Hermes vs OpenClaw: Which AI Agent Should You Run in 2026

Key Takeaways

• OpenClaw and Hermes respond very differently to the same request. OpenClaw formats and elaborates; Hermes stays plain and tracks unfinished tasks on its own.
• OpenClaw is built for breadth, Hermes is built to get sharper the longer it runs.
• OpenClaw’s security issues are documented and patched; Hermes is just too new to judge yet.
• Both run on the same Cloudways account and pricing, so picking one doesn’t rule out the other.

Ask ten different people which AI agent you should run, and you will get ten different answers, most of which are just returning a GitHub star count.

Of the available options, two names stand out in particular in 2026, OpenClaw and Hermes. Both are open-source, and both run on Cloudways. Yet a conversation with one does not resemble a conversation with the other in the slightest.

To observe the difference firsthand, we sent the same message to both assistants, using the same model on either side. The responses were nothing alike.

This is only the beginning of a longer conversation. This article will document our findings from the test, explore situations where one agent has a decided advantage over the other, and address common pain points reported by users of either assistant once they have chosen one.

No conclusion is reached as to which is better. Cloudways hosts both, and this article seeks only to help choose between them.

How OpenClaw and Hermes Handle the Same Request

We wanted to see what each assistant does with the same request. Rather than speculate, we will demonstrate by running the same instruction through each agent.

The language model was the same in either case, Claude Opus 4.8, on both assistants.

“Message [number] my today’s schedule in a detailed manner. 10am arrival at office, 1pm meeting, 5pm tea break, 8pm departure.”

OpenClaw’s response was formatted, with a header containing the date, four time slots, and a brief fictionalizing beneath each, “Start of the workday. Settle in, review the day’s priorities.”

Otherwise, there is nothing to fault. It is the sort of response one would expect from a virtual assistant.

The more interesting part of the process takes place behind the scenes.

Prior to generating a response, OpenClaw noted that this workspace still had a pending bootstrap.

It had just spawned with no identity set, and was offering to spend two minutes setting one up, or to continue as a “nameless-but-competent assistant for now.” It chose the latter.

Hermes, for its part, produces unformatted text, with no elaboration or fictionalization. Four lines, giving times, and nothing more, followed by an offer to adjust anything.

The back end is equally interesting. Prior to responding, Hermes noted that a previous task was still pending, an auto-reply request from an earlier conversation. Since the WhatsApp bridge was in self-chat mode, it could not see the other side’s replies, and it asked whether it should proceed with that stalled task now.

Neither agent is at fault here.

OpenClaw tends to decorate messages by default and to check in with itself. Hermes, meanwhile, stays plain and does not let a loose thread go just because the conversation moved on.

See the Difference for Yourself

Same test, your own account. Try OpenClaw or Hermes and see how each one actually responds.

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Why OpenClaw and Hermes Are Built on Different Ideas

OpenClaw decorated an otherwise straightforward message with elaborations, while Hermes did not. Hermes noticed an abandoned task, while OpenClaw did not. These differences stem from the design philosophy of each project.

OpenClaw is intended to be a multi-agent control plane. It is meant to host numerous agents at once, which perform specialized tasks, possibly over multiple channels, drawing from a shared but extensive skills library.

In other words, it is less an “assistant” and more of a management console for several “assistants,” each with a different skill set, all of which report to the same inbox.

This functionality explains the self-reflective nature of the test above. An agent that exists to manage several others will necessarily have more to ask of the user at the start of a conversation to understand which tool to use.

Hermes is the opposite of this. It is a single, self-improving agent which runs a narrower set of skills, but with more depth of knowledge on the subjects at hand.

Rather than pull from a shared skills library, it writes and rewrites skills as it goes, based on what the user’s tasks actually require. This is the stated cause of its self-improving nature, of being able to “get better the more it works.” Whether it correctly assesses what should be rewritten is a matter of debate, one covered later in this piece.

Each assistant has clear strengths, but neither is universally more capable. OpenClaw is broader but shallower. Hermes is narrower but deeper.

The discussion above barely scratches the surface of either project. For more, here’s a general overview of OpenClaw and a closer look at how Hermes actually works.

OpenClaw vs Hermes: Feature-by-Feature Comparison

Beyond architectural differences, the two assistants have significant variations in their features. A feature comparison follows.

