Why AI Agents Struggle with Memory and How MemOS Boosts Cloud Calls Over 200%

1. Memory – the make‑or‑break factor for AI agents

Since ChatGPT introduced personal memory in 2025, users no longer need to repeat context, and OpenAI’s CEO repeatedly emphasized that memory is essential for AGI‑level personalization. The emergence of continuous agents such as OpenClaw highlighted that an agent’s ability to remember directly determines task success, especially for multi‑session, multi‑user, and multi‑agent scenarios where complexity explodes.

2. Two technical paths for memory

Model‑driven enhancement – exemplified by Google’s Memorizing Transformers and MemTensor’s 2023‑2024 training‑time models – injects memory into the base model architecture but incurs high cost and risk.

Application‑driven enhancement – using Prompt or Agent flows (e.g., Mem0, Zep) – offers lightweight, fast deployment but suffers from weaker coupling with the underlying model.

MemTensor’s MemOS framework fuses both paths, assigning specific responsibilities to the model (upper bound) and to the system (lower bound) and coordinating them through a layered design.

3. MemOS system framework – five layers and three‑layer memory coordination

Memory storage layer : MemCube (packable memory units) and MemStore (tradeable memory market), now extensible to the Skill layer.

Memory governance layer : permissions, lifecycle, watermark, and privacy controls.

Memory scheduling layer : core of MemOS, handling three memory types – clear‑text, activation, and parameter memory – and routing them across layers.

Encoding/decoding layer and application layer : top‑level interfaces for agents and external services.

MemOS uniquely operates from Infra → memory model → application, managing GPU and KV‑Cache resources for fine‑grained memory handling, unlike most frameworks that only address clear‑text memory via Prompt/Agent flows.

4. Platform scale and ecosystem

MemOS cloud service launched at the end of 2025 and became the largest domestic memory‑cloud platform. By Q3 2026, monthly calls exceeded 25 million (100 k‑200 k daily), with month‑over‑month growth of 100‑200% and token consumption reduced by 45‑72%.

The open‑source repository on GitHub has ~8.5 k stars and 12 k active users, supported by six enterprises and twelve academic institutions, with an active community (OpenMem).

5. Enhancing OpenClaw – six dimensions and plug‑in solutions

Identified four core issues in OpenClaw’s native memory system: overly agentic logic, incomplete separation of memory and context, over‑compression of details, and file‑retrieval‑style implementation. MemOS addresses these with plug‑ins that improve storage types, multi‑route retrieval, evolutionary learning (Mem2Skill), visualization, and collaborative Hub integration.

Local plug‑ins provide one‑click, non‑intrusive memory enhancement via six Context Engine hooks, achieving 75%+ compression through SHA‑256 deduplication, cosine similarity, and LLM‑Judge conflict detection.

6. Enterprise deployment – ClawForce five‑layer design and three‑tier security

ClawForce builds on MemOS to deliver a five‑layer architecture (memory, Skill engine, event listener, tool link, intelligent hub) with pre‑, in‑, and post‑execution security: isolation, edge‑side data desensitization and encryption, and full auditability.

Multi‑agent collaboration enables automatic skill extraction, quality scoring, and seamless hand‑off between agents, reducing OOM troubleshooting from 2 hours to 10 minutes and cutting interaction rounds by more than 50%.

7. Scenario rollout and appliance solutions

ClawForce has been applied in R&D (AI‑assisted coding and simulation), e‑commerce (7 × 24 h monitoring), document drafting (85% time reduction), and sales (doubling customer reach). Two integrated‑machine offerings are available: an Nvidia DGX‑based unit with 128 GB shared GPU/CPU memory and a domestically produced compute solution co‑developed with China Telecom.

MemTensor’s roadmap connects the open‑source MemOS framework to the enterprise‑grade ClawForce product, positioning memory as the shared, personalized infrastructure that will drive AI agents across industries.

Overall, the presentation demonstrates that robust, layered memory systems are essential for scaling AI agents from experimental prototypes to production‑grade services.