Pith Number

pith:R6ZKJWE7

pith:2024:R6ZKJWE74PMRR22HFVQVUHOBSI

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A Survey on the Memory Mechanism of Large Language Model based Agents

Chen Ma, Jieming Zhu, Ji-Rong Wen, Quanyu Dai, Rui Li, Xiaohe Bo, Xu Chen, Zeyu Zhang, Zhenhua Dong

Memory mechanisms let LLM-based agents handle long-term interactions by storing and retrieving information beyond single prompts.

arxiv:2404.13501 v1 · 2024-04-21 · cs.AI

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies.

C2weakest assumption

That the reviewed papers are representative of the field and that the proposed categorization successfully abstracts common designing patterns that will guide future work.

C3one line summary

A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.

References

173 extracted · 173 resolved · 30 Pith anchors

[1] ChatDev: Communicative Agents for Software Development 2023 · arXiv:2307.07924

[2] S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents 2023 · arXiv:2307.14984

[3] A Survey on Large Language Model based Autonomous Agents 2023 · arXiv:2308.11432

[4] The Rise and Potential of Large Language Model Based Agents: A Survey 2023 · arXiv:2309.07864

[5] Reflexion: Language agents with verbal reinforcement learning 2023

Formal links

1 machine-checked theorem link

Cited by

38 papers in Pith

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Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Receipt and verification

First computed	2026-05-17T23:38:53.154365Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8fb2a4d89fe3d918eb472d615a1dc1922285faca18c3bb582d615ab146bc6c45

Aliases

arxiv: 2404.13501 · arxiv_version: 2404.13501v1 · doi: 10.48550/arxiv.2404.13501 · pith_short_12: R6ZKJWE74PMR · pith_short_16: R6ZKJWE74PMRR22H · pith_short_8: R6ZKJWE7

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/R6ZKJWE74PMRR22HFVQVUHOBSI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8fb2a4d89fe3d918eb472d615a1dc1922285faca18c3bb582d615ab146bc6c45

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "35d6186f47d8566753e99b1afe1b3294be081e8454ebbf9675329240423d47e7",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2024-04-21T01:49:46Z",
    "title_canon_sha256": "251c5dcbf318b3c2aabf28fbdc7f9e582948d24feecf4438123696dab00a0f95"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2404.13501",
    "kind": "arxiv",
    "version": 1
  }
}