to Base Memory (red) Across Different Dialogues. Base Memory represents A-MEM without link generation and memory evolution.
4.7 Memory Analysis
We present the t-SNE visualization in Figure 4 of memory embeddings to demonstrate the structural advantages of our agentic memory system. Analyzing two dialogues sampled from long-term conversations in LoCoMo [22]. we observe that A-MEM (shown in blue) consistently exhibits more coherent clustering patterns compared to the baseline system (shown in red). This structural organization is particularly evident in Dialogue 2, where well-defined clusters emerge in the central region, providing empirical evidence for the effectiveness of our memory evolution mechanism and contextual description generation. In contrast, the baseline memory embeddings display a more dispersed distribution, demonstrating that memories lack structural organization without our link generation and memory evolution components. These visualization results validate that A A-MEM can autonomously maintain meaningful memory structures through dynamic evolution and linking mechanisms. More results can be seen in Appendix A.4.
5 Conclusions
In this work, we introduced A-MEM, a novel agentic memory system that enables LLM agents to dynamically organize and evolve their memories without relying on predefined structures. Drawing inspiration from the Zettelkasten method, our system creates an interconnected knowledge network through dynamic indexing and linking mechanisms that adapt to diverse real-world tasks. The sys- tem's core architecture features autonomous generation of contextual descriptions for new memories and intelligent establishment of connections with existing memories based shared attributes. Furthermore, our approach enables continuous evolution of historical memories by incorporating new experiences and developing higher-order attributes through ongoing interactions. Through extensive empirical evaluation across six foundation models, we demonstrated that A-MEM achieves superior performance compared to existing state-of-the the- baselines in long-term conversational tasks. Visualization analysis further validates the effectiveness of our memory organization approach. These results suggest that agentic memory systems can significantly enhance LLM agents' ability to utilize long-term knowledge in complex environments.
Limitations
While our agentic memory system achieves promising results, we acknowledge several areas for potential future exploration. First, although system dynamically organizes memories, the quality of these organizations may still influenced by the inherent capabilities the underlying language models. Different L.LMs might generate slightly different contextual descriptions establish varying connections between memories. Additionally, while our current implementation focuses on text-based interactions, future work could explore extending the system handle multimodal information, such as images or audio, which could provide richer contextual representations.

---

References
[1] Sönke Ahrens. How to Take Smart Notes: One Simple Technique to Boost Writing, Learning and Thinking. Amazon, 2017. Second Edition.
[2] Authropic. The claude model family: Opus, sonnet, haiku. Anthropic, Mar 2024. Accessed May 2025.
[3] Anthropic. Claude 3.5 sonnet model card addendum. Technical report, Anthropic, 2025. Accessed May 2025.
[4] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannanch Hajishirzi. Self-rag: Learning to retrieve, generate, and critique through self-reflection. arXiv preprint arXiv:2310.11111 2023.
[5] Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pages 65-72,
2005.
[6] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann,, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean- -ap-tttt Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference machine learning, pages 2206-22401 PMLR, 2022.
[7] Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091-288 14, 2023.
[8] Khant Dev and Singh Taranjeet. mem0: The memory layer for ai agents. https://githir com/memmai/ 0mmmmm00immmmmmmmmmmmmmm / 2024.
[9] Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. From local to global: graph rag approach to query-focused summarization. arXiv preprint arXiv:2404. 16/30, 2024.
[10] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. petrieval-augmeee generation for large language models: A survey, arXiv preprint arXiv:2312.1099) 2023.
[11] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Iims via reinforcement learning. arxiv preprint arXiv:2501.12944, 2025.
[12] I. Ilin. Advanced rag techniques: An illustrated overview, 2023.
[13] Jihyoung Jang, Minseong Boo, and Hyounghun Kim. Conversation chronicles: Towards diverse temporal and relational dynamics in multi-session conversations. arXiv preprint arXiv:2310. 13420, 2023.
[14] Zhengbao Jiang, Frank F Xu. Luyu Gao, Zhiqing Sun. Qian Liu, Jane Dwivedi-Yo Yiming Yang, Jamie Callan, and Graham Neubig, Active retrieval augmented generation. arXiv preprint arXiv:2305.0663 069983, 2023.
[15] David Kadavy. Digital Zettelkasten: Principles, Methods, & Exxamples Google Books, May 2021.
[16] Jiho Kim, Woosog Chay, Hyeonji Hwang, Daeun Kyung. Hyunseung Chung, Eunbyeol Cho, Yohan Jo, and Edward Choi. Dialsim: A real-time simulator for evaluating long-term multi-party dialogue understanding of conversational agents. arXiv preprint arXiv:2406. 13144, 2024.
[17] Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta, John Canny, and Ian Fischer. human-inspired reading agent with gist memory of very long contexts. arXiv preprint arXiv:2402.09927,, 2024.
10

---

[18] Patrick Lewis, Ethan Perez, Aleksandra Piktus. Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih. Tim Rocktäschel, et al. Retrieval-augmentt generation for knowledge-intensivee nlp tasks. Advances in Neural Information Processing Systems, 33:9459-944444 -4554, 2020.
[19] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches oul, pages 74-81, 2004.
[20] Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, et al. Ra-dit: Retrieval-augmmeted dual instruction tuning. arXiv preprint arXiv:2310.01352. 2023.
[21] Zhiwei Liu, Weiran Yao, Jianguo Zhang. Liangwei Yang. Zuxin Liu, Juntao Tan, Prafulla K Choubey, Tian Lan, Jason Wu, Huan Wang, et al. Agentlite: A lightweight library for building and advancing task-oriented Ilm agent system. arXiv preprint arXiv:2402.15538 2024.
[22] Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, and Yuwei Fang. Evaluating very long-term conversational memory of Ilm agents. arXiv preprint arXiv:2402.17753, 2024.
[23] Kai Mei, Zelong Li. Shuyuan Xu, Ruosong Ye, Yingqiang Ge, and Yongfeng Zhang. agent operating system. arXiv e-prints, pp. arXiv-2403, 2024.
Aios: Llm
[24] Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schüize. Ret-llm: Towards general read-write memory for large language models. arXiv preprint arXiv:2305. 14322, 2023.
[25] Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, lon Stoica, and Joseph E Gonzalez. Memgpt: Towards ilms operating systems. arXiv preprint arXiv:2310.08560, 2023.
[26] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings the 40th annual meeting of the Association for Computational Linguistics, pages 311-318, 2002.
[27] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert- networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2019.
[28] Aymeric Roucher, Albert Villanova del Moral, Thomas Wolf, Leandro von Werra, and Erik Kaunismäki. 'smolagents': a smol library to build great agentic systems. https://githul: com/huggringface/smie stticce//mmlaagnat..oao 2025.
[29] Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. Enhancing retrieval-augmenned large language models with iterative retrieval-generation synergy. arXiv preprini arXiv::305. 15294, 2023.
[30] Zeru Shi, Kai Mei, Mingyu Jin, Yongye Su, Chaoji Zuo, Wenyue Hua, Wujiang Xu, Yujie Ren, Zirui Liu, Mengnan Du et al. From commands to prompts: Llm-based semantic file system for aios. arXiv preprint arXiv:2410.118433 2024.
[31] Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving retrieval with chain-of-though reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:2212. 10509, 2022.
[32] Bing Wang, Xinnian Liang, Jian Yang, Hui Huang, Shuangzhi Wu, Peihao Wu, Lu Lu, Zejun Ma. and Zhoujun Li. Enhancing large language model with self-controlled memory framework. arXiv preprint arXiv:2304. 1tt:22304.13343. 2023.
[33] Xingyao Wang, Boxuan Li, Yufan Song, Frank F Xu, Xiangru Tang, Mingchen Zhuge, Jiayi Pan, Yueqi Song, Bowen Li, Jaskirat Singh, et al. Openhands: An open platform for ai software developers as generalist agents. arXiv preprint arXiv:2407.16741 2024.
[34] Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig. Learning to filter context for retrieval- augmented generation. arXiv preprint arXiv:2311.08377, 2023.
11