Feature / Aspect	OpenClaw	Hermes
Architecture	Multi-agent control plane, runs several agents at once	Single, always-on runtime
Memory across sessions	Manual setup	Automatic, hard limit
Dashboard channels (Cloudways)	Telegram, Discord, Slack, WhatsApp	Telegram, Discord, Slack, WhatsApp
Bring your own model	Claude, GPT, Gemini, OpenRouter	Claude, GPT, Gemini, OpenRouter
Skills	Tens of thousands pre-built, install as needed	60+ built-in, self-crafted over time
Security track record	6 CVEs, 341+ malicious skills found and purged	0 reported CVEs, still early
Release maturity	137 releases	11 releases
Cloudways pricing	Same four tiers, $9.99 to $79.99/month	Same four tiers, $9.99 to $79.99/month
Best known for	Breadth and configurability	Self-improving nature

Security and release figures via a community analysis of 1,300+ comments across 25 r/openclaw threads.

A table like the one above lends itself to oversimplification. A few rows are worth expanding on before continuing.

The similarity in channels is no coincidence. Cloudways developed both integrations at the same time. The “0 reported CVEs” for Hermes is not a stronger security position, necessarily, only a shorter runtime that has allowed for fewer issues to be found.

Five Situations Where One Agent Makes More Sense Than the Other

The demonstration above shows the philosophical differences between the two assistants, but what do those differences actually mean? This section covers five specific situations, where one agent is demonstrably more appropriate and advantageous to the task at hand.

#1 Automating a Repetitive Coding or Ops Task Solo

We asked Hermes to confirm that cloudways.com was up, and summarize the page. It offered a neat, correct summary, with HTTP status and actual page content, in 16 seconds.

We then asked it to perform the same thing on digitalocean.com, without further explanation.

Hermes didn’t prompt us as to what “the same thing” meant; it performed it, in the same format, and returned in 11 seconds with a result. It included one addition nobody asked for, that both domains were live and functioning normally, and linked the two results together on its own initiative.

There was no sign of the actual skill-writing process in use. Not in the response, not in the backend.

That’s not the same as it not happening at all. It’s more likely that the process, execute, evaluate, extract, refine, isn’t triggered until the action is taking place more than once, or in more than one session, not just a second request in the same conversation.

What this test demonstrates is narrower, but still valuable. Hermes didn’t need to be informed what “same thing” meant in order to repeat it correctly.

#2 Managing Client Work Across Multiple Channels

We asked OpenClaw to take two separate actions, for two different clients, to check the SSL certificate for a domain, and to summarize the uptime. OpenClaw didn’t attempt to determine which task belonged to which client, or make assumptions about the domain in either case.

It asked for both, first, noting what each was, “I need the actual site URLs/domains to do either check, I don’t have them yet“, and flagged that an uptime check wasn’t the same as a tracked percentage required to monitor, which it didn’t have access to.

We provided the domains, cloudways.com for the first client, digitalocean.com for the second, and OpenClaw executed the checks for both.

For the SSL certificate, it returned the actual issuer, validity dates, expiry date, and approximately 140 days of runway before needing renewal. For the uptime, it returned real response timing, connect time, TLS handshake, and time to first byte.

It maintained the association between task and client throughout the exchange, and closed by recommending a next step for both, a renewal notice for the first, ongoing monitoring for the second, correctly associated but without having been reminded which was which.

One small note, worth calling out. Both agents in their own separate tests in this piece, draw the same distinction in their own processes, that a current status check isn’t the same as historical record. Neither pretend to have the more difficult number they don’t actually have.

That’s the actual strength of OpenClaw in this case; give it a few distinct things at once, and it’ll remember which is which without being reminded.

Juggle Every Client Without Dropping One

OpenClaw keeps track of who’s who across channels, so you don’t have to repeat yourself.

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#3 Choosing Based on Security and Compliance Needs

In both cases, the assistants have limited but demonstrably different security records, and it’s worth examining why before either could influence a choice.

OpenClaw’s record is known; it’s had a demonstrable vulnerability, since patched, and has seen 341+ malicious skills appear in the wild, since found and purged.

It’s not without its issues; those issues have been known and resolved.

Hermes, meanwhile, has zero reported vulnerabilities to its name, and hasn’t seen the same level of scrutiny due to its smaller install base since launch.

