Llm agent memory. Supports data connectors such as Google Drive, Notion, GitHub, Slack, email. LLM Agents LLM based agents, hereinafter also referred to as LLM agents for short, involve LLM applications that can execute complex tasks through the use of an architecture that combines LLMs with key modules like planning and memory. Nov 28, 2024 · LLM agents can learn and improve in two ways: by adjusting their internal parameters (through model fine-tuning) or by recording important information in a long-term memory that can be retrieved Dec 14, 2024 · What are the benefits of using Agent Memory in AI for LLM applications? Some of the benefits of using Agent Memory in AI for LLM applications include improved efficiency, faster data access speeds, reduced latency, and increased scalability. User can ask in natural language to Feb 5, 2024 · We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Jul 7, 2025 · Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks. The key ideas behind the MemGPT paper, the two tiers of memory in and outside the context window, and how agent states comprised of memory, tools, and messages are turned into prompts. The key component to support agent-environment Dec 17, 2024 · Memory plays a pivotal role in enabling large language model~(LLM)-based agents to engage in complex and long-term interactions, such as question answering (QA) and dialogue systems. However, these unstructured memory representations do not facilitate the reasoning and planning essential for complex decision-making. Jun 9, 2025 · Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences. Overall, “A Jul 2, 2025 · Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Memory. Mar 25, 2025 · The paper “A-MEM: Agentic Memory for LLM Agents” presents a novel memory system designed to equip large language model (LLM) agents with long-term, adaptive memory capabilities. Letta, an open-source framework that adds memory to your LLM agents, giving them advanced reasoning capabilities and transparent long-term memory. We systematically analyze evaluation benchmarks and frameworks across four critical Mar 11, 2025 · LLM agents are advanced AI systems that use planning, memory, and tools to solve complex language tasks with context-aware reasoning. RAISE, an enhancement of the ReAct framework, incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in Jan 22, 2024 · In this paper, we provide a review of the current efforts to develop LLM agents, which are autonomous agents that leverage large language models. Dec 22, 2023 · The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links. Our project introduces an innovative Agentic Memory system that revolutionizes how LLM agents manage and utilize their memories: Jan 18, 2025 · When building an LLM agent to accomplish a task, effective memory management is crucial, especially for long and multi-step objectives. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them: Memory in LLMChain Custom Agents In order to add a memory to an agent we are going to perform the following steps: We are going to create an LLMChain with memory. However, the growing memory size and need for semantic structuring pose significant challenges. A-MEM: Agentic Memory for LLM Agents. One crucial aspect of these agents is their long-term memory, which is often implemented using vector databases. While basic memory might simply involve recalling previous interactions, advanced memory systems enable agents to learn and improve over time, adapting their behavior based on accumulated experience. Jan 13, 2025 · Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. Q4_K_M. This paper investigates how memory structures and memory Jan 5, 2024 · This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 into conversational agents. The fundamental process for each agent to reect and respond involves the concept of short-term and long-term memory. In the provided example we used OpenAI LLM with function calls to create this agent. While existing retrieval-augmented generation (RAG) frameworks for large language model Feb 10, 2025 · Many biological systems solve these challenges with episodic memory, which supports single-shot learning of instance-specific contexts. Feb 6, 2024 · Deep dive into various types of Agent Memory STM: Working memory (LLM Context): It is a data structure with multiple parts which are usually represented with a prompt template and relevant variables. Feb 17, 2025 · While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. For completing the task, agents make use of two key components: (i) LLM Model Jul 10, 2025 · These results show that MIRIX sets a new performance standard for memory-augmented LLM agents. Various memory management techniques are available, Learn how to build agentic memory into your applications in this short course, LLMs as Operating Systems: Agent Memory, created in partnership with Letta, and taught by its founders Charles Packer and Sarah Wooders. Dur- ing