# SGMem: Sentence Graph Memory for Long-Term Conversational Agents

arXiv: https://arxiv.org/abs/2509.21212 · HTML: https://mlsanity.com/paper/2509.21212/

## Key facts

- Cited by 29 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 25 of its 41 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Placed in

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

## Builds on (most cited first, 25 of 25)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://mlsanity.com/paper/2305.10250/) (2023)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [From RAG to Memory: Non-Parametric Continual Learning for Large Language Models](https://mlsanity.com/paper/2502.14802/) (2025)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Memory OS of AI Agent](https://mlsanity.com/paper/2506.06326/) (2025)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)
- [From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs](https://mlsanity.com/paper/2504.15965/) (2025)
- [Hello Again! LLM-powered Personalized Agent for Long-term Dialogue](https://mlsanity.com/paper/2406.05925/) (2024)
- [Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG](https://mlsanity.com/paper/2410.05983/) (2024)
- [In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents](https://mlsanity.com/paper/2503.08026/) (2025)
- [On Memory Construction and Retrieval for Personalized Conversational Agents](https://mlsanity.com/paper/2502.05589/) (2025)
- [How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior](https://mlsanity.com/paper/2505.16067/) (2025)
- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents](https://mlsanity.com/paper/2505.19549/) (2025)
- [MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation](https://mlsanity.com/paper/2501.06713/) (2025)
- [KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2412.05547/) (2024)

## Built on by (most cited first, 29 of 29)

- [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026)
- [A Simple Yet Strong Baseline for Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2511.17208/) (2025)
- [Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations](https://mlsanity.com/paper/2602.19320/) (2026)
- [Self-Improvements in Modern Agentic Systems: A Survey](https://mlsanity.com/paper/2607.13104/) (2026)
- [HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents](https://mlsanity.com/paper/2601.06377/) (2026)
- [Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning](https://mlsanity.com/paper/2601.04726/) (2026)
- [TeleMem: Building Long-Term and Multimodal Memory for Agentic AI](https://mlsanity.com/paper/2601.06037/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents](https://mlsanity.com/paper/2512.23343/) (2025)
- [MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution](https://mlsanity.com/paper/2603.18718/) (2026)
- [Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory](https://mlsanity.com/paper/2601.01280/) (2026)
- [RGMem: Renormalization Group-inspired Memory Evolution for Language Agents](https://mlsanity.com/paper/2510.16392/) (2025)
- [GSEM: Graph-based Self-Evolving Memory for Experience Augmented Clinical Reasoning](https://mlsanity.com/paper/2603.22096/) (2026)
- [ES-Mem: Event Segmentation-Based Memory for Long-Term Dialogue Agents](https://mlsanity.com/paper/2601.07582/) (2026)
- [HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling](https://mlsanity.com/paper/2602.13933/) (2026)
- [MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models](https://mlsanity.com/paper/2605.28009/) (2026)
- [SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory](https://mlsanity.com/paper/2605.12061/) (2026)
- [PGMem: Tightly Coupled Persona-Memory Graph for Lifelong Personalized Agents](https://mlsanity.com/paper/2608.01708/) (2026)
- [MemAdapter: Fast Alignment across Agent Memory Paradigms via Generative Subgraph Retrieval](https://mlsanity.com/paper/2602.08369/) (2026)
- [G-Long: Graph-Enhanced Memory Management for Efficient Long-Term Dialogue Agents](https://mlsanity.com/paper/2606.13115/) (2026)
- [Towards Root Memories: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs](https://mlsanity.com/paper/2606.23283/) (2026)
- [Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents](https://mlsanity.com/paper/2608.15008/) (2026)
- [AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs](https://mlsanity.com/paper/2608.26004/) (2026)
- [Proactive Memory for Ad-Hoc Recall over Streaming Dialogues](https://mlsanity.com/paper/2603.04885/) (2026)
- [Improving Multi-turn Dialogue Consistency with Self-Recall Thinking](https://mlsanity.com/paper/2605.15102/) (2026)
- [Experience Memory Graph: One-Shot Error Correction for Agents](https://mlsanity.com/paper/2607.13884/) (2026)
- [FTA-Mem: Fact-Time-Affect Anchored Memory for Low-Density Long-Term Dialogue](https://mlsanity.com/paper/2608.16303/) (2026)
- [Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation](https://mlsanity.com/paper/2609.08599/) (2026)
- [Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents](https://mlsanity.com/paper/2610.11920/) (2026)

## Abstract (arXiv)

Long-term conversational agents require effective memory management to handle dialogue histories that exceed the context window of large language models (LLMs). Existing methods based on fact extraction or summarization reduce redundancy but struggle to organize and retrieve relevant information across different granularities of dialogue and generated memory. We introduce SGMem (Sentence Graph Memory), which represents dialogue as sentence-level graphs within chunked units, capturing associations across turn-, round-, and session-level contexts. By combining retrieved raw dialogue with generated memory such as summaries, facts and insights, SGMem supplies LLMs with coherent and relevant context for response generation. Experiments on LongMemEval and LoCoMo show that SGMem consistently improves accuracy and outperforms strong baselines in long-term conversational question answering.