The Memory Pain of AI Agents: Inside MemOS Architecture and 200% Cloud Usage Growth

Memory is emerging as the biggest shortcoming of AI agents. After ChatGPT introduced personal memory in 2025, users no longer need to repeat context, and the importance of long‑term memory for continuous agent evolution became evident. The appearance of continuous‑type agents such as OpenClaw highlighted that the amount of information an agent can remember directly determines what it can accomplish.

Two Technical Paths for Memory

The industry generally follows two routes. The first is model‑driven enhancement, exemplified by Google’s Memorizing Transformers and a series of MemTensor models trained in 2023‑2024, which embed memory capabilities directly into the model architecture but incur high cost and risk. The second is application‑driven enhancement, where frameworks such as Mem0, Letta and Zep simulate memory through prompt or agent flows; this approach is lightweight and fast to deploy but lacks tight integration with the underlying model.

MemOS: A Hybrid Solution

MemOS combines both paths by assigning specific responsibilities to the model and to the system. It introduces a five‑layer architecture—memory extraction, organization, retrieval, update, and sharing—plus a three‑layer memory collaboration (plain, activation, and parameter memory). The design principle is that the model sets the upper bound while the application defines the lower bound, and the system coordinates the two.

Five‑Layer System Framework

Memory Storage Layer : Implements the smallest packable memory unit (MemCube) and a tradable memory market (MemStore), now extensible to the Skill layer.

Memory Governance Layer : Handles permission, lifecycle, watermark, and privacy controls.

Memory Scheduling Layer : Core of MemOS, managing multi‑granularity scheduling across plain, activation, and parameter memory.

Encoding/Decoding Layer : Interfaces with the model and external tools.

Application Layer : Provides the final agent‑level services.

MemOS uniquely operates from the infra level through memory base models up to the application, whereas most competing frameworks only manipulate plain memory via prompts or agent flows.

Performance and Scale

Since the cloud service launch in late 2025, MemOS has become the largest memory‑cloud platform in China. By the end of March 2026, monthly calls exceeded 25 million (daily >1 million), with month‑over‑month growth of 100‑200%. Token consumption per request dropped 45‑72%, and LLM‑Judge quality scores improved by over 30%, cutting interaction rounds by more than half and reducing overall token usage by nearly 50%.

The open‑source repository on GitHub has amassed nearly 8.5 k stars and over 12 k active users, including six enterprise contributors and twelve academic institutions.

Enhancing OpenClaw with MemOS Plugins

Four core issues were identified in OpenClaw’s native memory system: overly agentic logic leading to drift, incomplete separation of memory and context, excessive compression that loses detail, and a file‑retrieval‑style implementation that struggles with complex scenarios. MemOS addresses these with six plugin dimensions—storage type, multi‑path retrieval, evolution (memory‑to‑skill conversion), visualization, collaboration via Hub, and automated skill generation (Mem2Skill). The plugins achieve an average compression ratio above 75% and provide both cloud‑based and on‑premise deployment options.

Enterprise‑Level Deployment: ClawForce

ClawForce builds on MemOS with a five‑layer design (memory, skill engine, event listener, tool integration, intelligent core) and a three‑layer security mechanism (pre‑deployment isolation, in‑process data desensitization and encryption, post‑operation audit). It solves five common enterprise pain points: deployment difficulty, knowledge loss, response omission, limited workflow integration, and unclear data boundaries.

Real‑world cases demonstrate dramatic efficiency gains: a K8s memory‑leak investigation reduced from 2 hours to 10 minutes; a skill extraction pipeline (Mem2Skill) turned conversational fragments into reusable parameterized skills; and across industries, tasks such as e‑commerce monitoring, document drafting, and sales outreach saw time reductions of 85%‑90% and conversion improvements.

One‑Box Solutions

MemTensor also offers two integrated hardware solutions: an NVIDIA DGX‑based appliance with 128 GB shared GPU/CPU memory, and a domestically produced compute platform in partnership with China Telecom, both supporting large‑scale quantized models.

Overall, MemOS illustrates how a well‑engineered memory infrastructure can transform AI agents from a “pain point” into a foundational capability that scales across domains.