Orient Securities has released a research report stating that Moltbot is an open-source personal AI assistant framework centered on gateway scheduling and hybrid memory, supporting remote task operations through common chat applications. Potential investment opportunities on the hardware side are emerging: the demand for persistent computing power driven by personal Agents is taking shape. Local Agent frameworks, represented by Moltbot, will spur demand for a new category of personal computing hardware that operates 24/7 while balancing privacy and energy efficiency. The main views of Orient Securities are as follows:

Technologically, Moltbot operates on the principles of gateway scheduling, hybrid memory, and semantic snapshots. Moltbot (formerly Clawdbot) is an open-source personal AI assistant framework that supports common chat applications like WhatsApp, Telegram, Slack, and iMessage as interaction interfaces. Users can remotely command the AI assistant to perform tasks via a chat window on their phone or computer, eliminating the need to switch to a dedicated application. It can directly manipulate local files, run scripts, control browsers, manage emails, and set scheduled tasks.

From a technical architecture perspective, Moltbot is a long-running local TypeScript CLI process that exposes a unified gateway to handle instruction requests from various communication channels. Its core design philosophy is a serial scheduling model that prioritizes controllability: all tasks are, by default, queued sequentially at the session level for execution, with parallel processing only enabled for operations explicitly marked as low-risk, thereby fundamentally reducing race conditions and debugging complexity.

At the execution layer, the Agent Runner employs a standard ReAct loop but incorporates engineering-grade fault-tolerance mechanisms. These include dynamic prompt assembly, token window protection, and model auto-fallback with cooldown periods, ensuring stable operation during long-duration tasks and scenarios involving multiple tool calls.

Regarding its memory system: short-term memory logs execution traces in JSONL format, while long-term memory is directly persisted as Markdown files. This is combined with a hybrid retrieval system using both vector search and keyword search to avoid reliance on opaque databases. For computer interaction, Moltbot isolates shell execution environments using Docker sandboxes and employs semantic snapshots instead of visual screenshots for browser operations, compressing page states from image-level data to structured text, which significantly reduces token costs and interaction latency.

At the whole-machine level, focus is on edge computing boxes and AI NAS. The report posits that local Agents, exemplified by Moltbot, require 7x24 operation of Docker containers, monitoring of multiple communication channels, and the ability to wake and execute tasks instantly. Traditional high-power PCs are unsuitable, while purely cloud-based solutions face privacy and control limitations, thus driving computing power towards localized, integrated hardware forms. Edge computing boxes, such as the Mac mini, offer a blend of desktop-level performance and energy efficiency, making them suitable as local execution and inference nodes, thereby objectively boosting demand for low-power, high-performance small-form-factor computers. High-performance NAS devices are evolving from mere storage units into "home AI computing centers," leveraging their capability for long-term stable operation, local data闭环, and expandable memory/compute power, making them well-suited to host Agent gateways, memory systems, and lightweight inference; attention should be paid to manufacturers continuously upgrading in the directions of NPU integration and large memory capacity.

For storage, focus is on innovations in unified memory architecture. The report argues that Moltbot is highly dependent on long contexts and local models, making memory capacity and bandwidth potential bottlenecks. Consequently, unified memory architectures that enable high-speed sharing of large-capacity DRAM among CPUs, GPUs, and NPUs are expected to become widespread across a broader range of chip platforms, giving related memory subsystems and architectural designs medium- to long-term investment value.

Regarding computing power, the persistent operation of Agents generates demand for low-power compute. The report suggests that Moltbot's need to continuously monitor messages and act as a system wake-up hub creates specific power consumption requirements. SoC forms possessing ultra-low-power persistent listening capabilities and wake-up control will hold a distinct advantage.

Investment recommendations include: SoCs - Allwinner Technology and Rockchip; GPUs - Cambricon, Hygon Information, Loongson Technology, Moore Thread, and Dongxin Co., Ltd. (LiSuan Technology); Storage - Montage Technology. Risks highlighted include the potential for slower-than-expected adoption of local Agent applications, changes in technological pathways, and uncertainties in the pace of technology implementation.