If choosing between the two solely on the basis of security, OpenClaw actually has the more demonstrable record. A vulnerability that’s known and resolved speaks louder than one that’s simply not found yet, because nobody’s looked hard enough.

Either way, neither agent is demonstrably more compliant on its own. That depends on the hosting environment and restrictions placed on it, isolation, access controls, and who’s watching for the next issue, rather than which open-source project has fewer incidents reported against it in its changelog.

That’s exactly where Cloudways’ side of this matters more than either project’s own changelog, covered later in this piece.

#4 When Picking One Assistant Is Less Useful Than Using Both

The discussion so far has framed the two assistants as if picking one excludes the possibility of the other, but in practice it doesn’t. There’s a real workflow that makes use of both at the same time, and it’s worth covering on its own.

OpenClaw, as a multi-agent control plane, is well-suited to the task of managing multiple tasks across multiple channels, determining which task goes to which agent, and when.

Hermes, as a self-crafting agent, is well-suited to the execution of repeated or similar tasks, growing slightly sharper with each repetition.

Both are capable of working in the same conversation, at the same time, on the same account, requiring minimal additional setup.

They share a common skill format, which allows, but doesn’t guarantee, the occasional skill written for one to be used on the other. Mostly, it doesn’t work that way. Skills are generally written for one and not the other.

The distinction between the two isn’t as sharp as it sounds, but it’s real enough to be worth making. This isn’t a recommendation for someone just beginning to work with either project. It’s for someone who’s had enough of one to look at the edges, the breadth wasted on a task that only needs to become sharper over time, or the narrow focus that can’t manage the multi-channel routing the other was built for.

Both projects run on the same Cloudways account, and don’t require separate setup. More on exactly what that entails a little further down.

#5 Fixing the Same Recurring Problem Every Month

Some problems don’t happen once. They show up every few weeks, in roughly the same shape, and someone has to solve them all over again each time.

One documented example, a database replica kept lagging behind on a recurring basis. Someone worked through the fix manually the first time. Hermes turned that fix into a reusable skill afterward, on its own, without being asked to write one.

The next time the same replica issue showed up, the skill was already there.

That’s a genuinely different experience than solving the same problem from scratch every month. It only pays off, though, if the problem actually repeats. A one-off issue gets no benefit from a system built to remember patterns.

OpenClaw can handle the same kind of fix. It just won’t write the skill down afterward unless someone builds and uploads it to the library themselves. The task gets done either way. Only one of the two agents remembers doing it.

Let It Remember the Fix for You

Hermes turns a solved problem into a skill it never forgets. See it happen with your own recurring task.

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What People Get Wrong When Choosing Between OpenClaw and Hermes

A few assumptions come up over and over again, and most of them are incorrect.

Bigger star count, better tool. Both assistants saw real viral growth, but OpenClaw had five name changes in ten weeks, each of which contributed to a new wave of stars. Some of that reflects actual adoption; the rest is the same tool being rediscovered five separate times. Neither agent’s growth curve is an indicator of its ability to actually do what you need it to.

Hermes’ growth is entirely organic. A community study of over 1,300 Reddit comments found a vocal minority, approximately 15%, that believe Hermes’ growth is exaggerated, some of it promoted rather than organic. Worth being aware of before taking either project’s momentum as a sign of quality.

Self-improving means self-correcting. Hermes writes its own skills, based on what it judges to have been successful. What it judges, and what’s actually true, aren’t always the same thing, a fact both the self-evaluation research and the live test earlier in this piece speak to. A self-writing agent is only as good as its own assessment of what worked.

A larger skill library means better coverage. Quantity isn’t quality, and volume alone doesn’t indicate that a given skill will actually apply to your specific situation.

Ease of setup trumps actual fit. OpenClaw’s breadth is wasted on a task that only needs to get sharper with repetition. Hermes’ narrow focus won’t support the multi-channel orchestration OpenClaw was built for. Easy setup addresses a Tuesday afternoon problem; whether the tool actually does the job is a separate matter.

How OpenClaw and Hermes Handle Memory, Security, Skills, and Hosting

Four things determine how either agent actually behaves on a daily basis, beyond what it claims to be able to do. Here’s how they compare on each.

Managing Memory as It Grows

OpenClaw doesn’t remember anything across sessions by default. Cross-session memory requires manual setup, connecting a database, and configuring what gets saved, by whoever’s running the instance.

