最新消息显示，ApsaraDB RDS Debuts RDSHermes: Empowering Database AI Agents to Evolve Autonomou
ApsaraDB RDS AI Application has become a database-native AI agent service that supports both OpenClaw and Hermes Agent frameworks. Hermes Agent is a new generation of AI Agent framework open source by Nous Research. It has won 90k + Star in GitHub in less than half a year. It is called “the only Agent with built-in learning closed loop”. It and OpenClaw represent two very different philosophies of agent building, and also provide different capabilities orientation for enterprises. RDSHermes is a database-native AI agent service built based on the open source Hermes agent. It provides enterprises with an AI agent that can evolve autonomously and is secure, controllable and observable. For RDS users, this means that an important choice is opened: you can choose the most suitable Agent framework to drive intelligence according to your business scenario. Below, we disassemble the similarities and differences between the two in depth from the architectural level to help you make a judgment. From a design perspective: OpenClaw is an ‘all-around executor’, while Hermes Agent is an ‘ever-smarter learner’ Let’s look at other differences in the design and implementation of the core modules. OpenClaw ‘s memory mechanism is based on the MEMORY.md file and daily logs. The quality of the Agent’s memory depends entirely on “what is written”—if key information is not properly recorded in the file, it cannot be retrieved in the next conversation. This is a passive memory: reliable, but dependent on manual maintenance. Hermes Agent adopts an autonomous memory architecture: FTS5 full-text indexed session storage, periodic introspection (the Agent proactively reviews what information is worth remembering), and Honcho dialectical user modeling (building a continuously deepening “model of you”). Memory does not rely on human intervention but is automatically accumulated and optimized during use. OpenClaw ‘s skills are static Markdown files, manually written and installed. The quality and coverage of skills depend on community contributions and team maintenance. Hermes Agent ‘s skill system is dynamic: after completing a complex task, the Agent will suggest that the user consolidate the solution into a new skill; in subsequent use, if the skill is found to have room for improvement, the Agent can modify and improve existing skills through tool calls during use. This means the Agent’s capabilities will naturally grow with usage over time. OpenClaw usually with single-process operation requires independent deployment of servers, the configuration process is relatively heavy (30-60 minutes), more inclined to “install once, long-term use” mode. Hermes Agent provides six types of terminal backends (local, Docker, SSH, Daytona, Singularity, and Modal). Among them, Daytona and Modal support Serverless persistence-Agent environments sleep when idle and wake up on demand, with almost zero idle costs. Hermes Agent’s unique differentiating capability is its research-oriented infrastructure: The built-in Atropos reinforcement learning environment supports batch trajectory generation and trajectory compression, and can be used to train next-generation tool invocation models. This means that Hermes Agent is not only a tool, but also a research platform that can generate training data. For enterprises with AI R&D teams, this is a unique value point. Advantages of OpenClaw • Higher ecological maturity: The ClawHub skills market already has a large number of community-contributed skills, covering a wider range of scenarios • Predictable behavior: Static skills mean consistent and reproducible behavior, which is more friendly to scenarios requiring strictly standardized operations • Broader enterprise adoption: As an earlier framework, it has a large number of enterprise implementation cases and best practices Limitations of OpenClaw • Memory relies on manual maintenance: key information will be lost if it is not written to MEMORY.md • Skills cannot self-evolve: skill updates rely entirely on manual editing and community maintenance • Heavy initial configuration: requires manual configuration of openclaw.json, workspace files, and memory settings Advantages of Hermes Agent • Self-evolution: the only agent with a built-in learning loop, allowing skills and memory to grow autonomously • Ultimate model flexibility: one-click switching across 200+ models to significantly reduce inference costs • Deployment flexibility: six backends plus Serverless persistence, adapting to diverse infrastructure environments • One-click migration: the hermes claw migrate command automatically imports all OpenClaw data Limitations of Hermes Agent • Young ecosystem: published less than half a year ago, the quantity and quality of community-contributed skills are still accumulating • Unpredictability of self-evolution: agents autonomously modifying skills may introduce unexpected behaviors, requiring stronger observability as a safeguard • Decision cost of model selection: the flexibility of the 200 + model also means that it takes time to find the optimal configuration. RDSHermes provides a convenient way to connect flying books, QQ, enterprise WeChat and personal WeChat, which can be bound to personal WeChat through simple two-dimensional code scanning. The community version Hermes Agent provides a webui interface that can configure and manage Hermes in version v0.9.0. However, it lacks the ability of white screen ChatUI and requires users to interface with Open Webui, etc., which virtually increases the use threshold for some users. Different from the open source version of the main CLI service, RDSHermes provides an additional Hermes-WebUI portal for the community, allowing users to issue tasks to Hermes through WebUI dialogues like OpenClaw. 🔗Hermes- WebUI: https://github.com/nesquena/hermes-webui For enterprise users, the most important point is that whether you choose OpenClaw or Hermes Agent, the core capabilities provided by RDS AI applications remain consistent. In particular, Hermes Agent’s self-evolving nature (self-creating skills, self-modifying behavior) introduces behavioral unpredictability while bringing intelligence growth. The observability center and the four-layer security system of RDS provide an enterprise-level security barrier for this “degree of freedom”. Agents can evolve by themselves, but each operation is within the security boundary and on the audit link. OpenClaw and Hermes Agent represent two different evolutionary paths in the field of AI agents-one pursuing ecological breadth and stability, and the other pursuing intelligent depth and self-growth. There are no absolute advantages and disadvantages of the two, only the adaptation of the scene. The value of RDS AI applications lies in the fact that they are not bound to any framework, but provide enterprise-level AI agent access, security audit, and observability for all frameworks. No matter how the agent ecosystem evolves, the requirements of enterprises for secure database access, intelligent O&M, and compliance audit remain unchanged. This is the long-term value of RDS AI applications as the “data base” in the AI agent era. The framework is changing, the data base is unchanged. To select an ApsaraDB RDS AI application, you must select the greatest common divisor that is flexible and secure in the Agent era.
可以预见，这一趋势将在未来深刻影响IDC行业格局
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