# Memori: A Persistent Memory Layer for Efficient, Context-Aware LLM Agents

arXiv: https://arxiv.org/abs/2603.19935 · HTML: https://mlsanity.com/paper/2603.19935/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 9 of 9)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://mlsanity.com/paper/2305.10250/) (2023)
- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [MemR3: Memory Retrieval via Reflective Reasoning for LLM Agents](https://mlsanity.com/paper/2512.20237/) (2025)

## Built on by (most cited first, 12 of 12)

- [MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents](https://mlsanity.com/paper/2605.18652/) (2026)
- [Structured Belief State and the First Precision-Aware Benchmark for LLM Memory Retrieval](https://mlsanity.com/paper/2605.11325/) (2026)
- [Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution](https://mlsanity.com/paper/2608.08311/) (2026)
- [From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work](https://mlsanity.com/paper/2605.06365/) (2026)
- [Mnemon: Raw Records, Fast Judgments, Slow Thoughts](https://mlsanity.com/paper/2609.36059/) (2026)
- [Large Language Models Do Not Always Need Readable Language](https://mlsanity.com/paper/2606.19857/) (2026)
- [Profile-Graph Memory for LLM Agents: Implicit Cross-Entity Traversal through Narrative Profiles](https://mlsanity.com/paper/2607.19359/) (2026)
- [Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents](https://mlsanity.com/paper/2608.15008/) (2026)
- [Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability](https://mlsanity.com/paper/2609.05339/) (2026)
- [Intermediate Artifacts as First-Class Citizens: A Data Model for Durable Intermediate Artifacts in Agentic Systems](https://mlsanity.com/paper/2605.12087/) (2026)
- [MemArena: An Ego-Centric Benchmark for On-Device Agentic Personal Memory Assistants at Scale](https://mlsanity.com/paper/2608.02613/) (2026)
- [Compact-Memory LLM Agents via Online Max-Member Clustering and Atom-Aware Packing](https://mlsanity.com/paper/2609.04915/) (2026)

## Abstract (arXiv)

As large language models (LLMs) evolve into autonomous agents, persistent memory at the API layer is essential for enabling context-aware behavior across LLMs and multi-session interactions. Existing approaches force vendor lock-in and rely on injecting large volumes of raw conversation into prompts, leading to high token costs and degraded performance. We introduce Memori, an LLM-agnostic persistent memory layer that treats memory as a data structuring problem. Its Advanced Augmentation pipeline converts unstructured dialogue into compact semantic triples and conversation summaries, enabling precise retrieval and coherent reasoning. Evaluated on the LoCoMo benchmark, Memori achieves 81.95% accuracy, outperforming existing memory systems while using only 1,294 tokens per query (~5% of full context). This results in substantial cost reductions, including 67% fewer tokens than competing approaches and over 20x savings compared to full-context methods. These results show that effective memory in LLM agents depends on structured representations instead of larger context windows, enabling scalable and cost-efficient deployment.