---

[35] Lilian Weng. Lim- Llm-powered autonomous agents. lilianweng. lillanweng.githubbio, Jun 2023.
[36] J Xu. Beyond goldfish memory: Long-term open-domain conversation. arXiv preprint arXiv:2107.07567, 2021.
[37] Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma. Hongwei Wang, and Dong Yu. Chain-of-note: Enhancing robustness in retrieval-uugmented language models. arXiv preprint 1rX1102311.09210,, 2023.
[38] Zichun Yu, Chenyan Xiong, Shi Yu, and Zhiyuan Liu. Augmentation-aadated retriever improves generalization of language models as generic plug-in. arXiv preprint arXiv:2305. 173333 2023.
[39] Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19724-19731, 2024.
12

---

Contents
1
Introduction
2 Related Work
2.1 Memory for L.M Agents
2.2
Retrieval-Augmented Generation
3
Methodolodgy
3.1
Note Construction
3.2
Link Generation...
3.3
Memory Evolution
3.4
Retrieve Relative Memory
Experiment
4.1
Dataset and Evaluation
4.2
Implementation Details
4.3
Empricial Results
4.4
Ablation Study
4.5
Hyperparameter Analysis
4.6
Scaling Analysis
4.7
Memory Analysis
5
Conclusions
6
Limitations
Experiment
14
A.1 Detailed Baselines Introduction.
14
A.2 Evaluation Metric
14
A.3 Comparison Results
15
A.4 Memory Analysis
16
A.5 Hyperparameters setting
17
Prompt Templates and Examples
19
B.1 Prompt Template of Note Construction
19
B.2 Prompt Template of Link Generation
19
B.3 Prompt Template of Memory Evolution
20
B.4 Examples of Q/A with A-MEM
13

---

APPENDIX
A Experiment
A.1 Detailed Baselines Introduction
LoCoMo [22] takes direct approach by leveraging foundation models without memory mechanisms question answering tasks. For each query, incorporates the complete preceding conversation and questions into the prompt, evaluating the model's reasoning capabilities.
ReadAgent [17] lackles long-context document processing through a sophisticated three-step method- ology: it begins with episode pagination to segment content into manageable chunks, followed by memory gisting distill each page into concise memory representations, and concludes with interac- tive look-up to retrieve pertinent information as needed.
MemoryBank [39] introduces innovative memory management system that maintains and effi- ciently retrieves historical interactions. The system features dynamic memory updating mechanism based the Ebbinghaus Forgetting Curve theory, which intelligently adjusts memory strength according to time and significance. Additionally, it incorporates a user portrait building system that progressively refines its understanding of user personality through continuous interaction analysis.
MemGPT [25] presents a novel virtual context management system drawing inspiration from traditional operating systems' memory hierarchies. The architecture implements dual-tier structure: a main context (analogous to RAM) that provides immediate access during LLM inference, and an external context (analogous to disk storage) that maintains information beyond the fixed context window.
A.2 Evaluation Metric
The F1 score represents the harmonic mean of precision and recall, offering balanced metric that combines both measures into single value. This metric is particularly valuable when we need to balance between complete and accurate responses:
Fl 2. precision recall
(11)
precision recall
where
true positives
precision true positives + false positives
(12)
true positives
recall true positives + false negatives
(13)
In question-answering systems, the Fl score serves a crucial role in evaluating exact matches between predicted and reference answers. This is especially important for span-based QA tasks, where systems must identify precise text segments while maintaining comprehensive coverage of the answer.
BLEU-1 [26] provides method for evaluating the precision of unigram matches between system outputs and reference texts:
Σ 1=
BLEU-1 = BP exp
Wn log Pn
(14)
where
icer
BP = c 1-rlc iferr
(15)
Σ, Σx min (hiR, m3k)
Pn
Σ, Ex has
(16)
14

---

Here, e is candidate length, is reference length, hGg is the count of n n-gram i in candidate k, and ma the maximum count in any reference. In QA, BLEU-1 evaluates the lexical precision of generated answers, particularly useful for generative QA systems where exact matching might be too strict.
ROUGE-L [19] measures the longest common subsequence between the generated and reference texts.
(1+ R
ROUGE-L
(17)
Rí + BPP
R₁ LCS(X,Y)
(18)
|X|
Pt = LCS(X, Y) |
(19)
where is reference text, Y candidate text, and LCS is the Longest Common Subsequence.
ROUGE-2 [19] calculates the overlap of bigrams between the generated and reference texts.
ROUGE-2 Ebigramerer min (buunaa
Count Coute (bigram)) bigramerdt (ougta)))igramm)
(20)
Σ
Both ROUGE-L and ROUGE-2 are particularly useful for evaluating the fluency and coherence of generated answers, with ROUGE-L focusing on sequence matching and ROUGE-2 on local word order.
METEOR [5] computes score based aligned unigrams between the candidate and reference texts, considering synonyms and paraphrases.
METEOR F Fmean (1 Penalty)
(21)
10P R
F Fmean=
(22)
R + 9P
Penalty = 0.5 ch
(23)
where P is precision, R is recall, ch is number of chunks, and m is number of matched unigrams. METEOR is valuable for QA evaluation as it considers semantic similarity beyond exact matching, making it suitable for evaluating paraphrased answers.
SBERT Similarity [27] measures the semantic similarity between two texts using sentence embed- dings.
SBERT -HEERTSSiilarity = cos(SBBRT(()) (BERRT())))BETT())))
(24)
b
cos a, 6) ||a|||||||
(25)
SBERT(#) represents the sentence embedding of text. SBERT Similarity is particularly useful for evaluating semantic understanding in QA systems, as it can capture meaning similarities even when the lexical overlap is low.
A.3 Comparison Results
Our comprehensive evaluation using ROUGE-2, ROUGE-L, METEOR, and SBERT metrics demon- strates that A-MEM achieves superior performance while maintaining remarkable computational efficiency. Through extensive empirical testing across various model sizes and task categories, we have established A-MEM as a more effective approach compared to existing baselines, supported by several compelling findings. In our analysis of non-GPT models, specifically Qwen2.5 and Llama 3.2, baseline approaches across all metrics. The Multi-Hop category showcases particularly striking results, where Qwen2.5-15b with A-MEM achieves ROUGE-L score of 27.23, dramatically surpassing LoComo's 4.68 and ReadAgent's 2.81 representing a nearly six-fold improvement. This pattern of superiority extends consistently across METEOR and SBERT
A-MEM consistently outperforms
15

