A few months ago, the conversation around autonomous AI agents was dominated by OpenClaw. That has shifted. A newer project called Hermes has surged in attention, its GitHub stars climbing fast, with a visible wave of users migrating to it. Hermes’ distinguishing claim is striking: it learns from itself in a loop, so the more you use it, the better it gets.
The obvious question, in a field that runs on hype, is whether that claim holds up. After installing it, configuring it, and spending real money running it, here is an honest assessment of what Hermes actually does, how it differs from OpenClaw, and whether it is worth switching.
First, what these agents actually are
If you have only ever used ChatGPT, Claude, or Gemini, you have used a chatbot. You open it, you talk to it, it answers. Modern chatbots can take some actions, reading your email or doing a few things on your behalf, but they are fundamentally reactive and limited in autonomy.
An autonomous agent is a different category. OpenClaw and Hermes are autonomous agents that typically run on an external computer or a VPS, controllable from your phone, with access to their own memory, their own tools, their own system prompt, and the ability to execute tasks on their own. They do not just answer. They act, repeatedly, until a job is done.
The gap between a chatbot and an agent is the gap between asking someone for instructions and handing the whole task to someone who completes it. Chatbots are increasingly gaining agentic features, but tools like OpenClaw and Hermes were built agent-first from the ground up.
Why people are leaving OpenClaw ?
OpenClaw was the first widely used agent of its kind, and as a version-one product it carried version-one problems. Four of them come up repeatedly in the migration to Hermes.
Installation was hard. Even with one-click installers, using OpenClaw to its full potential quickly became a technical challenge.
Memory was weak. OpenClaw did not have persistent memory configured by default. It could be set up, but it took deliberate effort, and many users complained the agent simply forgot things. Tell it “stop responding that way” or “remember to do this,” and half the time it would lose the instruction, because it lived only in the context window rather than in a persistent file.
Maintenance was painful. The infrastructure was complicated to keep running.
Updates broke things. New versions frequently broke working setups, which created a bind: skip updates and risk security issues, or apply them and watch the agent stop functioning.
The headline feature: a self-improving agent
The reason Hermes is getting attention is its self-improvement loop, and it is genuinely the most interesting thing about it.
Here is how it works in practice. The first time you ask Hermes to do something it has never done, it does not already know the steps. So it asks you questions, works through the task with you, and uses the underlying model to figure out a solution. Crucially, it keeps trying until it succeeds. By default it is configured to attempt a task up to 90 times before giving up.
Once it succeeds, something useful happens automatically. Hermes writes a skill: a step-by-step recipe file describing exactly how to complete that task. You did not ask it to. The next time you request the same kind of work, it uses the skill directly instead of reasoning the whole thing out again. And as you give feedback over time, it keeps refining that skill.
A concrete example from testing: asking Hermes to analyze the comments on a YouTube channel’s latest video and report back over WhatsApp. It had never done this before. It navigated to the link, took screenshots, read the first 50 comments, and produced a detailed report on recurring questions, weak signals, and constructive criticism. Then, unprompted, it asked whether it should turn this into a recurring scheduled job to analyze comments on every new video. In the background, it had created a new skill named for YouTube comment analysis, ready to reuse.
Self-learning feels obvious because humans do it instinctively. For an AI agent it is not obvious at all. OpenClaw required you to install skills by hand and explicitly instruct it to modify them. Hermes does this autonomously, which is the single biggest functional difference between the two.
Inside Hermes: memory, skills, and scheduled jobs
A few core concepts make the system work, and they are worth understanding because they generalize across agent platforms.
Skills are recipes. A skill is a file that describes, step by step, how to perform a task. Their value is twofold: they save tokens, because the agent does not have to rediscover how to do something each time, and they reduce errors, because a documented procedure is more reliable than improvised reasoning. Hermes ships with many preinstalled skills, and you can add more, but the real power comes from the skills your agent builds for your specific workflows.
Memory is what separates an intelligent agent from a forgetful one. Hermes simplifies memory into a few components: user notes, a user profile holding everything the agent knows about you, and what it calls its “soul,” its own identity. Where OpenClaw scattered this across many small files, Hermes consolidates it. This persistent memory is why the agent gets smarter with use. It remembers your profile, who it is, and the notes accumulated across every conversation.
Cron jobs are scheduled tasks that run themselves on a timer, and you set them up simply by asking. One running example: a recurring real-estate search that surfaces new apartment listings several times a day. When that job once broke, pasting the error to Hermes was enough. It diagnosed that the job was executing in the wrong directory, fixed the wrapper, confirmed it would run every 30 minutes, and logged a self-improvement review with updated memory. The failure triggered the improvement loop rather than requiring manual repair.
The Web UI and the kanban
Most people start with Hermes in a terminal, and the terminal experience is not pleasant. You can connect it to WhatsApp, Telegram, iMessage, and Signal, as you could with OpenClaw. But the bigger discovery is an open-source Web UI for Hermes that substantially improves the experience.
The Web UI surfaces what the terminal hides: scheduled jobs, a kanban board, the skill library, visible memory, workspaces, multiple users, a todo area, usage insights, and logs. For day-to-day work at a desk, it is a real upgrade. WhatsApp stays useful for when you are away from your computer.