inference, agent Liconditions its responses on both short and long-term memories, paralleling how humans remember recent conversations while also recalling distilled important experiences from long-term memory. Instead of flat blob memory, A-MEM uses a structured knowledge network of “memory notes” (inspired by Zettelkasten) where each new memory is stored with descriptors and linked bidirectionally to related past memories. In this paper, we conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. In this paper, we conduct an empirical study on how memory management choices impact the LLM agents' behavior, especially their long-term performance. To extract private in-formation from memory, we propose an effec-tive attacking prompt design and an automated prompt generation method based on different levels of knowledge about the LLM agent. MemGPT, for instance, uses modular memory layers to store and retrieve data dynamically. Nov 23, 2023 · View a PDF of the paper titled FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design, by Yangyang Yu and 8 other authors Memory EXTRaction Attack (MEXTRA) un-der a black-box setting. Jun 10, 2024 · What is interesting is that in a multi agent system, where each agent operates with a lighter LLM for task planning, actions from multiple agent plans can be summarized and shared via gossipping. Apr 15, 2025 · How Semantic Memory Works in LLM Agents There are three main architectural patterns for implementing semantic memory in LLM agents: 1. Hierarchical Memory Systems Future agents may implement hierarchical memory structures that automatically abstract general principles from specific episodes, creating increasingly sophisticated semantic memory over time. Additionally, Zep excels in more comprehensive and challenging evaluations than DMR that better reflect real-world enterprise use cases. We review the current efforts to develop LLM agents, describe their use of vector databases for long-term memory, identify open problems in using vector databases as long-term memory, and propose topics for future work. These notes — containing context This repository enables the large language model to use long-term memory through a vector database (This method is called RAG (Retrieval Augmented Generation) — this is a technique that allows LLM to retrieve facts from an external database). gguf (using LLAMA_cpp_python binding) and chromadb. This limitation restricts their ability to retain and utilize extensive context from previous interactions. In this work Apr 21, 2024 · A comprehensive survey on the memory mechanism of LLM-based agents is proposed and many agent applications, where the memory module plays an important role are presented, where the memory module plays an important role. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems. Apply memory management to create adaptive, collaborative AI agents for real-world tasks like research and HR. To allow users to experience our memory system, we provide a packaged application powered by MIRIX. Jul 5, 2024 · Current LLM-based agents process past experiences using a full history of observations, summarization, retrieval augmentation. Stay tuned for more advanced posts in the future by following me. Feb 16, 2024 · The role of memory in LLM chats In the previous article, we discussed how the reasoning and decision-making capabilities of LLM agents can help us solve practical tasks. Whether you’re an indie developer experimenting with AI apps or a company needing offline capabilities, this setup is highly customizable and production-ready with the right tooling. 13 How it's Used: Sep 30, 2024 · Curious about how to replicate ChatGPT’s new functionality of remembering things in your own LangGraph agents? In this paper, we introduce a novel agentic memory system, named as A-Mem, for LLM agents that enables dynamic memory structuring without relying on static, predetermined memory operations. Standard LLM agent designs lack robust episodic memory and continuity across distinct interactions. This can be crucial for building a good agent experience. Traditional approaches rely on fixed memory structures—predefined storage points and retrieval patterns that do not easily adapt to new or unexpected information. In this paper, we identify four core 基于大型语言模型（LLM）的智能体最近吸引了研究和工业社区的广泛关注。与原始的大型语言模型相比，基于LLM的智能体以其自我进化能力为特色，这是解决需要长期和复杂智能体-环境交互的现实世界问题的基础。支持智能体-环境交互的关键组件是 智能体的记忆 。尽管先前的研究提出了许多有 Nov 8, 2023 · Hopefully on reading about the core concepts of Langchain (Agents, Tools, Memory) and following the walkthrough of a sample project provided some insight into how exactly complex applications Jun 12, 2024 · Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs to pass them. Inspired by this, we present an episodic memory framework for LLM agents, centered around five key properties of episodic memory that underlie adaptive and context-sensitive behavior. Aug 27, 2024 · AI Agent 是时下热门的一个方向，在 OpenAI 应用研究主管 LilianWeng 写的万字长文中[1]，她提出 Agent = LLM+ 记忆 + 规划技能 + 工具使用。 