# Are We Ready For An Agent-Native Memory System? arXiv: https://arxiv.org/abs/2606.24775 ยท HTML: https://mlsanity.com/paper/2606.24775/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. - Builds on 16 of its 36 references that are in the map. - First posted on arXiv 23 Jun 2026. ## Placed in - Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/) - LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/) ## Builds on (most cited first, 16 of 16) - [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023) - [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023) - [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025) - [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024) - [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025) - [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025) - [Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions](https://mlsanity.com/paper/2507.05257/) (2025) - [MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent](https://mlsanity.com/paper/2507.02259/) (2025) - [MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents](https://mlsanity.com/paper/2506.15841/) (2025) - [SimpleMem: Efficient Lifelong Memory for LLM Agents](https://mlsanity.com/paper/2601.02553/) (2026) - [MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents](https://mlsanity.com/paper/2506.21605/) (2025) - [From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs](https://mlsanity.com/paper/2410.14052/) (2024) - [Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers](https://mlsanity.com/paper/2603.07670/) (2026) - [Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First](https://mlsanity.com/paper/2509.00997/) (2025) - [Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework](https://mlsanity.com/paper/2604.01707/) (2026) - [Automating Database-Native Function Code Synthesis with LLMs](https://mlsanity.com/paper/2604.06231/) (2026) ## Built on by (most cited first, 15 of 15) - [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026) - [AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction](https://mlsanity.com/paper/2607.21106/) (2026) - [A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory](https://mlsanity.com/paper/2607.01935/) (2026) - [LightMem-Ego: Your AI Memory for Everyday Life](https://mlsanity.com/paper/2607.11487/) (2026) - [Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings](https://mlsanity.com/paper/2607.21962/) (2026) - [The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory](https://mlsanity.com/paper/2608.22767/) (2026) - [Delivery, Not Storage: Cue-Anchored Working Memory as a Harness Property for Coding Agents](https://mlsanity.com/paper/2607.20972/) (2026) - [Why Git Is the Memory Solution for the Agentic Development Lifecycle](https://mlsanity.com/paper/2607.14390/) (2026) - [DreamBench-SWE: A Multi-Session Memory-Hygiene Benchmark for Software Agents](https://mlsanity.com/paper/2608.20664/) (2026) - [Vestrum: Improving Agent Harnesses by Adapting Their Verification, Structure and Memory](https://mlsanity.com/paper/2609.33822/) (2026) - [MemCodex: Self-Programming Hierarchical Memory for Language Agents](https://mlsanity.com/paper/2609.39765/) (2026) - [Hallucination Across the Reasoning Lifecycle: Interface Visibility, Causal Evidence, and Release Control in Large Reasoning Models](https://mlsanity.com/paper/2610.05472/) (2026) - [Bookkeeping, Composition, or Unreachable Gold? Reading MemoryAgentBench's Conflict-Resolution Scores Against a Frozen Last-Write Resolver](https://mlsanity.com/paper/2610.09193/) (2026) - [Stale, Misattributed, or Late: Where Personal Memory Fails Before Generation](https://mlsanity.com/paper/2610.10265/) (2026) - [Recall What Is Missing: Composing Evidence from Long-Term Agent Memory](https://mlsanity.com/paper/2610.15642/) (2026) ## Abstract (arXiv) Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolution, existing evaluations still benchmark agent memory mainly through end-to-end task success metrics (e.g., F1, BLEU), while treating the underlying system as a monolithic black box. As a result, critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored. In this paper, we present a systematic experimental study of agent memory from a data management perspective. We propose an analytical framework that decomposes agent memory into four core modules: memory representation and storage, extraction, retrieval and routing, and maintenance. Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets. Our extensive end-to-end evaluation shows that no single architecture dominates across all scenarios; instead, effectiveness depends heavily on how well the memory structure aligns with the workload bottleneck. Furthermore, through fine-grained ablation studies, we quantify their individual effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability. Finally, we reveal cost-performance trade-offs under realistic workloads, showing localized maintenance is more cost-efficient than global reorganization. Based on these findings, we identify promising directions towards building truly agent-native memory systems. The code is publicly available at https://github.com/OpenDataBox/MemoryData.