# Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents

arXiv: https://arxiv.org/abs/2308.01542 · HTML: https://mlsanity.com/paper/2308.01542/

## Key facts

- Cited by 36 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 13 of its 25 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Placed in

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

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [Long Time No See! Open-Domain Conversation with Long-Term Persona Memory](https://mlsanity.com/paper/2203.05797/) (2022)
- [Keep Me Updated! Memory Management in Long-term Conversations](https://mlsanity.com/paper/2210.08750/) (2022)
- [Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://mlsanity.com/paper/2204.08128/) (2022)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [Evaluating the Interpretability of Generative Models by Interactive Reconstruction](https://mlsanity.com/paper/2102.01264/) (2021)
- [Prompt Middleware: Mapping Prompts for Large Language Models to UI Affordances](https://mlsanity.com/paper/2307.01142/) (2023)

## Built on by (most cited first, 30 of 36)

- [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)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming](https://mlsanity.com/paper/2310.06500/) (2023)
- [Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks](https://mlsanity.com/paper/2408.03615/) (2024)
- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [LLMR: Real-time Prompting of Interactive Worlds using Large Language Models](https://mlsanity.com/paper/2309.12276/) (2023)
- [TravelAgent: An AI Assistant for Personalized Travel Planning](https://mlsanity.com/paper/2409.08069/) (2024)
- [CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data](https://mlsanity.com/paper/2409.13903/) (2024)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory](https://mlsanity.com/paper/2508.04903/) (2025)
- [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026)
- [LARP: Language-Agent Role Play for Open-World Games](https://mlsanity.com/paper/2312.17653/) (2023)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory](https://mlsanity.com/paper/2409.15084/) (2024)
- [Affordable Generative Agents](https://mlsanity.com/paper/2402.02053/) (2024)
- [HiMA-Ecom: Enabling Joint Training of Hierarchical Multi-Agent E-commerce Assistants](https://mlsanity.com/paper/2506.19846/) (2025)
- [RedDebate: Safer Responses Through Multi-Agent Red Teaming Debates](https://mlsanity.com/paper/2506.11083/) (2025)
- [DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent Behaviors](https://mlsanity.com/paper/2602.05446/) (2026)
- [Position: Stop Acting Like Language Model Agents Are Normal Agents](https://mlsanity.com/paper/2502.10420/) (2025)
- [AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot](https://mlsanity.com/paper/2504.04299/) (2025)
- [Carbon and Silicon, Coexist or Compete? A Survey on Human-AI Interactions in Agent-based Modeling and Simulation](https://mlsanity.com/paper/2502.18145/) (2025)
- [Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM](https://mlsanity.com/paper/2404.02402/) (2024)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [LUMINA: Long-horizon Understanding for Multi-turn Interactive Agents](https://mlsanity.com/paper/2601.16649/) (2026)
- [AdMem: Advanced Memory for Task-solving Agents](https://mlsanity.com/paper/2606.06787/) (2026)

## Abstract (arXiv)

The recent advent of large language models (LLM) has resulted in high-performing conversational agents such as chatGPT. These agents must remember key information from an ongoing conversation to provide responses that are contextually relevant to the user. However, these agents have limited memory and can be distracted by irrelevant parts of the conversation. While many strategies exist to manage conversational memory, users currently lack affordances for viewing and controlling what the agent remembers, resulting in a poor mental model and conversational breakdowns. In this paper, we present Memory Sandbox, an interactive system and design probe that allows users to manage the conversational memory of LLM-powered agents. By treating memories as data objects that can be viewed, manipulated, recorded, summarized, and shared across conversations, Memory Sandbox provides interaction affordances for users to manage how the agent should `see' the conversation.