MIRIX：面向大语言模型智能体的多智能体记忆系统

MIRIX: Multi-Agent Memory System for LLM-Based Agents

July 10, 2025

作者: Yu Wang, Xi Chen

cs.AI

摘要

尽管AI智能体的记忆能力日益受到关注，现有解决方案仍存在根本性局限。多数方法依赖扁平、范围狭窄的记忆组件，限制了其个性化、抽象化以及长期可靠回忆用户特定信息的能力。为此，我们推出MIRIX，一个模块化、多智能体记忆系统，通过解决该领域最关键的挑战——使语言模型真正具备记忆能力，重新定义了AI记忆的未来。与以往方法不同，MIRIX超越文本，拥抱丰富的视觉和多模态体验，使记忆在现实场景中真正实用。MIRIX包含六种精心构建的独特记忆类型：核心记忆、情景记忆、语义记忆、程序记忆、资源记忆及知识库，结合一个多智能体框架，动态控制与协调记忆的更新与检索。这一设计使得智能体能够大规模持久化、推理并准确检索多样化的长期用户数据。我们在两个高要求场景中验证了MIRIX。首先，在ScreenshotVQA这一包含每序列近20,000张高分辨率电脑截图、需要深度上下文理解且现有记忆系统无法应用的多模态基准测试中，MIRIX比RAG基线提高了35%的准确率，同时减少了99.9%的存储需求。其次，在LOCOMO这一单模态文本输入的长对话基准测试中，MIRIX达到了85.4%的最先进性能，远超现有基线。这些结果表明，MIRIX为记忆增强型大语言模型智能体设立了新的性能标准。为了让用户体验我们的记忆系统，我们提供了一个由MIRIX驱动的打包应用。它能实时监控屏幕，构建个性化记忆库，并提供直观的可视化界面和安全的本地存储，确保隐私。

English

Although memory capabilities of AI agents are gaining increasing attention, existing solutions remain fundamentally limited. Most rely on flat, narrowly scoped memory components, constraining their ability to personalize, abstract, and reliably recall user-specific information over time. To this end, we introduce MIRIX, a modular, multi-agent memory system that redefines the future of AI memory by solving the field's most critical challenge: enabling language models to truly remember. Unlike prior approaches, MIRIX transcends text to embrace rich visual and multimodal experiences, making memory genuinely useful in real-world scenarios. MIRIX consists of six distinct, carefully structured memory types: Core, Episodic, Semantic, Procedural, Resource Memory, and Knowledge Vault, coupled with a multi-agent framework that dynamically controls and coordinates updates and retrieval. This design enables agents to persist, reason over, and accurately retrieve diverse, long-term user data at scale. We validate MIRIX in two demanding settings. First, on ScreenshotVQA, a challenging multimodal benchmark comprising nearly 20,000 high-resolution computer screenshots per sequence, requiring deep contextual understanding and where no existing memory systems can be applied, MIRIX achieves 35% higher accuracy than the RAG baseline while reducing storage requirements by 99.9%. Second, on LOCOMO, a long-form conversation benchmark with single-modal textual input, MIRIX attains state-of-the-art performance of 85.4%, far surpassing existing baselines. These results show that MIRIX sets a new performance standard for memory-augmented LLM agents. To allow users to experience our memory system, we provide a packaged application powered by MIRIX. It monitors the screen in real time, builds a personalized memory base, and offers intuitive visualization and secure local storage to ensure privacy.