Why Memory Is the Bottleneck for AI Agents and How MemOS Achieves 200% Cloud Call Growth

Memory is emerging as the biggest shortcoming of AI agents; after ChatGPT introduced personal memory, users no longer need to repeat context, and the arrival of continuous agents such as OpenClaw makes the amount of memory an agent can retain directly determine its capabilities, leading the industry to treat memory as a core factor for agent evolution.

MemTensor’s open‑source MemOS framework has attracted nearly 8.5K GitHub stars and its cloud service surpassed 25 million calls per month, with month‑over‑month growth between 100% and 200%, making it the fastest‑growing agent‑memory infrastructure in China. In a recent technical talk, CEO Xiong Feiyu outlined the importance of memory in the AGI era, the evolution path of memory systems, and the practical integration of MemOS with the enterprise product ClawForce.

The presentation is organized into seven parts: (1) memory as a make‑or‑break factor for agents; (2) two technical paths—model‑driven and application‑driven; (3) the MemOS system framework with a five‑layer architecture and three‑layer memory coordination; (4) platform scale and ecosystem; (5) MemOS‑enhanced OpenClaw across six dimensions; (6) enterprise deployment with ClawForce’s five‑layer design and triple‑layer security; (7) scenario roll‑outs and an all‑in‑one hardware solution.

From an efficiency‑tool perspective, memory has evolved into a decisive factor for agent deployment. In 2023 memory received limited attention; in April 2025 ChatGPT launched personal memory, and the emergence of OpenClaw highlighted that memory now influences token consumption, recall rate, and long‑term task success across multi‑session, multi‑user, and multi‑agent scenarios.

The two technical paths are: (a) model‑driven enhancement, exemplified by Google’s Memorizing Transformers and MemTensor’s 2023‑24 training series, which embed memory directly into the model architecture but incur high cost and risk; (b) application‑driven enhancement, which simulates memory via Prompt or Agent flows using frameworks such as Mem0, Letta, and Zep, offering lightweight deployment at the expense of tighter model integration.

MemOS merges both paths by introducing a layered processing model. A complete memory system is decomposed into five core stages—extraction, organization, retrieval, update, and sharing. Certain stages are vulnerable to hallucination, which can accumulate downstream. The framework adds a memory‑governance layer (permissions, lifecycle, watermark, privacy) and a memory‑scheduling layer that orchestrates three memory types: explicit, activation, and parameter memory. The top encoding and application layers sit above, while MemOS uniquely provides infra‑level parameter memory and KV‑Cache management for fine‑grained control.

Since its cloud launch at the end of 2025, MemOS has become the largest domestic memory‑cloud platform. By Q3 2026 the service handled over 25 million monthly calls (daily >1 million), achieving a 45%–72% reduction in token consumption. The open‑source repository has amassed ~8.5K stars, 12 k active users, and a community comprising six enterprises and twelve academic units.

In practice, OpenClaw’s original memory system exhibited four core issues: (1) overly agentic logic leading to drift; (2) imperfect separation of memory and context; (3) excessive compression causing loss of detail; (4) file‑retrieval‑style design that struggles with complex scenarios. MemOS addresses these via six plugin dimensions—storage type, multi‑route retrieval (diversity handling, time decay, deduplication), evolution (auto‑transforming memory into skills), visualization, collaboration through a Hub, and a unified API. Both cloud‑based and on‑prem plugins support one‑click installation; the local plugin employs a three‑stage dedup pipeline (SHA‑256 exact match, vector cosine similarity, LLM‑judge conflict detection) achieving >75% compression.

A new capability called Mem2Skill extracts content from dialogue fragments, structures it into parameterized skills, and converts “remembering” into “learning,” thereby turning memory into actionable ability.

ClawForce demonstrates enterprise roll‑out with a five‑layer design (memory layer, skill engine, event listener, tool linking, management console) and a three‑stage security model (pre‑deployment isolation, in‑process data desensitization and encryption, post‑operation audit). It solves five common pain points: deployment difficulty, loss of organizational experience, missed responses, limited workflow integration, and unclear data boundaries. The system supports multi‑agent collaboration, automatic skill generation, and auditability.

Real‑world deployments include: R&D pipelines that automate code generation and simulation; e‑commerce monitoring that reduces a 7 × 24 h task to 10 min; document drafting cut by 85%; sales scenarios that double customer reach; plus extensions to support customer service, recruitment, finance, legal, HRBP, data analysis, supply chain, and more. Hardware offerings feature an Nvidia DGX‑based 128 GB shared‑memory appliance and a telecom‑provided domestic compute solution.

Overall, MemTensor bridges an open‑source memory framework to an enterprise‑grade product, positioning memory as the foundational infrastructure for AI agents and enabling a future where intelligent agents evolve through persistent, personalized memory.