---

Table 5: Experimental results on LoCoMo dataset of QA tasks across five categories (Multi Hop, Temporal, Open Domain, Single Hop, and Adversial) using different methods. Results reported in ROUGE-2 and ROUGE-L scores, abbreviated to RGE-2 RGE-L. The best performance marked bold, and our proposed method A A-MEM (highlighted in gray) demonstrates competitive performance across six foundation language models.
Category
Model
Method
Multi Hop
Temporal
Open Domain
Single Hop
Adversial
RGE-2
RGE-L
RGE-2
RGE-L
RGE-2
RGE-L
RGE-2
RGE-L
RGFx2
RGE-L
LoCoMo
9.64
23.92
2.01
18.09
3.40
11.58
26.48
40.20
60.46
69.59
40-mini
READAGENT
2,47
9.45
5.76
6.66
9.79
MANKRYBANK
1.18
5.43
0.52
9.64
0.97
5.77
1.64
6.63
4,55
7.35
MEMGPT
10.58
25.60
4.76
0.76
9.14
28.44
42.24
36.62
43.75
GPT
A-MEM
10.61
25.86
21.39
44.27
3.42
12.09
29.50
45.18
42.62
50.04
LoCoMo
11.33
30.65
1.68
8.17
3.21
16.33
45.42
63.86
45.13
52.67
READAGENT
3,91
14.36
0.43
3.96
0.52
8.58
4.75
13.41
4.24
6.81
MANK
1.84
7,36
0.36
2.29
2.13
6.85
3.02
9.35
1.22
4,41
MEMGPT
30.18
4.66
3.27
43.27
62.75
28.72
35.08
A-MEM
12.76
31.71
9.82
25.04
6.09
16.63
33.67
50.31
30.31
36.34
LoCoMo
9:24
0.00
4.68
45.61
READAGENT
0.74
7.14
0.10
2.81
3.05
12,63
1.47
7.88
20.73
27.82
MAMORYBANK
1.51
11.18
0.14
5.39
1.80
8.44
5.07
13.72
29.24
36.95
Qwen2.5
MEMGPT
1.16
11.35
0.00
7,88
2.87
14.62
2.18
9.82
23.96
31.69
A-MRM
4.88
17.94
5.88
27.23
3.44
16.87
12.32
24.38
36.32
46.60
LoCoMO
0.49
4.83
0.14
3.20
1.31
5.38
6.98
12.66
17.10
READAGENT
0.08
4,08
0.00
1.96
1.26
6.19
0.73
4.34
7.35
10.64
3.76
0.05
6.32
9.55
13.41
MEMGPT
0.69
5,55
0.05
3.17
1.90
7.90
2.05
7.32
10.46
14.39
2.91
12.42
8.11
27.74
1,51
7.51
8,80
17.57
21.39
27.98
LoCoMO
2.51
11.48
0.44
8.25
1.69
13.06
2.94
13.00
39.85
52.74
READAGENT
0.53
6.49
0.00
4,62
5.47
14.29
1.19
8.03
34.52
45.55
MEMORYBANK
2,96
13.57
0.23
10.53
4.01
18.38
6.41
17.66
41.15
53.31
LJama 3.2
MEMGPT
1.82
9.91
0.06
6.56
2.13
11.36
2.00
10.37
38.59
$0.31
A-MEM
4.82
19.31
1.84
20.47
5,99
18,49
14.82
29.78
46.76
60.23
LoCoMo
0.98
7.22
0.03
4,45
2.36
11.39
2.85
8.45
25.47
30.26
2,47
2.51
14.01
MEMORYBANK
1.83
6.96
0.25
3.41
0.43
4.43
2,73
7.83
14.64
18.59
MEMGPT
0.72
5,39
0.11
2.85
0.61
5.74
1.45
4.42
16.62
21.47
A-MRM
6.02
17.62
7.93
27.97
5.38
13,00
16.89
28.55
35.48
42.25
scores. When examining GPT-based models, our results reveal an interesting pattern. While LoComo and MemGPT demonstrate strong capabilities in Open Domain and Adversarial tasks, A-MEM shows remarkable superiority in Multi- Hop reasoning tasks. Using GPT-4o-mini, A- MEM achieves a ROUGE-L score of 44.27 in Multi Hop tasks, more than doubling LoComo's 's 18.09. This significant advantage maintains consistency across other metrics, with METHOR scores of 23.43 versus 7.61 and SBERT scores 70.49 versus 52.30. The significance of these results is amplified by A-MEM'S exceptional computational efficiency. Our approach requires only 1 1,200-2,500 tokens, compared to the substantial 16,900 tokens needed by LoComo and MemGPT. This efficiency stems from two key architectural innovations: First, our novel agentic memory architecture creates interconnected memory networks through atomic notes with rich contextual descriptions, enabling more effective capture and utilization of information relationships. Second, our selective top-k retrieval mechanism facilitates dynamic memory evolution and structured organization. The effectiveness of these in- novations is particularly evident in complex reasoning tasks, demonstrated by the consistently strong Multi hulti-Hop performance across evaluation metrics. Besides, we also show the experimental results with different foundational models including DeepSeek-Re R1-32B [11], Claude 3.0 Haiku [2] and Claude 3.5 Haiku [3].
A.4 Memory Analysis
In addition to the memory visualizations of the first two dialogues shown in the main text, we present additional visualizations in Fig.5 that demonstrate the structural advantages of our agentic memory system. Through analysis of two dialogues sampled from long-term conversations in LoCoMo[22] we observe that A-MEM (shown blue) consistently produces more coherent clustering patterns compared to the baseline system (shown in red). This structural organization is particularly evident Dialogue 2, where distinct clusters emerge the central region, providing empirical support for the effectiveness of our memory evolution mechanism and contextual description generation. In contrast, the baseline memory embeddings exhibit more scattered distribution, indicating that memories lack structural organization without our link generation and memory evolution components.
16

---

Table 6: Experimental results on LoCoMo dataset of QA tasks across five categories (Multi Hop. Temporal, Open Domain, Single Hop, and Adversial) using different methods. Results reported in METEOR and SBERT Similarity scores, abbreviated ME and SBERT. best performance is marked bold, our proposed method A-MEM (highlighted in gray) demonstrates competitive performance across six foundation language models.
Category
Model
Method
Multi Hop SBERT
Temporul
Open Domain SBERT
Single Hop SBERT
Adversial
ME
ME
SBERT
ME
ME
ME
SBERT
LoCoMo
47.97
52.30
40.42
57.78
63.28
40-mini
READAGENT
5,46
28.67
4.76
45.07
3.69
26.72
8.01
26.78
8.38
15.20
MEMORYBANK
3.42
21.71
4.07
37.58
4.21
23.71
5.81
20.76
6.24
13.00
MEMGPT
15.79
49.33
13.25
61.53
4.59
32.77
41.40
58.19
39.16
47.24
GPT
A-MEM
16.36
49.46
23.43
70.49
8.36
38.48
42.32
59,38
45.64
53.26
LOCOMO
16.34
53.82
7.21
32.15
8.98
43.72
53,39
73,40
47.72
56,09
READAGENT
7,86
37.41
3.76
26.22
4.42
30.75
9.36
31.37
5.47
12.34
MEMORYBANK
3,22
26.23
2.29
4.18
24.89
6.64
23,90
2.93
10.01
MEMGPT
16.64
55.12
12.68
35.93
7.78
37,91
52.14
72.83
31.15
39.08
A-MEM
17.53
55.96
13.10
45.40
10,62
38.87
41.93
62.47
32.34
40.11
LoCoMo
4,99
32.23
2.86
34,03
5.89
35.61
8.57
29.47
40.53
50.49
1.5b
READAGENT
28.20
26.33
MEMORYBANK
5.57
35,40
2.80
32.47
4.27
33.85
10.59
32.16
32.93
42.83
Qwen2.5
MEMGPT
5.40
35.64
2.35
39.04
7.68
40.36
7.07
30.16
27.24
40.63
A-MEM
9.49
43.49
11.92
61.65
9.11
42.58
19.69
41.93
40.64
$2.44
LoCoMo
2,00
24.37
1,92
25.24
25,38
21.28
16.67
23.14
READAGENT
1.78
21.10
1.69
20.78
4,43
25.15
3.37
18.20
10.46
17.39
MEMORYBANK
2.37
17.81
2.22
21.93
3.86
20.65
3.99
16.26
15.49
20.77
MEMGPT
3.74
24.31
2.25
27.67
6.44
29.59
6.24
22.40
13.19
20.83
A-MEM
6.25
33.72
14.04
62.54
6.56
30.60
15.98
33,98
27.36
33.72
LoCoMO
5.77
38.02
3.38
45.44
6.20
42.69
9.33
34.19
46.79
60.74
READAGENT
2,97
29.26
1.31
26.45
7.13
39.19
5.36
26.44
42.39
54.35
MEMORYBANK
6.77
39.33
4.43
45.63
7.76
42.81
13.01
37.32
50.43
60.81
Liama 3.2
MEMGPT
5.10
32.99
2.54
41.81
3.26
35,99
6.62
30.68
45
61.33
A-MEM
9.01
45.16
7.50
54.79
8,30
43.42
22.46
47.07
53.72
68.00
LoCoMo
3.69
27.94
2.96
20.40
6.46
32.17
6.58
22.92
29.02
35.74
READAGENT
1.21
17.40
2.33
2.46
21.25
HEMORYBANE
3.84
25.06
2.73
13.65
3.05
21.08
6.35
22.02
17.14
24.39
MEMGPT
2.78
22.06
2.21
14.97
3.63
23.18
3.47
17.81
20.50
26.87
A-MEM
9.74
39.32
13.19
59.70
32.27
24.30
42.86
39.74
46.76
Table Experimental results on LoCoMo dataset of QA tasks across five categories (Multi Hop, Temporal, Open Domain, Single Hop, and Adversial) using different methods. Results are reported in Fl and BLEU-1 (%)
scores with different foundation models.
Category Open Domain
Method
Multi Hop
Temporal
Single Hop BLEU-1
Adversial
F1
BLEU--
F1
BLEU-1
BLEU-1
FI
BLEU--
DeepSeck-R1-325
LoCoMo
4.79
4.35
12.52
10.72
8.20
20.23
MEMGPT
8.28
5.45
4.97
10.97
11.34
9,03
30.77
29.23
A-MEM
15.02
10.64
14.64
11.01
12.82
15.37
12.30
27.92
27.19
Claude 3.0 Haiku
LoCoMo
4,56
3.33
0.82
0.59
2.86
3.22
3.56
3.24
3.46
3.42
MEMGPT
7.65
6,36
1.65
1.26
7.41
6.64
8.60
7.29
7.66
7.37
A-MEM
19.28
14.69
16.65
12.23
11.85
9.61
34.72
30.05
35.99
34.87
Claude 3.5 Haiku
LoCoMo MEMGPT
11.34
8.21
3.29
2.69
3.79
3.58
14.01
12.57
7.37
7.12
8.27
6.55
3.99
2.76
4.71
4.48
16.52
14.89
5.64
5.45
A-MEM
29.70
23.19
31.54
27.53
11.42
9.47
42.60
37.41
13.65
12.71
These visualizations validate that A-MEM can autonomously maintain meaningful memory structures through its dynamic evolution and linking mechanisms.
A.5 Hyperparameters setting
All hyperparameter values are presented in Table 8. For models that have already achieved state-of-the-art (SOTA) performance with k=10, we maintain this value without further tuning.
17

