MIRIX: Multi-Agent Memory System for LLM-Based Agents
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.
68706310c8391850d60977de
Yu Wang
claimed_verified
2025-07-11T08:02:31.566Z
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Yu Wang
user
YuWangX
68706310c8391850d60977df
Xi Chen
MIRIX: Multi-Agent Memory System for LLM-Based Agents
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.
MIRIX, a modular multi-agent memory system, enhances language models' memory capabilities by integrating diverse memory types and a dynamic framework, achieving superior performance in multimodal and long-form conversation benchmarks.
modular multi-agent memory system Core Episodic Semantic Procedural Resource Memory Knowledge Vault ScreenshotVQA LOCOMO long-form conversation benchmark memory-augmented LLM agents