Why Memory Is the Biggest Challenge for AI Agents and How MemOS Boosts Cloud Calls by Over 200%

Memory as a Critical Factor for AI Agents

Memory has become the most significant bottleneck for AI agents. After ChatGPT introduced personal memory in 2025, users no longer need to repeat context, and the model can provide more relevant answers. The emergence of continuous agents such as OpenClaw makes the amount of memory an agent can retain directly determine its capabilities, turning memory from a nice‑to‑have feature into a core requirement for sustainable evolution.

Two Technical Paths: Model‑Driven vs Application‑Driven

The industry generally follows two paths. The model‑driven path enhances memory by modifying the base model architecture, exemplified by Google’s Memorizing Transformers and a series of MemTensor models trained in 2023‑2024; this approach offers strong capabilities but incurs high cost and risk. The application‑driven path simulates memory through Prompt or Agent flows, with frameworks such as Mem0, Letta, and Zep; it is lightweight and quick to implement but less tightly integrated with the underlying model. MemTensor’s MemOS framework fuses both paths, assigning high‑level limits to the model‑driven layer and low‑level details to the application‑driven layer, achieving a layered collaboration that the industry widely adopts by 2026.

MemOS 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 especially vulnerable to hallucination because memory is a highly abstracted summary of knowledge. The framework consists of:

Memory Storage Layer : the minimal 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 memory—to coordinate flow across the three layers.

Encoding/Decoding Layer and Application Layer : the topmost layers that interface with user applications.

MemOS uniquely provides end‑to‑end enhancement from Infra (GPU, KV‑Cache management) to parameter memory, whereas most competing frameworks only operate at the plain‑memory level via Prompt or Agent flows.

Platform Scale and Ecosystem

MemOS cloud service launched at the end of 2025 and has become the largest memory‑cloud platform in China. By March 2026, monthly calls exceeded 25 million, with daily calls over 1 million and month‑over‑month growth between 100 % and 200 %. Token consumption per request dropped by 45 %‑72 %, benefiting agent developers, OpenClaw tool users, and game or hardware vendors alike. The open‑source repository on GitHub has amassed nearly 8.5 k stars and over 12 k active users, including six enterprises and twelve academic institutions.

Enhancing OpenClaw with MemOS

OpenClaw’s native memory system suffers from four issues: overly agentic logic leading to drift, incomplete integration between memory and context, over‑compression that loses critical details, and a retrieval design that resembles file search rather than true memory. MemOS addresses these by providing six plugin dimensions—storage type, multi‑path retrieval, diversity handling, time decay, deduplication, and evolution—allowing OpenClaw to become smarter over time, visualize memory for junior developers, and collaborate via a Hub that eliminates knowledge silos. Cloud plugins enable one‑click SaaS integration, while local plugins offer privacy‑preserving deployment with three‑step installation.

Local plugins implement a three‑stage deduplication funnel (SHA‑256 exact match, vector cosine similarity, LLM‑Judge contradiction detection) achieving an average compression ratio of 75 %.

After integrating MemOS, LLM‑Judge scoring improves by over 30 %, interaction rounds halve, and token consumption drops by roughly 50 %.

Enterprise Deployment: ClawForce Design and Security

ClawForce builds on MemOS with a five‑layer design (memory, Skill engine, event listener, tool connector, management console) and three‑tier security (pre‑deployment isolation, in‑flight data desensitization and encryption, post‑operation audit). It solves five common enterprise pain points: deployment difficulty, scattered experience, missed responses, limited workflow integration, and unclear data boundaries. Administrators can define OpenClaw metadata, generate full‑link MD files, and push configurations—including model settings, capability mounting, and IM system integration—through a single AI‑driven workflow. Skill extraction and automatic quality scoring enable continuous knowledge accumulation, while the Hub ensures cross‑agent collaboration.

Real‑World Scenarios and One‑Box Solutions

ClawForce has been deployed across multiple industries:

R&D: from Feishu requirement submission to AI‑driven code generation, simulation, and production‑line automation.

E‑commerce: 7×24 hour monitoring, anomaly alerts, strategy suggestions, and report generation.

Document writing: 85 % reduction in drafting time with format compliance.

Sales: doubled customer reach and improved opportunity conversion via automated Skill feedback.

Two one‑box solutions are offered: a DGX‑based appliance with 128 GB shared GPU/CPU memory for mainstream quantized models, and a domestically produced compute solution co‑developed with China Telecom, both supporting flexible configuration.

Overall, MemTensor aims to turn memory into a shared, personalized infrastructure for AI agents, bridging open‑source research and enterprise products to enable smarter, more efficient applications across thousands of industries.