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30
20
-12
(a) Dialogue 3
(b) Dialogue 4
(c) Dialogue
20
-10
-30
-40
-70
(d) Dialogue 6
(e) Dialogue 7
(f) Dialogue
1104
-10
-30
-30
22
20
(g) Dialogue
(h) Dialogue 10
Figure T-SNE Visualization of Memory Embeddings Showing More Organized Distribution with A A-MEM (blue) Compared to Base Memory (red) Across Different Dialogues. Base Memory represents A-MEM without link generation and memory evolution.
Table Selection of k values retriever across specific categorics and model choices.
Model
Multi Hop
Temporal
Open Domain
Single Hop
Adversial
GPT-4o-mini
40
40
50
50
40
GPT-4o
40
40
50
50
40
Swen2.5-1.50
10
10
10
10
10
Qwen2,5 3b
10
10
50
10
10
Llama3 2-1b lb
10
10
10
10
10
Llama3.2-1
10
20
10
10
10
18

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B
Prompt Templates and Examples
B.1 Prompt Template of Note Construction
The prompt template in Note Construction: Pa1
Generate structured analysis of the following content by: Identifying the most salient keywords (focus on nouns, verbs, and key concepts)
1.
2.
Extracting core themes and contextual elements
3. Creating relevant categorical tags
Format the response as a JSON object:
"keywords": // several specific, distinct keywords that capture key concepts and terminology // Order from most to least important Don't include keywords that are the name of the speaker or time // At least three keywords, but don't be too redundant. 1, "context"; // one sentence summarizing: // Main topic/domain // Key arguments/points // Intended audience/purpose
"tags": [ // several broad sategories/thenee for classification // Include domain, format, and type tags // least three tags, but don't be too redundant. ]
}
Content for analysis:
B.2 Prompt Template of Link Generation
The prompt template in Link Generation: P.2
You are an AI memory evolution agent responsible for managing and evolving knowledge base.
Analyze the the new memory note according to keywords and context, also with their several nearest neighbors memory.
The new memory context:
[context] content: {content)
keywords: (keywords}
The nearest neighbors memories: (nearest__neighboss_eemmoo
Based on this information, determine:
Should this memory be evolved? Consider its relationships with other memories.
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B.3 Prompt Template of Memory Evolution
The prompt template in Memory Evolution: Pa3
You are an AI memory evolution agent responsible for managing and evolving knowledge base.
Analyze the the new memory note according to keywords and context, also with their several nearest neighbors memory. Make decisions about its evolution.
The new memory context: [context) content: {content)
keywords: (keywords)
The nearest neighbors memories:
Based on this information, determine:
1. What specific actions should be taken (strengthen,
1.1 If choose to strengthen the connection, which memory should it be connected to? Can you give the updatedd tags of this memory?
1.2 If choose to update neighbor, you update the context and tags of these memories based on the understanding of these memories. Tags should be determined by the content of these characteristic of these memories, which can be used to retrieve them later and categorize them.
All the above information should be returned in a list format according to the sequence: [[new_memory] [neighbor_memog [atibbor_mmmry__]]] ... r.._nnighbor__emae __memmr_m]]]]]__ These actions Return your decision in JSON format with the following structure: {( "should_evolve": true/false,
combined.
"actions": ("strengthen", "merge", "prune"],
cetegrested_connecttie longgs_to_uddatee ["tag_1",, .... "tag_n"], "new_context,
gerighbor_memory.tte
geoo__context_neegbborooooo neighborhood"" ["new context", ...., "new context"],
[["tag_1",.... "tag_n"],.. ""._""""..."tt..111"
["tag_1", ...., "tag_n"i].
}}
20

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B.4 Examples of Q/A with A-MEM
Example:
Question 686: Which hobby did Dave pick in October 2023?
Prediction: photography
Reference: photography
talk start cime:10:54 am on November, 2023
memory content: Speaker Davesays Hey Calvin, long time no talk! lot has happened. I've taken up photography and it's been great been taking pics of the scenery around here which is really cool. memory context: The main topic is the speaker's new hobby of photography, highlighting their enjoyment of capturing local scenery, aimed engaging a friend in conversation about personal experiences.
memory keywords: ('photograhy'', 'scenery', 'covversatoo',, 'experience', 'hobby']
memory tags: ['hobby', 'photography', 'personal development', 'conerrstionn,, 'leisure']
talk start :ime:6:30 pm on 21 July, 2023
memory content: Speaker Calvinsays Thanks, Dave! It feels great having my own space to work in. I've been experimenting with different genres lately, pushing myself out of my comfort zone. Adding electronic elements to my songs gives them a fresh vibe. It's been exciting process of self-discovery and growth!
memory context: The speaker discusses their creative process in music, highlighting experimentation with genres and the incorporation of electronic elements for personal growth and artistic evolution.
memory keywords: ['space', "experinentation', 'genres', 'electronic', "self colf--iiscovry',, 'growth']
memory tags: ['music', 'creativity', 'self iiprrvveentt, 'artistic expression']
21

