How 'Hermes' Sends Another Signal Toward The Next Evolution Of AI Beyond Chatbots, Following OpenClaw

The arrival of large language models marked a turning point in how people interact with AI.

When ChatGPT was released to the public by OpenAI, it made conversational AI widely accessible and practical for everyday use. In the years that followed, tools like Claude and Google Gemini further refined the experience, improving accuracy, speed, and overall usability. Despite these advances, the core interaction model has remained largely unchanged: users ask questions, and the system responds within the limits of a single session.

But more recently, attention has begun to shift toward a different model altogether.

Instead of focusing purely on conversation, developers are building systems designed to persist, remember, and take action over time.

And Hermes is part of this newer class of software, often described as AI agents.

Developed by Nous Research, it began gaining wider attention in early 2026 as interest in agent-based systems accelerated.

With more than 100,000 GitHub stars in 10 weeks, Hermes describes itself as "not a coding copilot tethered to an IDE or a chatbot wrapper around a single API," but "an autonomous agent that lives on your server, remembers what it learns, and gets more capable the longer it runs."

Having spawned one of the fast-growing ecosystem of community-built GUI wrappers, Hermes is not a language model. It does not generate intelligence on its own. Instead, it acts as a coordination layer that connects to existing models such as those from OpenAI or Anthropic, as well as locally hosted systems. Its role is to manage context, decide what to do next, and route tasks through available tools.

This structure allows it to behave less like a chatbot and more like a system capable of executing multi step processes.

This distinction places it closer to agent frameworks like OpenClaw than to conversational tools.

While chat based systems are designed for interaction, Hermes and similar frameworks are designed for execution. They rely on an internal loop that evaluates a task, selects an action, carries it out, and then reassesses the outcome. The goal is not just to provide answers, but to complete tasks across multiple steps with minimal supervision.

One of the defining features of Hermes is its memory system.

Traditional chat interfaces treat each conversation as mostly isolated, with limited carryover between sessions. Hermes stores information about users, past tasks, and ongoing projects in a more persistent way.

Over time, it can build a working context that does not need to be reintroduced. This shifts the interaction model from repeated prompting toward continuity, where the system accumulates knowledge about what it has already done and uses it to inform future actions.

Another important component is its use of tools.

Hermes can be configured to access capabilities such as running terminal commands, editing files, browsing the web, or interacting with external services. When a request is made, the system evaluates whether it should respond directly or call a tool.

This decision making loop is what gives it agent like behavior. Instead of producing a single answer, it can break a task into steps, execute them, and return results in a structured way.

Hermes also introduces the concept of reusable skills.

These are structured sequences of actions that can be saved and applied again later. For example, if a user walks the system through a deployment process, Hermes can store that workflow and reuse it when a similar task appears. Over time, this creates a growing library of behaviors that extends beyond simple prompting, allowing the system to reflect prior experience.

In terms of architecture, Hermes is relatively modular.

It typically includes an interface layer, which can be a command line tool, messaging platform, or web interface, and a backend loop that manages reasoning and execution. This loop repeatedly evaluates the current state, decides on an action, and updates memory. The separation between interface, memory, and execution allows users to adapt it to different environments or integrate it into existing workflows.

When comparing it with OpenClaw, Hermes leans more into persistence and personalization.

Hermer has a stronger memory focus, since it's specifically designed to build a long-term memory of its users, the tasks, and given workflows. In other words, Hermes tries to feel like a continuous system that evolves with use. And with its skills system, it emphasizes reusable behaviors, meaning that when it leans how to do something, it can package that into a repeatable workflow.

OpenClaw on the other hand, is more task-oriented. While it can be a self-hosted assistant that lives alongside users 24/7, OpenClaw is more execution-based, or something close to an AutoGPT-style automation.

In other words, Hermes isn't like other LLMs (because it is not). Instead, it's more like in the same family as OpenClaw, but acts like a "long-term AI coworker" rather than just a task runner.

Despite the interest surrounding it, this kind of technology still comes with clear limitations.

Systems like Hermes can be difficult to set up and require a level of technical familiarity that limits broader adoption.

Their performance is also dependent on the underlying models they use, meaning they inherit the same weaknesses, including errors, inconsistencies, and occasional hallucinations. The added layer of autonomy introduces further risk, as incorrect decisions can propagate across multiple steps before being noticed.

There are also practical concerns around reliability and control.

Tasks that seem straightforward in theory can fail in unpredictable ways when executed autonomously, especially when external tools or changing environments are involved. Memory, while useful, can accumulate outdated or incorrect information if not managed carefully. In addition, giving an AI system the ability to run commands or interact with files raises security considerations that are not present in simpler chat based systems.

The growing interest in Hermes reflects a broader shift in how AI systems are being designed and used. There is a move away from single turn interactions toward systems that can operate over longer periods, maintain state, and perform actions with limited supervision. Hermes represents one approach within this trend, emphasizing user control, extensibility, and persistent context. It does not replace chat based tools, but it points toward a model where AI systems are expected not only to respond, but to follow through, while also highlighting the technical and practical challenges that still need to be addressed.