Unpacking MemOS: How AI Agents Overcome the “Memory Pain” and Boost Cloud Calls by 200%

Memory: The Critical Factor for AI Agents

Memory has become the biggest shortcoming of AI agents. After ChatGPT launched a personal‑memory feature, users no longer need to repeat background information, and the model can answer more precisely. The emergence of continuous agents such as OpenClaw makes the amount of memory an agent can retain directly determine what it can accomplish.

Two Technical Paths: Model‑Driven vs Application‑Driven

The industry’s memory‑enhancement solutions fall into two categories. The model‑driven path (e.g., Google’s Memorizing Transformers and MemTensor’s 2023‑2024 training models) injects memory capability by modifying the base model architecture, but it incurs high cost and risk. The application‑driven path uses prompt or agent flows (e.g., Mem0, Zep) to simulate memory, offering lightweight deployment but weaker integration with the underlying model.

MemTensor’s MemOS merges both paths: a layered approach decides which memory tasks belong to the model and which to the system, a practice that became an industry consensus by 2026.

MemOS System Framework: Five‑Layer Architecture and Three‑Layer Memory Coordination

MemOS decomposes a complete memory system into five core stages: extraction, organization, retrieval, update, and sharing. Certain stages are vulnerable to model hallucination, which can accumulate downstream.

The five layers are:

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

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

Memory Scheduling Layer : the core of MemOS, handling three memory types—plain, activation, and parameter—and coordinating flow across three sub‑layers.

Encoding/Decoding Layer and Application Layer : top‑level interfaces for agents and external services.

Parameter memory injects post‑training industry know‑how into the inference model, while activation memory manages KV‑Cache to keep cache‑hit rates high, reducing token consumption.

Platform Scale and Ecosystem

MemOS’s cloud service launched at the end of 2025 and is now the largest memory‑cloud platform in China. By March 2026, monthly calls exceeded 25 million (≈100 k per day), with month‑over‑month growth of 100‑200%. Each request saves 45‑72% token consumption.

The open‑source repository on GitHub has nearly 8.5 k stars, 12 k active users, and contributions from six enterprises and twelve academic institutions.

MemOS Enhances OpenClaw: Six Dimensions and Plugins

In practice, OpenClaw’s native memory system suffers from four issues: overly agentic logic leading to drift, separation of memory and context that prevents seamless integration, over‑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 (diversity, time decay, deduplication), evolution (transforming Memory into Skill), visualization, collaboration via Hub, and multi‑agent coordination.

Local plugins provide one‑click, non‑intrusive memory enhancement using six Context Engine hooks, achieving an average compression ratio above 75% through SHA‑256 deduplication, cosine similarity, and LLM‑Judge conflict detection.

Enterprise Deployment: ClawForce Five‑Layer Design and Triple‑Layer Security

Even with MemOS, enterprises must solve five common pain points to move AI agents from “usable” to “reliable”: deployment difficulty, knowledge loss after staff turnover, missed responses, limited integration with Office/CRM/OA, and unclear data boundaries.

ClawForce’s architecture centers on an intelligent hub and includes memory, Skill engine, event listeners, and tool links. Security is split into pre‑, during‑, and post‑execution layers, providing isolation, on‑device data desensitization, encryption, and full audit trails.

Scenario Deployment and Integrated Appliance Solutions

ClawForce has been deployed across multiple industries: R&D pipelines (from requirement capture in Feishu to AI‑generated code and simulation), e‑commerce operations (7×24 h monitoring, anomaly alerts, policy‑compliant report generation), document drafting (85% time reduction), and sales (doubling customer reach). Additional scenarios include customer service, recruitment, finance, legal, HRBP, data analysis, market, project management, supply chain, administration, compliance, and training.

Hardware offerings include a DGX‑based appliance with 128 GB shared GPU/CPU memory for quantized models, and a domestically produced compute solution from China Telecom for privacy‑sensitive deployments.

Overall, MemOS provides a complete path from open‑source framework to enterprise‑grade product, turning memory into a shared, personalized infrastructure for AI agents and enabling smarter applications across thousands of use cases.