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28


Reviews:
[{"review_id": "ysnG9vjE3F", "reviewer": "Reviewer_zYJp", "summary": "This paper present agentic memory, A-MEM, inspired by the Zettelkasten method. The agentic memory is composed of:\n1) note construction: record original interaction, keywords, tags, description, etc.\n2) link generation: use cosine similarity to search for the k most relevant memories, then an LLM is prompted to analyse potential common attributions.\n3) memory evolution: relevant memories are evolved when a new memory is added. Allowing the system to learn as it accumulates experience. \nFinally, the paper performs extensive studies, ablation and hyper-parameter sensitivity.", "questions": "* LoCoMo is both a baseline and a dataset?\n* Why is the memory usage the exact same for every method in Table 4? The column might not be necessary since memory usage is a function of memory size.\n* Does average token length in Table 1 includes all the tokens used to generate the meta data during note writing and memory evolution?", "limitations": "yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n\n## Novelty\n* An agentic perspective on memory system is timely and novel. \n\n## Evaluation\n* The datasets LoCoMo and DialSim look well designed to test memory systems.\n* Good ablation studies.  \n* A-mem provides consistent improvement over the baselines.\n\n# Weaknesses\n\n## Expensive\n* The proposed approach sounds expensive in terms of 1) memory since it registers the original interaction content, and 2) LLM calls since the LLM must generate tags and contextual description of each note. Furthermore, the LLM must analyze potential connections between notes based on their potential common attributes. \n\n## Presentation\n* I believe the presentation could be improved. For example, in Figure 2 several terms appear twice: box n+1 and n+2, and memory evolution and evolve. \n* In section 4.4, please include links to Table 3 in the text.\n* Add average number of tokens to Table 3.", "was_revised": true, "final_justification": "The proposed agentic memory system is novel, carefully evaluated and timely. The method also consistently improves over the baseline without being more expensive.", "rebuttal": "Thank you for your positive evaluation of our work. We appreciate your recognition of our key contributions regarding A-MEM, the effectiveness of our Zettelkasten-inspired approach with its three core components, and the comprehensive evaluation demonstrating consistent improvements over baselines. We are grateful for your acknowledgment of the well-designed LoCoMo and DialSim datasets, the thoroughness of our ablation studies, and the novelty of our approach in enabling systems to learn and evolve through accumulated experience. We address the points you raised to further enhance the clarity of our work.\n\n***\n> ***Q1. Whether the approach is expensive because of LLM will be used multiple times for each memory?*** \n\nAs reported in the cost-efficiency analysis in the initial version of the paper, we demonstrate that memory operations using GPT-4o-mini cost less than $0.0003. Here, we provide a detailed analysis of the cost for memory operations. It should be noted that all price calculations are based on OpenAI pricing information from five months ago, and prices may fluctuate over time.\n\nThe analysis of the A-MEM memory system reveals the following key metrics:\n\n- System Prompt Overhead: 363 tokens per operation\n- Average Input Processing: 1,042.00 tokens per memory operation\n- Average Output Generation: 131.00 tokens per memory operation\n- Total Tokens Per Operation: 1,173.00 tokens\n- For per memory storage, using the gpt-4o-mini api costs an average of 5.39 seconds, and using locally-hosted llama3.2 1B model costs an average of 1.12 seconds with a single GPU.\n\nBased on the GPT-4o-mini pricing model (`$`0.150/1M tokens for input; `$`0.600/1M tokens for output):\n\n- **Input Cost Calculation:**\n1,042.00 tokens × (`$`0.150/1M tokens) = `$`0.0001563 per operation\n- **Output Cost Calculation:**\n131.00 tokens × (`$`0.600/1M tokens) = `$`0.0000786 per operation\n- **Total Cost Per Memory Operation:**\n`$`0.0001563 + `$`0.0000786 = `$`0.0002349\n- **Projected Cost at Scale:**\nFor 1 million memory operations:`$`0.0002349 × 1,000,000 = `$`234.90\n\n**Conclusion.** The A-MEM system demonstrates reasonable resource efficiency at approximately `$`0.0002349 per memory operation (`$`234.90 per million operations). Despite requiring multiple LLM calls during memory processing, this cost structure remains economically viable and scales predictably.\n\n\n***\n> ***Q2. Could you please revise Figure 2 to remove duplicate terms like \"box n+1\" and \"evolve\"?***\n\nIn the initial version of our paper, we used the terms \"box n+1\" and \"box n+2\" to indicate that these two boxes are newly added in the link generation process. In the final version, we will revise the figure for better clarity.\n\n***\n> ***Q3. In section 4.4, please include links to Table 3 in the text.***\n\nThank you for the suggestion. We will revise to include hyperlinks to Table 3 in Section 4.4 text. \n\n***\n> ***Q4. Add the average number of tokens to Table 3.***\n\nFor the \"w/o LG & ME\" method, the average token count is 605. For the \"w/o ME\" method, the average token count is 2,371. We will include this information in the final version of our paper.\nNote that the average token count is computed based on the QA test phase, excluding the memory construction phase.\n\n***\n> ***Q5. Can you clarify the role of LoCoMo, as it appears to be used as both a baseline and a dataset?***\n\nLoCoMo [1] provides a dataset and a basic memory evaluation method in their paper. In the initial version of our paper, we used the same abbreviation to represent our method. In the final version, we will revise our method's abbreviation to avoid confusion and improve clarity.\n\n***\n> ***Q6. Why is the memory usage the exact same for every method in Table 4?***\n\nFor fair comparison, we use the same text embedding model across all three memory systems, resulting in the embedding storage occupying the majority of memory usage. We will update Table 4 in the final version of the paper to reflect this clarification.\n\n***\n> ***Q7. Does the average token length in Table 1 include all tokens used for metadata generation during both note writing and memory evolution?***\n\nSince memory construction and QA evaluation are separate processes in the datasets, we do not include the average token cost of memory construction in Table 1. For the memory construction token cost, please refer to the response to Q1.\n\nReference:\n\n[1] Maharana, Adyasha, et al. \"Evaluating very long-term conversational memory of llm agents.\" arXiv preprint arXiv:2402.17753 (2024)."}, {"review_id": "mZLVC8tqQK", "reviewer": "Reviewer_qBs5", "summary": "This paper proposes a novel memory construction method for LLM-based systems, termed A-Mem. Unlike traditional approaches such as RAG, A-Mem offers greater flexibility through dynamic memory organization. New memories can be added, linked to similar historical memories, and used to trigger updates to the contextual representations and attributes of existing ones, enabling the memory network to evolve continuously. An LLM serves as a judge to determine memory similarity and guide the refinement of the memory network. Empirical experiments conducted on six foundation models demonstrate significant improvements over existing state-of-the-art baselines.", "questions": "As noted in the \"Weaknesses\" section, if we already have a sufficiently capable LLM judge with comprehensive prior knowledge, is it still necessary to maintain a separate memory network? Have you conducted any experiments to evaluate the necessity of maintaining such a network under the assumption that a capable LLM judge is already available?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper addresses a significant and timely problem—how to construct memory systems for LLMs—which is a critical research direction in advancing long-term reasoning and context management.\n2. The concept of a dynamically evolving memory structure is intuitive and compelling, as it mirrors the way human memory functions.\n3. The paper not only proposes this idea but also provides an implementation and empirical validation through a series of experiments.\n\nWeaknesses:\nAs acknowledged in the \"Limitations\" section, the efficiency and overall performance of A-Mem heavily rely on the capabilities of the LLM judge, which is responsible for determining memory similarity and guiding the refinement of the memory network. Conceptually, if we already possess a sufficiently capable LLM judge with comprehensive prior knowledge, one might question the necessity of maintaining a separate memory network at all.", "was_revised": true, "final_justification": "The authors' responses have largely addressed my concerns. Therefore, I decided to raise my score to 4.", "rebuttal": "Thank you for your review. We sincerely appreciate your recognition that A-Mem addresses a significant and timely problem in LLM memory construction, which you identified as critical for advancing long-term reasoning. We're grateful you found our dynamically evolving memory structure intuitive and compelling, noting how it mirrors human memory functions. Thank you also for acknowledging our comprehensive approach that provides concrete implementation and empirical validation across six foundation models, demonstrating significant improvements over existing baselines.