On July 6,ifanalysis officially released the "2026 ifanalysis China AI Agent Memory Market Size Research Report". This report redefines memory, clarifying it as an experiential asset accumulated through interactions, dynamically updated, and continuously guiding decision-making for AI agents. ifanalysis predicts that the market size of China's AI agent memory will surge from 1.44 billion RMB in 2025 to 64.25 billion RMB in 2030, with a compound annual growth rate (CAGR) exceeding 110% during this period.
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01. Report Overview
Over the past two years, the industry's focus on improving AI agent capabilities has mainly centered on model upgrades and iterations. The mainstream view holds that a more powerful model means stronger AI agent capabilities. As next-generation AI agent products like OpenClaw are deployed, the industry has found that while model capabilities can support agents in completing long-horizon tasks, it is memory capability that will truly determine task effectiveness and the upper limit of agent capabilities.
Memory is becoming the infrastructure track with the fastest growth rate and the largest market potential in the AI agent industry chain. Calculations by ifanalysis show that the market size of China's AI agent memory will grow from 1.44 billion RMB in 2025 to 64.25 billion RMB in 2030, with a CAGR of over 110% during this period.
Figure 1: Forecast of China's AI Agent Memory Market Size from 2025 to 2030
Despite the rapid market growth, there are still significant misconceptions about memory in the current industry. The first misconception is the belief that memory is deeply coupled with the foundation model and is one of the model's critical capabilities.
In fact, the model is responsible for the AI agent's thinking, while memory is responsible for the agent's growth. The model determines what the AI agent can do, whereas memory determines how well it can do it. For enterprises, organizational experience does not belong to a specific model, but to the enterprise itself; for individuals, preferences, relationships, and behavioral habits do not depend on a specific model, but belong to the users themselves.
Therefore, memory is not an ancillary function of the model, but a brand-new AI agent infrastructure that forms an independent market space. According to ifanalysis's calculations, by 2030, memory will account for nearly 26% of the AI agent infrastructure, becoming the second-largest independent market, second only to the model.
Figure 2: Proportion of Memory in AI Agent Infrastructure from 2025 to 2030
The second misconception is the erroneous understanding of the value of memory. Some view memory as an upgrade to knowledge bases, some interpret it as ultra-long context capabilities, and others see it as a crucial means to save tokens. These perceptions all ignore the core value of memory: memory is not a storage system for knowledge, but an accumulation system for experience.
According to the definition by ifanalysis, memory is an experiential asset formed, stored, and dynamically updated during long-term interactions by AI agents, which continuously supports decision-making, execution, and collaboration.
Compared with traditional data and knowledge assets, the most significant characteristic of memory lies in its dynamics and growth potential. It not only records what happened in the past but also why it was done that way, what the results were, and how to optimize it in the future. Memory will continuously evolve with environmental changes, task execution, and user feedback, thereby steadily advancing AI agent capabilities.
The core of memory is not the technical framework, but the personalized experience it carries. This experience is dynamic and will be continuously updated and iterated over time, thus becoming an indispensable and critical asset for both organizations and individuals.
02. Analysis of Driving Factors for the Growth of the AI Agent Memory Market
2.1 Shifting from Knowledge-Driven to Experience-Driven
The fundamental reason driving the rapid growth of the AI agent memory market is that the AI industry is transitioning from a knowledge-driven stage to an experience-driven stage. The core value in the era of large models lies in knowledge production. Models learn world knowledge through massive training data and complete knowledge retrieval through reasoning capabilities. As model capabilities continue to improve, the cost of knowledge acquisition is dropping rapidly. More and more AI agents will possess similar knowledge levels and reasoning capabilities, and the gap in model capabilities will gradually narrow.
Against this backdrop, experience is beginning to become the key factor determining the competitiveness of AI agents. Knowledge determines the lower limit of capability, while experience determines the upper limit. For an AI agent with the same model capabilities, whether it can understand user preferences, reuse historical experience, and continuously optimize execution results will directly determine its final performance. Therefore, experience is becoming a new means of production in the age of AI agents, and memory becomes the core infrastructure for managing and utilizing this means of production.
2.2 Memory Drives the Self-Evolution of AI Agents
The transformation of experience into a means of production is first reflected in the evolution of AI agent capabilities. Currently, most AI agents still rely on real-time model reasoning to complete tasks; although they possess thinking capabilities, they lack the ability to grow. The value of memory lies in transforming each task execution process into a long-term experiential asset. Task objectives, execution paths, decision-making logic, result feedback, and optimization strategies can all be continuously accumulated and reused in subsequent tasks.