The kanban deserves a specific mention, because it exposes one of the more powerful capabilities: sub-agent orchestration. When you give Hermes a large task, an orchestrator agent can deploy multiple sub-agents, each with its own profile and even its own model, to execute pieces of the work in parallel. The kanban becomes your monitoring board. In one test, asking Hermes to build a website from a YouTube channel’s videos led it to pull the video list, propose a set of tasks (channel analysis, site design, and so on), create worker profiles, populate the board, and run the sub-agents through to completion, producing a basic working site with little input.
Sub-agents are not just an organizational nicety. Each sub-agent has its own context, so delegating isolated work to them costs fewer tokens than running everything in one bloated main context. Orchestration is a cost strategy as much as a capability.
Where to run it, and the security question
A warning that matters: do not install Hermes on your personal computer. By default it asks you to approve each action, but the entire point of an autonomous agent is that you want it to act without babysitting, and an agent with unsupervised access to all your personal files is a genuine risk.
The safer pattern is to run it on a separate machine or a server, where it only has access to what you deliberately put there. Several installation paths exist. One straightforward option is deploying it through a managed host that offers open-source app catalogs (Hostinger is one such option), which can bundle the Web UI version into a single container and remove much of the setup friction. A plan with a reasonable amount of RAM is advisable, since these agents are resource-hungry.
The real obstacle: cost
This is where most people give up, so it deserves honesty.
Running an autonomous agent on premium models is expensive. Agents are token-hungry, and per-token pricing on models like Opus or GPT-5 adds up fast. There are two ways to power these agents: a subscription, where you hit usage limits quickly because the agents consume so much, or the API, where you pay per token and the bill climbs steeply.
There is an important wrinkle. Early on, some users powered their agents with a $200 Claude subscription for effectively unlimited requests, which made the agents cheap to run. Anthropic has since prohibited using its Pro and Max subscriptions on external tools like OpenClaw and Hermes, which pushes users back to per-token API pricing.
The current workaround is a cheaper model. DeepSeek V4 stands out on price: three days of use came to roughly 40 cents, against $17 burned in a few conversations on Anthropic’s largest model. The Flash variant is enough for most tasks, and DeepSeek’s cache-hit pricing makes repeated content especially cheap.
The caveat is real and worth stating plainly: DeepSeek is a Chinese company, and using it means accepting that your data leaves for China. For anyone handling sensitive information, that is a meaningful tradeoff. The privacy-preserving alternative is to run a model locally through Ollama, which Hermes supports. A sensible middle path is mixing models by profile: a cheap model like DeepSeek for orchestration, a stronger model like GPT-5 for coding tasks.
Cost is the hidden reason most people abandon autonomous agents, not capability. The skill that actually unlocks daily use is matching the right model to the right task: cheap models for routing and orchestration, premium models reserved for the work that genuinely needs them.
The verdict
Is Hermes hype or substance? On a hands-on basis, the substance is real. It is meaningfully easier to install than OpenClaw. It produces far less file clutter thanks to better skill and memory management. The Web UI gives genuine visibility into what the agent is doing. And because persistent memory is the default rather than an opt-in, it forgets much less.
Whether you should switch depends on your situation.
If you already run a heavily configured OpenClaw setup, with your own self-improvement system and file structure in place, the upgrade is less dramatic. The Web UI, and specifically its kanban, is the one feature that stands out as a true differentiator. Without that, a mature OpenClaw install already does most of the same things.
If you are new and have never used either, go with Hermes. It is far easier to configure, the Web UI removes the burden of managing a file system by hand, and you start fresh on something modern.
Either way, the larger point stands. The biggest mistake in AI right now is staying parked on a free chatbot asking basic questions and concluding the technology is not ready. The real frontier is in testing tools like these: installing them, configuring them, experimenting, and forming a judgment from experience rather than from hype on social media.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot like ChatGPT or Claude is reactive: you ask, it answers, with limited ability to take actions. An autonomous agent like Hermes or OpenClaw runs on its own machine with its own memory, tools, and system prompt, and executes multi-step tasks independently, repeating attempts until the job is done.
What makes Hermes different from OpenClaw?
The headline difference is the self-improvement loop. When Hermes completes a new task, it automatically writes a reusable skill and refines it over time, without being told to. It also ships with persistent memory enabled by default, is easier to install, and has an open-source Web UI with a kanban board for monitoring sub-agents. OpenClaw can do much of this, but it requires more manual configuration.
Should I migrate from OpenClaw to Hermes?
If you already have a well-configured OpenClaw setup, the main reason to switch is the Web UI and its kanban view; otherwise the gains are modest. If you are a new user, Hermes is the easier starting point.
Why is running an autonomous agent so expensive?
These agents consume large numbers of tokens because they work in long, multi-step loops. On premium models like Opus or GPT-5, per-token API pricing escalates quickly. Subscriptions hit usage limits fast, and Anthropic now prohibits using its Pro and Max subscriptions on external tools like these.
How do I reduce the cost?
Use a cheaper model. DeepSeek V4 is dramatically less expensive, with the Flash variant sufficient for most tasks. The tradeoff is that DeepSeek is a Chinese company and your data leaves for China. For privacy, you can run a local model through Ollama. Many users mix models by profile: cheap models for orchestration, stronger models for code.
Can I install Hermes on my own computer?
It is not recommended. An autonomous agent with unsupervised access to all your personal files is a security risk. Run it on a separate machine or a server where it only has access to what you deliberately provide.