\n\n***\n> ***Q1. If we already have a sufficiently capable LLM judge with comprehensive prior knowledge, is it still necessary to maintain a separate memory network? Have you conducted any experiments to evaluate the necessity of maintaining such a network under the assumption that a capable LLM judge is already available?***\n\nEven if we have a sufficiently capable LLM judge with comprehensive prior knowledge, memory systems remain essential for LLM agents. We provide a detailed explanation from the following perspectives.\n\n**Necessity of memory system for LLM.** Large language models suffer from fundamental architectural limitations including finite context windows, quadratic attention complexity, and the \"lost in the middle\" problem where performance degrades significantly when accessing information in the middle of long contexts, even for explicitly long-context models [1-2]. Given these existing architectural constraints, it is impractical to feed all past interaction history of agents into LLMs while maintaining optimal performance. Therefore, memory systems enable LLM agents to transcend context window limitations by persistently storing experiences, abstracting knowledge, and supporting self-evolving capabilities for long-term agent-environment interactions [3].\n\n**LLMs in our A-mem.**  In our agentic memory system, we utilize LLMs to organize memory in a Zettelkasten-style storage structure. Additionally, we design a retriever to identify and retrieve similar information when accessing memory. By leveraging both LLMs and retrievers, our A-Mem provides condensed, informative context that enables LLM agents to transcend context window limitations with reasonable token consumption.\n\n**Experiment validation.** In the Table 1-2 of our initial paper version, we conducted experiments comparing our approach against the LoCoMo method, which relies on LLMs to process all context information without memory retrieval. Despite LoCoMo [4] using approximately 7x more tokens than our A-Mem, it still underperforms compared to our method.\n\n**Conclusion.** Due to the inherent limitations of LLM architecture, models cannot process excessively long contexts while maintaining optimal performance. With our A-Mem design, LLM agents can achieve superior performance through efficient memory retrieval mechanisms.\n\n\nReferences:\n\n[1] Liu, Nelson F., et al. \"Lost in the middle: How language models use long contexts.\" arXiv preprint arXiv:2307.03172 (2023).\n\n[2] Kaddour, Jean, et al. \"Challenges and applications of large language models.\" arXiv preprint arXiv:2307.10169 (2023).\n\n[3] Zhang, Zeyu, et al. \"A survey on the memory mechanism of large language model based agents.\" ACM Transactions on Information Systems (2024).\n\n[4] Maharana, Adyasha, et al. \"Evaluating very long-term conversational memory of llm agents.\" arXiv preprint arXiv:2402.17753 (2024)."}, {"review_id": "aOASuK72K0", "reviewer": "Reviewer_3BrE", "summary": "This paper introduces a novel agentic memory system called A-Mem, designed to enhance the performance of Large Language Model (LLM) agents in complex tasks. Drawing principles from the Zettelkasten method, A-Mem overcomes the limitations of existing memory systems, such as fixed structures and adaptability issues, by creating an interconnected knowledge network through dynamic note construction, link generation, and memory evolution. Experiments demonstrate that A-Mem achieves significant improvements in handling long-term conversational tasks compared to existing baselines, particularly excelling in complex tasks like multi-hop reasoning, while also showcasing its cost-effectiveness and scalability.", "questions": "1. Is it reasonable about equation (3)? If it comes from Zettlekasten, how about removing 1 or 2 of the 4 factors? Are the results worse?\n2. Why does A-Mem perform worse in retrieval time than Memory Bank in Table 4?", "limitations": "The potentially biggest issue is that the method proposed in this paper has not been actually applied to existing agents or multi-agent systems (MAS). It's difficult to demonstrate the effectiveness of this method for agents or MAS solely based on the experiments presented in this paper.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Novel idea.\n2. Dynamic memory organization and strong adaptability.\n3. Low resource consumption.\n\nWeaknesses:\n1. It does not offer a better memory retrieval strategy, still relying on the most basic RAG method.\n2. Establishing links between memories solely based on similarity might not be entirely reasonable.\n3. The scaling experiments and hyperparameter experiments do not provide a good impression. The **k** in hyperparameters should arguably be part of scaling, but performance deteriorates when **k** is large; scaling purely on entity number is meaningless, as it simply creates an extremely large document.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive evaluation of our work. We appreciate your recognition of our novel agentic memory system A-Mem, the effectiveness of our Zettelkasten-inspired approach with dynamic note construction, link generation, and memory evolution, and our ability to overcome the limitations of existing memory systems through dynamic memory organization. We are grateful for your acknowledgment of A-Mem's significant improvements in long-term conversational tasks and multi-hop reasoning, its strong adaptability, and its cost-effectiveness with low resource consumption. We address the points you raised to further enhance the clarity of our work.\n\n***\n> ***Q1. Can A-mem provide a better memory retrieval strategy, rather than the most basic RAG method?***\n\nUnlike simple RAG methods, our A-mem approach employs a distinctive memory retrieval mechanism. As illustrated in Figure 2, during the memory retrieval stage, we extract query embeddings using a text encoding model and search the memory database for relevant matches. When related memory is retrieved, similar memories that are linked\nwithin the same box are also automatically accessed.\nWe will revise Section 3.4 for clarity.\n\n***\n> ***Q2. Is establishing links between memories solely based on similarity entirely reasonable?*** \n\n**Design philosophy.** Our approach uses a two-stage mechanism that balances computational efficiency with semantic understanding. First, embedding similarity identifies top-k candidate memories from the existing collection, providing efficient filtering that scales without exhaustive comparisons. Second, an LLM analyzes these candidates to determine meaningful connections based on contextual relationships and semantic content in an agentic way. This hybrid architecture leverages the computational efficiency of vector similarity for initial retrieval while employing LLM reasoning to detect subtle patterns, causal relationships, and conceptual connections that transcend simple cosine similarity. \n\n**Case study.** Case studies from our memory system reveal how the two-stage mechanism captures nuanced connections that pure embedding approaches miss.\n\n**Case 1.** Our system connects two memories from Caroline's adoption journey. The first shows Caroline reaching out: \"Hey Mel, what's up? Long time no see! I just contacted my mentor for adoption advice. I'm ready to be a mom and share my love and family.\" Nine days later, the second captures her progress: \"Woohoo Melanie! I passed the adoption agency interviews last Friday! I'm so excited and thankful.\" This connection captures a clear temporal progression from preparation to achievement, demonstrating a causal relationship and narrative continuity in her adoption process.\n\n**Case 2.** Our system links Caroline's artistic advocacy evolution. It connects her sharing a painting while explaining, \"Representing inclusivity and diversity in my art is important to me. I also use it to speak up for the LGBTQ+ community\" with her announcement eleven days later: \"I'm putting together an LGBTQ art show next month and I'm gonna show my paintings. Super stoked!\" This captures her progression from individual artistic expression to community action, showing how personal practice evolves into public advocacy.\n\nThese connections emerge because our LLM analysis stage recognizes thematic continuity, temporal coherence, and causal relationships that embedding similarity alone cannot capture, successfully identifying meaningful relationships through contextual understanding rather than lexical overlap.\n\n***\n> ***Q3. Why does it not achieve the best performance in scaling experiments compared to memorybank?*** \n\nFor the scaling analysis, we provide a scalability assessment of our memory system. When we increase the memory size, the retrieval time increases minimally rather than intensely. Although retrieval time is slightly higher than MemoryBank [1], this is because our A-mem stores more contextual information to describe each memory node, including keywords, tags, and context. However, the increased time is quite small per request, while our A-mem provides richer memory representations that yield at least a two-fold improvement in LoCoMo comparison experiments. Based on both performance and scaling analysis, we conclude that our A-mem represents a significant advancement in building powerful and long-term memory mechanisms for LLM agents.\n\n***\n> ***Q4. Why does increasing the k parameter not lead to improvement?