As experience continuously accumulates, the execution efficiency of AI agents steadily improves, the error rate continuously drops, and they gradually form autonomous optimization capabilities. Memory, therefore, becomes a crucial foundation for AI agents to achieve autonomous evolution. Notably, when AI agents can directly invoke historical experience to make decisions, it can also significantly reduce the consumption of reasoning tokens.
2.3 Both Enterprise and Individual Users Require Experience Inheritance
The transformation of experience into a means of production is also reflected in enterprise AI construction and the competition among consumer-grade AI products. The most core competitive advantage of an enterprise often comes from experience and know-how. Business experience, industry experience, and decision-making experience have long been held by a few core employees; when employees leave, the experience is lost with them. Experience is difficult to replicate, inherit, and utilize at scale, which has always been a critical issue in enterprise operations.
As digital employees gradually take on more work tasks, enterprises are beginning to try to transform organizational experience into inheritable digital assets. The assetization of organizational experience is becoming an important direction for enterprise intelligent upgrading. The transformation of experience into a means of production is also reflected in the changing logic of competition among consumer-grade AI products. As model capabilities tend to homogenize, users' evaluation criteria for AI products are changing. Users are no longer just concerned with whether the model is smart, but whether the product truly understands them. User preferences, behavioral habits, long-term goals, social relationships, and historical experiences will all become important foundations for future AI products to provide personalized services.
The most competitive AI products in the future may not necessarily have the most powerful models, but they will definitely have the most complete user memory. Whoever can continuously accumulate user experience will be able to provide more precise service experiences, establish higher user stickiness, and form stronger commercial barriers.
Combining the above analysis, it can be seen that from the autonomous evolution of AI agents, to the inheritance of organizational experience, and then to the upgrading of personalized services, three forces are jointly driving the rapid growth of memory demand. Centered around the formation, management, invocation, and inheritance of experience, a new infrastructure market is rapidly taking shape.
03. Comprehensive Map of the AI Agent Memory Market
As experience gradually becomes the new means of production in the age of AI agents, a complete system is forming around the storage, management, and application of experiential assets. According to the definition by ifanalysis, the AI agent memory market mainly consists of three layers: memory hardware, memory operating system, and memory applications. Among them, memory hardware is responsible for experience storage, memory applications are responsible for experience consumption, and the memory operating system is responsible for experience management and scheduling.
Figure 3: Comprehensive Map of the AI Agent Memory Market
From the perspective of market size, the three-layer market will all benefit from the growth dividends brought by the popularization of AI agents. However, from the perspective of value distribution, the memory operating system is expected to become the core segment with the fastest growth and the highest value proportion in the future.
Figure 4: Market Size Distribution of the AI Agent Memory Market
According to calculations by ifanalysis, the market size of the memory operating system will reach 21.85 billion RMB in 2030, accounting for 34% of the overall memory market.
Figure 5: Market Size of AI Agent Memory Operating System from 2025 to 2030
At the same time, the CAGR of the memory operating system market size is also the fastest. According to statistics from ifanalysis, the five-year CAGR of the AI agent memory operating system exceeds 152%, while the CAGR of the overall memory market during the same period is 114%.
Currently, the market's understanding of memory still remains at the storage level, and many vendors view memory as an extension of vector databases, knowledge bases, or context management systems. However, as AI agents begin to undertake increasingly complex tasks, the true challenge of memory is not storage capacity, but computing capabilities such as management, orchestration, and scheduling. As the number of tasks executed by AI agents continues to grow, a massive amount of task trajectories, decision-making processes, user feedback, and behavioral records will continuously accumulate.
How to extract effective experience from massive information, how to establish associative relationships between experiences, and how to invoke the right experience at the right time are becoming key factors affecting AI agent capabilities. Therefore, memory systems not only require storage capabilities but also computing capabilities.
Future memory operating systems will need to possess multiple capabilities such as memory extraction, compression, association, orchestration, and reflection. ifanalysis believes that the memory operating system will be located at the core of the value chain, connecting downward to hardware infrastructure on one hand, and serving various applications upward on the other. Whether it is digital employees, personal AI assistants, or enterprise AI agents, they all need to complete the management and invocation of experience through the memory operating system.
As the number of AI agents continues to grow, the network effects and platform effects of the memory operating system will be continuously enhanced. Subsequently, ifanalysis will also release the "2026 ifanalysis China AI Agent Memory Market Research Report". Stay tuned.
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