Hermes manages this automatically, writing to a memory file as it goes. That file has a hard size limit; when it’s hit, the very next task that requires memory simply fails, mid-conversation, until someone opens the file and manually deletes some entries to make room.

Neither approach is maintenance-free, but one requires setup up front, and the other requires occasional cleanup, usually at the most inconvenient time.

Handling Security Risks on Either Platform

Covered in detail earlier in this piece, so this is just the short version. OpenClaw’s had a real, patched incident. Hermes hasn’t had one yet, mostly because there aren’t enough people who’ve had the chance to look.

Neither fact is an indication of safety; that depends on how either is hosted, rather than which has shorter changelog entries.

Comparing Skill Ecosystems: ClawHub vs Agent-Generated Skills

OpenClaw pulls from ClawHub, a community library with tens of thousands of entries. Install what you need; the tradeoff is quality control, and that’s how the bad skills got through before being caught.

Hermes writes its own, based on tasks it’s completed. The library begins smaller and grows specifically around how you use it.

The tradeoff is the self-evaluation issue covered earlier; a skill only gets written if Hermes decides the task was successful, and that doesn’t always happen.

Self-Hosted vs Managed Hosting

Run either agent yourself, and you own the server, the updates, and the security. More on exactly what changes with managed vs self-hosted AI agents a little further down.

Common Problems People Run Into With OpenClaw and Hermes

Three specific issues come up often enough in practice that they’re worth addressing directly.

Does Hermes Overwrite Manual Skill Edits?

Sometimes, yes. Hermes will automatically patch its own skills when it determines that one has gone out of date, and that patch can overwrite changes someone made by hand. There’s no prompt before it does so asking permission.

This is a documented concern from long-term setups, not an edge case. If you’ve made manual edits to a skill, it’s worth periodically checking that it still does what you want it to.

Why Does a Channel Connection Sometimes Get Stuck?

We actually ran into this ourselves, mid-test. Hermes flagged that its WhatsApp bridge was in “self-chat mode”, and couldn’t see replies coming back from the other side of a conversation it had initiated. It noticed on its own and asked whether to continue regardless.

That’s a relatively specific, technical issue, and worth being aware of before encountering it yourself. If a channel connection seems to stop responding to the other party, checking that the bridge isn’t in self-chat mode before assuming something is wrong is a reasonable first step.

How Do You Migrate From OpenClaw to Hermes?

There’s an actual, documented path for this, a command called hermes claw migrate. It pulls in your OpenClaw configuration, workspace files, and stored context, and has a dry-run preview before actually making any changes.

Not everything translates, but skills written specifically for OpenClaw’s format generally don’t, and anything referencing OpenClaw-specific integrations needs to be rebuilt separately. It’s a real head start, not a perfect copy.

How to Deploy Hermes or OpenClaw Without Managing a Server

Everything so far has been about choosing the right agent. Here’s what actually happens after that point.

1. Create a Cloudways account and select a plan, starting at $9.99 a month

 

2. Choose OpenClaw or Hermes.

3. Connect your model’s API key, Claude, GPT, Gemini, or an OpenRouter model.

4. Choose which messaging channels to connect.

The server itself is handled on Cloudways’ end, along with the security configuration that most self-hosted setups leave to the operators.

Updates get tested before being deployed to your instance, for either agent. A release that causes problems gets held back rather than being pushed to your setup.

Each instance runs isolated from every other customer’s. Your model, billed by whoever’s provider you selected. Your server, billed by Cloudways. Those two remain separate.

Running both isn’t a second process. Same account, same dashboard, one instance each for the agents, running independently. Picking one isn’t an exclusion of the other.

The Bottom Line

Go back to that first test. OpenClaw dressed up a plain schedule and checked in about its own identity. Hermes kept it plain and remembered a stalled task on its own. Neither one was incorrect. They were simply prioritizing different concerns.

That’s the actual choice to be made, not which one performed better on an arbitrary feature list. Breadth or depth. Multiple channels and a large skill library to pull from, versus one thing that gets quietly sharper on your specific work the longer it runs.

Cloudways runs both, same account, same pricing, same server handling either way. Picking wrong is less of a concern than it seems at first. Try one; if it’s not the right fit, the other is a few clicks away rather than a rebuild.

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