*** \n\nIn the initial version of the paper, we regard the k value, which controls the number of relevant memories retrieved for each interaction, as a hyperparameter due to its direct influence on the balance between memory coverage and retrieval precision, requiring empirical tuning to achieve optimal performance across different scenarios.\n\nWhen increasing the value of k, the length of input tokens to the LLM increases correspondingly, which may degrade LLM performance. As discussed in prior research [2-3], large language models suffer from fundamental architectural limitations, including finite context windows, quadratic attention complexity, and the \"lost in the middle\" problem, where performance degrades significantly when accessing information in the middle of long contexts, even for explicitly long-context models.\n\nIn the final version of the paper, we will integrate these two analyses into one section for clarity.\n\n***\n> ***Q5. Is it reasonable for equation (3)? If it comes from Zettlekasten, how about removing 1 or 2 of the 4 factors? Are the results worse?*** \n\nFor the vector representation of memory notes, we utilize the attributes of context, content, keywords, and tags, which capture multiple perspectives of each memory note. To validate the effectiveness of this design, we conduct an ablation study by systematically removing each component. Here, $c$ represents the original content, $K$ denotes the keywords, $G$ represents the tags, and $X$ indicates the context. As shown in the table, each component demonstrates its effectiveness in the overall representation, with content and context being particularly impactful.\n\n**Results are reported in F1 and BLEU-1 (%) scores with foundational model GPT-4o-mini.**\n\n| Model | Single Hop |  | Multi Hop |  | Temporal |  | Open Domain |  | Adversial |  |\n|---|---|---|---|---|---|---|---|---|---|---|\n| | F1 | BLEU-1 | F1 | BLEU-1 | F1 | BLEU-1 | F1 | BLEU-1 | F1 | BLEU-1 | F1 | BLEU-1 | \n| -$c$ | 17.20 | 13.10 | 14.84 | 10.93 | 10.73 | 8.57 | 31.54 | 27.06 | 32.08 | 31.34 |\n| -$K$ | 18.76 | 14.36 | 16.15 | 11.98 | 11.56 | 9.37 | 33.68 | 29.35 | 35.04 | 34.07 |\n| -$G$ | 18.76 | 14.28 | 16.22 | 11.88 | 11.51 | 9.36 | 33.83 | 29.28 | 35.03 | 34.02 |\n| -$X$ | 18.12 | 13.74 | 15.48 | 11.35 | 10.97 | 9.01 | 32.37 | 27.94 | 33.43 | 32.41 |\n| **A-MEM** | **19.28** | **14.69** | **16.65** | **12.23** | **11.85** | **9.61** | **34.72** | **30.05** | **35.99** | **34.87** |\n\n***\n> ***Q6. What are the experimental results in the agent systems?*** \n\nTo further demonstrate the effectiveness of our A-mem approach in agent systems, we conduct experiments across two scenarios: QA search and web shopping agents.\n\n**Experiments Setup.** For the QA search task, we use a mixture of data from HotpotQA [4] and Natural Questions [5]. We evaluate performance using exact match accuracy and multi-objective F1 scores, comparing against the LoCoMo baseline [6] under two conditions: with and without token truncation. When multiple ground truths are available, we select the maximum F1 score across all alternatives. For multi-objective tasks, the final F1 score represents the sum of F1 scores across all sub-questions. We also report token usage statistics for all methods.\n\nFor the web shopping scenario, we evaluate on the WebShop [7] benchmark, measuring performance using the reward scores provided by the environment simulator. \n\n**Results Analysis.** As shown in the following table, compared to LoCoMo, our A-mem achieves comparable performance while using only 33%-50% of the token budget, demonstrating its ability to provide more informative memory representations. This efficiency stems from our memory system's capacity to dynamically establish connections between memories based on shared attributes and continuously update existing memory descriptions with new contextual information, enabling better capture and utilization of inter-informational relationships.\n\n**Results on the QA agent task.**\n\n| Method | Foundation Model |EM |F1 | Token ($*10^2$) |\n|---|---|---|---|---|\n| LoCoMo (Truncate) |Qwen2.5-7B  | 0.396 | 0.497 | **13.3** |\n| LoCoMo |Qwen2.5-7B | 0.165 | 0.213 | 43.3 | \n| LoCoMo |Qwen2.5-14B | 0.567 | 0.703 | 38.4 | \n| **A-Mem** |Qwen2.5-7B | **0.730** | **0.961** | 18.8 |\n\n**Results on WebShop task with GPT-4o as foundation model.**\n\n| Method  |Final Reward | Token ($*10^3$) |\n|---|---|---|\n| LoCoMo (Truncate) |13.82  | **0.99** |\n| LoCoMo |  **25.48**  | 5.30 | \n| **A-Mem**  | 24.50 |  1.84 |\n\nReference:\n\n[1] Zhong, Wanjun, et al. \"Memorybank: Enhancing large language models with long-term memory.\" AAAI 2024.\n\n[2] Liu, Nelson F., et al. \"Lost in the middle: How language models use long contexts.\" arXiv:2307.03172 (2023).\n\n[3] Kaddour, Jean, et al. \"Challenges and applications of large language models.\" arXiv:2307.10169 (2023).\n\n[4] Yang, Zhilin, et al. \"HotpotQA: A dataset for diverse, explainable multi-hop question answering.\" arXiv:1809.09600.\n\n[5] Kwiatkowski, Tom, et al. \"Natural questions: a benchmark for question answering research.\" TACL (2019).\n\n[6] Maharana, Adyasha, et al. \"Evaluating very long-term conversational memory of llm agents.\" arXiv:2402.17753 (2024).\n\n[7] Yao, Shunyu, et al. \"Webshop: Towards scalable real-world web interaction with grounded language agents.\" NIPS 2022."}, {"review_id": "OhjjKkc6Aq", "reviewer": "Reviewer_rgsn", "summary": "This paper proposes A-MEM, an agentic memory system for LLM agents inspired by the Zettelkasten method. It structures each interaction as a contextualized note with semantic attributes and automatically establishes links to relevant past memories. New memories can trigger updates to existing ones, enabling dynamic memory evolution. Experiments on long-term dialogue datasets demonstrate that A-MEM outperforms existing baselines, particularly in multi-hop reasoning tasks, while maintaining high efficiency and scalability.", "questions": "1. In the Methodology section, it remains unclear whether the interaction between the environment and the LLM agent is equivalent to a query and the LLM’s response. If these processes are indeed the same, it would be helpful to unify the terminology and revise Figure 2 accordingly to avoid potential confusion for readers.\n2. Does the proposed method update the memory after every single interaction? If many interactions do not meaningfully impact the memory, could forcing updates via the LLM lead to degraded performance? What are the advantages of using the LLM for memory evolution compared to alternative approaches, and are there potential negative effects? \n3. In the Scaling Analysis, does the reported runtime include the time required for memory evolution? If so, providing a breakdown of the timing for each component would enhance the credibility of the efficiency claims. If not, this omission might render the comparison unfair. It would be beneficial to include an analysis of how the retrieval time scales with the hyperparameter k.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths: \n1. Originality and Significance: The proposed A-MEM method is both novel and innovative, introducing a new memory paradigm characterized by a concise structure and well-motivated design. Compared to existing approaches, it notably supports evolutionary memory capabilities.\n2. Quality: The experiments are comprehensive, involving multiple baselines, diverse evaluation metrics, varied dataset types, and different generative models. Furthermore, the authors provide the experiment of computational efficiency, strengthening the practical relevance of the approach.\n3. Clarity: The manuscript is well-written and accessible, facilitating reader comprehension.\n\nWeaknesses:\n1. Clarity: The description of Methodolodgy Section lacks completeness and clarity in certain aspects. Specific concerns are detailed in the questions 1 and 2.\n2. Quality:  For Experiment Section：\n1) The Scaling Analysis and Memory Analysis sections are not sufficiently convincing. In particular, the Scaling Analysis suffers from the issues highlighted in question 3. The description of Memory Analysis is somewhat vague, raising doubts about the claim that “A-MEM can autonomously maintain meaningful memory structures.”\n2) Apart from the main experiments with LOCOMO, other experiments do not consistently compare against all four baselines, including the primary experiment on the DialSim dataset.\n3) Many experiments lack in-depth analysis explaining the reasons behind the model’s superior performance.\n3. Significance：The use of a same encoder for structurally different memory and query inputs appears overly simplistic, suggesting that the model design does not adequately account for the heterogeneity of these content.", "was_revised": true, "final_justification": "Good paper, after rebuttal, several issues have been addressed.", "rebuttal": "Thank you for your thoughtful and comprehensive evaluation of our work. We are deeply grateful for your recognition of A-Mem's originality and significance. We greatly appreciate your positive assessment of our comprehensive evaluation with multiple baselines, diverse metrics, and computational efficiency analysis. We are also thankful for your recognition of the manuscript's clarity and accessibility.\n\n***\n> ***Q1. Whether the interaction between the environment and the LLM agent is equivalent to a query and the LLM’s response?*** \n\nIn Figure 2, the interaction between the environment and LLM agents is not equivalent to a query and the LLM's response. A chatbot, such as GPT and Claude, is one type of LLM agent that has more datasets to evaluate. However, our A-mem can be used in any kind of agent scenarios, such as coding LLM agents, embodied L/VLM agents, etc.