图1 Overview of a LLM-powered autonomous agent system 组件二： Apr 28, 2025 · Our findings highlight critical role of structured, persistent memory mechanisms for long-term conversational coherence, paving the way for more reliable and efficient LLM-driven AI agents. Feb 26, 2025 · What memory really means in LLM applications, how it relates to state management, and an overview of different approaches. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. 2. Mar 21, 2025 · LangMem is a framework for implementing memory systems in language-based agents. Current agents typically utilize a memory module or a retrieval-augmented generation (RAG) mechanism, retrieving past knowledge and instances with Feb 27, 2025 · Adding Read-Only Memory to LLMs and LLM Agents Large language models (LLMs) can be enhanced with memory systems that allow them to access information beyond their context window. Therefore, agents are stateful and they have memory. Feb 17, 2025 · Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. The application is built with mistral-7b-instruct-v0. Jul 10, 2025 · Explore how MemOS transforms LLM capabilities by elevating memory to a first-class resource, solving critical challenges in knowledge retention, context management, and personalized AI interactions through innovative memory architecture. Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. SemanticKernel. It includes Perceptual inputs: Observation (aka Grounding Agents promote human-type reasoning and are a great advancement towards building AGI and understanding ourselves as humans. LLM agents extend this concept to memory, reasoning, tools, answers, and actions. Sep 9, 2024 · Agents: A higher order abstraction that uses an LLMs reasoning capabilities for structuring a complex query into several distinct tasks. May 17, 2023 · Revolutionary advancements in Large Language Models have drastically reshaped our interactions with artificial intelligence systems. Apr 29, 2025 · In the notebook, we build two versions of a travel agent, one that manages long-term memory manually and one that does so using tools the LLM calls. Mar 22, 2025 · The Future of Agentic Memory As we advance in this field, several exciting directions emerge: 1. Memory is a key component of how humans approach tasks and should be weighted the same when building AI agents. Letta operates as a server and can be integrated into Python applications using its SDK. Provides ETL for LLMs via web scraping, Markdown extraction. It monitors the screen in real time, builds a personalized memory base, and offers intuitive visualization and secure local storage to ensure privacy. Jun 9, 2025 · Using Mem0 for Agent memory Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. We are going to use that LLMChain to create Feb 27, 2024 · To address this research gap, we introduce a machine-human pipeline to generate high-quality, very long-term dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. This Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Despite this, a notable hindrance remains-the deficiency of a long-term memory mechanism within these models. Mar 27, 2025 · Abstract Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. May 21, 2025 · Memory is a critical component in large language model (LLM)-based agents, enabling them to store and retrieve past executions to improve task performance over time. Retrieval-Augmented Generation (RAG) RAG is the most common Mar 27, 2024 · LLMs are often augmented with external memory via RAG. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, existing LLM agents are typically designed for static systems and lack the Jan 13, 2025 · Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, existing LLM agents are typically designed for static systems and lack the Sep 20, 2024 · When you call an LLM model, for example, as part of running an AI agent, the only information it gets is what is contained in the prompt… Mar 20, 2025 · The emergence of LLM-based agents represents a paradigm shift in AI, enabling autonomous systems to plan, reason, use tools, and maintain memory while interacting with dynamic environments. Two dummy methods were implemented to demonstrate its capabilities, namely calling them one after the other, and passing the results between Long-term memory solutions currently implemented via vector databases have significant limita-tions. Mem0 Platform provides a smart, self-improving memory layer for Large Language Models (LLMs), enabling developers to create personalized AI experiences that evolve with each user interaction. Learn how an LLM agent can act as an operating system to manage memory, autonomously optimizing context use. We examine the memory management approaches used in these agents. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. Feb 6, 2024 · LLMs in their current form are stateless which means that LLMs do not retain information about the user’s previous interaction. Finally, human annotators are tasked with manually filtering and refining the generated data. Contribute to zjunlp/LLMAgentPapers development by creating an account on GitHub. Memory in Agent This notebook goes over adding memory to an Agent. This technology allows AI agents to handle larger models and more complex tasks with ease. In specific, we first discuss ''what is'' and ''why do we need'' the memory in LLM-based agents. We term agents with memory mechanisms as memory agents. Moreover, each interaction is treated as an isolated episode 引言 基于大语言模型的智能体 (LLM-based Agent)在近期得到了广泛关注，其中，Memory模块是增强Agent能力的重要组件，也是未来研究的重要方向之一。 本文汇总选取了18篇与大语言模型智能体的记忆机制相关的论文，供大家阅读和参考。 Apr 15, 2024 · The adaptation of Large Language Model (LLM)-based agents to execute tasks via natural language prompts represents a significant advancement, notably eliminating the need for explicit retraining or fine tuning, but are constrained by the comprehensiveness and diversity of the provided examples, leading to outputs that often diverge significantly from expected results, especially when it comes Abstract Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Mar 5, 2025 · A-MEM explained A-MEM introduces an agentic memory architecture that enables autonomous and flexible memory management for LLM agents, according to the researchers. However, agents store the previous interactions in variables and use it in subsequent LLM calls. It is based on ideas from the MemGPT paper, which proposes using an LLM to self-edit memory via tool call. Nov 15, 2023 · The TiM framework consists of two crucial stages: (1) before generating a response, a LLM agent recalls relevant thoughts from memory, and (2) after generating a response, the LLM agent post-thinks and incorporates both historical and new thoughts to update the memory. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real- Jan 8, 2025 · Graphlit is a managed knowledge API platform providing ingestion, memory & retrieval for AI apps and agents. memary emulates human memory to advance these agents. Memory Consolidation and . Jun 19, 2025 · Complete LLM agents framework guide covering architecture components, memory modules, tool integration, and planning systems for intelligent AI development. Oct 19, 2024 · If agents are the biggest buzzword of LLM application development in 2024, memory might be the second biggest. Abstract Memory is a critical component in large language model (LLM)-based agents, en-abling them to store and retrieve past executions to improve task performance over time. 3 days ago · Abstract Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. Here are two diagrams showing the components used in both agents: Feb 18, 2024 · Adding memory to your LLM is a great way to improve model performance and achieve better results. A common technique to enable long-term memory is to store all previous interactions, actions, and conversations in an external vector database. Apr 21, 2024 · To bridge this gap, in this paper, we propose a comprehensive survey on the memory mechanism of LLM-based agents. Specifically, we focus on two fundamental memory operations that are widely The LLM agent architecture is utilized for each agent, enabling them to effectively memorize and reflect conversation history into ongoing dialogues. Jun 4, 2025 · Using a Langchain agent with a local LLM offers a compelling way to build autonomous, private, and cost-effective AI workflows. Jul 7, 2025 · What is Agent Memory? Agent memory is what and how your agent remembers information over time. In this work, we systematically investigate the vulnerability of LLM agents to our proposed Memory EXTRaction Attack (MEXTRA Mar 15, 2025 · In a previous post, we discussedsome limitations of LLMs and the relationships between LLMs and LLM-based agents. (2023) represents one of the most state-of-the-art approaches currently available for comparison in agent memory retrieval. Before runtime, the STM is synthesized by replacing the relevant variables in the prompt template with information retrieved from the LTM. Existing memory systems [25, 39, 28, 21] for LLM agents provide basic memory storage functionality. Mar 17, 2025 · Long-term memory in LLM Agents includes the agent’s past action space that needs to be retained over an extended period. Nov 26, 2024 · Memory-Specific Projects Letta: Letta is an open-source framework designed to build stateful LLM applications. Letta (formerly MemGPT) is the stateful agents framework with memory, reasoning, and context management. To address this, an innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. Dec 16, 2024 · These tools enable developers to integrate persistent memory into AI applications, improving context management. Contribute to WujiangXu/A-mem development by creating an account on GitHub. Mem0Provider integrates with the Mem0 service allowing agents to remember user preferences and context across multiple threads, enabling a seamless user experience. - letta-ai/letta Jan 20, 2025 · We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark. This approach reduces computational load while ensuring accuracy, making it a practical solution for scaling memory in AI systems. Jul 23, 2025 · LLM agents typically utilize two types of memory: Short-term Memory: This is used to manage current conversations or tasks and help the agent track ongoing activities. Abstract Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Jun 23, 2025 · In this article we have started from the most basic LLM-based agent, added the concept of memory, and then gave it the ability to go fetch data from external data sources. Why does memory matter in LLM agents? Explore short- and long-term memory systems, MemGPT, RAG, and hybrid approaches for effective memory management in AI agents. This paper provides the first comprehensive survey of evaluation methodologies for these increasingly capable agents. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems and psychological Aug 18, 2024 · 想深入了解 Agent 技术中的 Memory 记忆模块吗？本文将详细解读其工作原理与实现方式。从记忆的定义与类型，到 LLM 记忆的来源、保存和工作机制，逐一剖析，带你领略这一关键技术的奥秘。文中还列举了各种记忆实现案例，让你更直观地感受其应用。快来探索 Agent 技术的核心组成部分吧！ May 7, 2025 · Given the nascent stage of research in this area, particularly regarding LLM-based generative agents, the baseline memory retrieval method we used Park et al. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex 3 days ago · To address this research gap, we introduce a machine-human pipeline to generate high-quality, very long-term dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. Apr 21, 2024 · Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. LLM agents typically have a constrained memory capacity, limited by the number of tokens they can process in a single exchange. The Microsoft. It supports local models through vLLM and Ollama, with Q6 or Q8 models recommended for Jul 7, 2025 · Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks. Agentic memory refers to an LLM agent’s dynamic long-term memory system that can grow and reorganize itself, as demonstrated by A-MEM. This video examines how to implement a read-only memory system that enables an LLM to retrieve and reference past conversations. Ex-periments on two representative agents demon-strate the effectiveness of MEXTRA. In this paper, we identify four core Jul 17, 2024 · LLM agents have demonstrated remarkable performance across various applications, primarily due to their advanced capabilities in reasoning, utilizing external knowledge and tools, calling APIs, and executing actions to interact with environments. Traditional memory systems, while providing basic storage and retrieval functionality, often lack advanced memory organization capabilities. 2. Introducing “stateful agents”: AI systems that maintain persistent memory and actually learn during deployment, not just during training. LangMem provides an effective way to overcome these challenges by offering a structured long-term memory for Agents. Further, each agent can share coherent images, thereby enhancing the multi-modal dialogue aspect. Moreover, we equip each agent with the capability of sharing and reacting to images. These systems require agent developers to predefine memory storage structures, specify storage points within the workflow, and establish retrieval timing. Jul 19, 2025 · Definition for LLM Agents: Semantic memory allows agents to access and retrieve factual information relevant to the user's queries, going beyond the LLM's pre-trained knowledge or the immediate context window. But what even is memory? At a high level, memory is just a system that remembers something about previous interactions. It allows agents to remember what happened in the past and use that information to improve behavior in the future. While various memory modules have been proposed for these tasks, the impact of different memory structures across tasks remains insufficiently explored. What you'll learn Build agents with long-term, persistent memory using Letta to manage and edit context efficiently. Mar 1, 2025 · Current memory systems for large language model (LLM) agents often struggle with rigidity and a lack of dynamic organization. May 21, 2025 · Abstract Memory is a critical component in large language model (LLM)-based agents, enabling them to store and retrieve past executions to improve task performance over time. LangChain can parse LLM output to identify tasks, and then query an LLM repetitively until all tasks are completed, thereby synthesizing intermediate results into a final answer. Apr 15, 2025 · In the context of AI agents, memory is the ability to retain and recall relevant information across time, tasks, and multiple user interactions. Apr 22, 2025 · Mem0 takes care of all LLM and search requests required to store data in memory and retrieve data from memory, making it very simple to manage memory for multiple users and agents in one place. Must-read Papers on LLM Agents. lwjenu odihm nokyku cfgzzyst gmnjalg rfiopzb afj sxnf afsizn yuxk