\n\nThe environment represents the external world or task domain that the agent operates within, providing observations, rewards, and feedback based on the agent's actions. An interaction encompasses the complete cycle of the agent perceiving the environment state, taking an action, and receiving feedback or rewards [1]. Our A-mem serves a dual purpose: it records the entire process of these interactions as contextualized memory notes, and simultaneously provides relevant historical context information during each subsequent interaction to inform better decision-making.\n\nHowever, due to the limitations of available long-term memory datasets, we primarily evaluate our approach using chatbot agent scenarios in LoCoMo and DialSum. To further demonstrate the effectiveness of our A-mem approach in agent systems, we conduct experiments across two scenarios: QA search and web shopping agents. As shown in the following table, compared to LoCoMo, our A-mem achieves comparable performance while using only 33%-50% of the token budget, demonstrating its ability to provide more informative memory representations. The full experiment details can be found in the response to Reviewer 3BrE Q6.\n\n\n***\n> ***Q2. Does the proposed method update the memory after every single interaction? Whether forcing updates via the LLM lead to degraded performance in some situations? What are the advantages of using the LLM for memory evolution?*** \n\n**Storage Mechanism.** The memory storage strategy can be designed to store and update memory at different frequencies. If LLM agents prefer coarse-grained memories (to save tokens and reduce computational overhead), the saving frequency can be set to a lower rate, such as at the episode level or after multiple sessions, rather than at each individual session.\n\n**Update Control.**  The memory evolution process operates in two stages: first, retrieving the top-k most similar memories based on text embedding similarity, then updating them via LLMs. The retrieval process is governed by a similarity threshold that determines which memories qualify for updates. By adjusting this threshold, we can control the frequency and scope of memory evolution—a higher threshold results in fewer, more selective updates, while a lower threshold allows more frequent updates across a broader range of memories.\n\nIn our benchmark evaluations, most memories exhibit strong interconnections, so we did not impose a retrieval threshold. However, for real-world deployment where memory relationships may be more sparse or varied, the retrieval threshold serves as a crucial hyperparameter. By setting an appropriate threshold, A-mem can avoid updating irrelevant or weakly related memories, thereby preventing potentially harmful updates that could degrade memory quality.\n\n**Advantage.** Leveraging LLMs for memory updates allows the existing memory repository to continuously adapt and form associations with new memories, thereby capturing scenario-specific information more accurately. Additionally, the inherent pre-training capabilities of LLMs enable robust memory updating across diverse, general-purpose contexts.\n\n\n***\n> ***Q3. What is the time for each part of constructing memory? Could you provide an analysis of how the retrieval time scales with the hyperparameter k?*** \n\nFor link generation and memory evolution, these two operations, the time cost includes two parts including the LLM calls and the retrieval time. For the LLM calls using GPT-4o-mini, the time is an average of 2.4s, which has a strong relation to your api level. For the retrieval time, it can refer to Table 4 in our paper. \n\nBesides, we investigate the retrieval time scales with various k values. The retrieval time remains constant with respect to k due to fundamental algorithmic properties. The retrieval operation consists of four sequential components with distinct complexity characteristics. Query encoding operates at $O(1)$ with respect to k since the query embedding is computed once regardless of k. Similarity computation requires $O(N)$ time with respect to k, as cosine similarity must be computed against the entire corpus of N documents. Global sorting operates at $O(N \\log N)$ with respect to k since the similarity scores for all N documents require sorting. Finally, top-k selection requires $O(k)$ time with respect to k for extracting the k highest-scoring results.\n\nThe computational bottleneck lies in the first three steps, which are invariant to k. Similarity computation dominates runtime as it requires $O(N)$ dot products between query and document embeddings. Sorting complexity depends on corpus size N, not retrieval size k. Top-k selection contributes negligible overhead, typically less than 0.1% of total time. Therefore, the total retrieval time follows:\n\n$$\\text{Total Time} \\approx T_{\\text{embedding}} + T_{\\text{similarity}} + T_{\\text{sorting}} + \\varepsilon(k)$$\n\nwhere $\\varepsilon(k)$ is asymptotically negligible compared to the N-dependent terms. The key insight is that retrieval systems evaluate the entire corpus regardless of k value, with only the final extraction step scaling with k. This makes k selection performance-neutral, allowing optimization based on task requirements rather than computational constraints, since throughput depends on corpus size N rather than result set size k.\n\n\n***\n> ***Q4. Other experiments do not consistently compare against all four baselines in DialSim.*** \n\nWe provide additional experimental results from other baselines on the DialSim dataset. Across six evaluation metrics, we can conclude that our A-mem achieves the best performance.\n\n| Method | F1 | BLEU-1 | ROUGE-L | ROUGE-2 | METEOR | SBERT Similarity |\n|---|---|---|---|---|---|---|\n| ReadAgent | 2.38 | 3.04 | 2.20 | 0.17 | 1.37 | 17.77 |\n| MemoryBank | 1.85 | 3.17 | 1.72 | 0.23 | 1.03 | 9.93 | \n| **A-Mem** | **3.45** | **3.37** | **3.54** | **3.60** | **2.05** | **19.51** |\n\n***\n> ***Q5. In-depth analysis explaining the reasons behind the model’s superior performance.*** \n\nOur A-Mem system demonstrates superior performance by identifying meaningful semantic connections between different memories. Case studies from our memory system reveal how the two-stage mechanism captures nuanced connections that pure embedding approaches miss.\n\n**Case 1.** Our system connects two memories from Caroline's adoption journey. The first shows Caroline reaching out: \"Hey Mel, what's up? Long time no see! I just contacted my mentor for adoption advice. I'm ready to be a mom and share my love and family.\" Nine days later, the second captures her progress: \"Woohoo Melanie! I passed the adoption agency interviews last Friday! I'm so excited and thankful.\" This connection captures a clear temporal progression from preparation to achievement, demonstrating a causal relationship and narrative continuity in her adoption process.\n\n**Case 2.** Our system links Caroline's artistic advocacy evolution. It connects her sharing a painting while explaining \"Representing inclusivity and diversity in my art is important to me. I also use it to speak up for the LGBTQ+ community\" with her announcement eleven days later: \"I'm putting together an LGBTQ art show next month and I'm gonna show my paintings. Super stoked!\" This captures her progression from individual artistic expression to community action, showing how personal practice evolves into public advocacy.\n\nThese connections emerge because our LLM analysis stage recognizes thematic continuity, temporal coherence, and causal relationships that embedding similarity alone cannot capture, successfully identifying meaningful relationships through contextual understanding rather than lexical overlap.\n\n***\n> ***Q6. The use of the same encoder for structurally different memory and query inputs appears overly simplistic, which may overlook the heterogeneity of this content.*** \n\nOur approach is simple yet effective. Using a unified text embedder provides universal applicability with strong generalization—modern embedders trained on massive heterogeneous datasets demonstrate superior performance across diverse domains without requiring manual feature engineering [2]. Their excellent transfer learning capabilities enable broad applicability across real-world scenarios, providing the flexibility essential for practical deployment [3].\n\nWhile future work could explore specialized architectures like graph neural networks to capture additional structural information, our current design strikes an optimal balance between strong performance, broad applicability, and ease of deployment.\n\nReferences:\n\n[1] Sutton, R. S., & Barto, A. G. (2018). The agent-environment interface. In Reinforcement learning: An introduction (Chapter 3.1). MIT press.\n\n[2] Wang, Liang, et al. \"Text embeddings by weakly-supervised contrastive pre-training.\" arXiv preprint arXiv:2212.03533 (2022).\n\n[3] Cer, Daniel, et al. \"Universal sentence encoder.\" arXiv preprint arXiv:1803.11175 (2018)."}]

Author rebuttal:

Meta-review: This paper presents A-MEM for agentic memory inspired by Zettelkasten. It consists of note construction, link generation, and memory evolution. Reviewers noted the originality of the contribution and the importance of the problem, as well as the quality of the experiments, which are comprehensive (4 baselines, multiple tasks measured via both F1 and BLEU scores + ablations, scaling, memory). After discussion, all reviewers support accepting the paper.
Decision: Accept (poster)