# OpenClaw or Hermes Agent: a task-based comparison
> Messengers, memory, skills, models, installation, and security: a comparison of OpenClaw and Hermes Agent and how they work together.
Source: https://ailoot.ai/blog/en/openclaw-vs-hermes-agent/
Canonical: https://ailoot.ai/blog/en/openclaw-vs-hermes-agent/
Language: en
Publisher: Ailoot (https://ailoot.ai/)
Translation: guide-189
Revision: sha256-d0f3b64bcd85b1adfd116da1bbb08d10dfbdc6d884c0be18f46d865df645bda0
People are increasingly comparing OpenClaw and Hermes Agent in searches: they type “openclaw vs hermes” and try to figure out which one to install. From the outside, they really are twins: both are open source, both run on your machine, and both reply in Telegram. There is a difference, but it lies in details you can’t see from the GitHub description.
This comparison focuses on where each one is actually more convenient. Below, point by point: messengers, memory, models, installation, cost, and security. At the end, there’s a breakdown by scenario, so you can find your case right away without reading the whole thing.
Connecting models and working with APIs are covered in the [Ailoot documentation](https://ailoot.ai/docs/).
## How are OpenClaw and Hermes Agent alike, and what’s the main difference?
Key takeaway
Both are open-source AI agents that run on your computer or server, work with any model, and reply in a messenger. They have about eighty percent in common. The main difference is their focus: OpenClaw bets on broad messenger support and a large community, while Hermes focuses on self-writing skills, memory across sessions, and a desktop app.
They belong to the same category. Each is an agent that runs on your machine: it reads and edits files, runs commands, accesses the internet, and writes to you in a messenger you already use. It’s neither a code editor nor a browser service. The agent can store its configuration and conversation history on your own computer. That does not make model processing local: a selected cloud model receives the prompts and context you send, and external tools or messengers transmit their own data. A fully local workflow requires a local model and local tools, without cloud or messenger dependencies.
To make the difference clear right away, I’ve put everything into one table. I’ll go through each row separately below.
| Feature | OpenClaw | Hermes Agent |
|----|----|----|
| Created by | OpenClaw open-source community | Nous Research |
| Repository created | November 24, 2025 | July 22, 2025 |
| GitHub stars (22.08.2026) | 387 thousand | 234 thousand |
| License | Open source | Open source, MIT |
| Models | Claude, GPT, Gemini, local models, and others | Any of twenty providers |
| Messengers | Telegram, WhatsApp, Discord, Slack, iMessage, and 10+ more | Telegram, Discord, Slack, WhatsApp, and 17+ more |
| Memory across conversations | Markdown files in the working folder | MEMORY.md and USER.md files |
| Self-writing skills | Ready-made skills from the project's catalog | Writes its own after complex tasks |
| Desktop app | Windows Hub, still in beta | Available for macOS, Windows, Linux |
| Scheduled tasks | Built in | Built-in scheduler |
| Native model subscription | No, bring your own keys | Nous Portal, 300+ models |
Their personalities are already clear from the rows. OpenClaw covers more messengers and is more popular by star count: it has a larger community and more channels out of the box. Hermes is more self-contained: it has mature memory features, the agent accumulates skills on its own, there’s a full desktop app, and it has its own model subscription.
If you’re just getting to know these tools, I have separate detailed guides: [what OpenClaw is and how to install it](https://ailoot.ai/blog/en/openclaw-install-telegram/) and [what Hermes Agent is and how to set it up](https://ailoot.ai/blog/en/hermes-agent-install-setup/). I won’t recap those two pieces here. I’ll put the two tools side by side and compare them point by point.
## Which should you choose if messengers and Telegram are your priority?
Key takeaway
If you only need Telegram, both are equally good: you connect through a token from BotFather and a restricted access list. The difference appears when you use several messengers. OpenClaw supports more out of the box, including iMessage and WhatsApp, while Hermes is a little stricter about access and groups in its Telegram setup.
I’ll start with a common case: someone needs an agent specifically in Telegram, and nothing else matters. There’s almost nothing to choose between here. The process is the same for both: create a bot with @BotFather, get its token, enter it, and approve yourself with a pairing code. No one else can message your agent without your approval—that’s how both are configured by default, and rightly so.
The difference shows up when you use more than one messenger. OpenClaw was designed from the start as a gateway for many channels at once: Telegram, WhatsApp, Discord, Slack, iMessage, and about ten more. If you want one process that replies both in your work Slack, your personal Telegram, and your family WhatsApp, OpenClaw fits that profile.
Hermes also supports many messengers, more than twenty, and its Telegram integration is carefully built: voice messages with transcription, images, files, replies in groups, a one-hour pairing code, and a list of pending requests. The difference isn’t “can it do it or not,” but that OpenClaw has a broader list of less common channels, while Hermes pays a little more attention to access details within Telegram.
I’ll mention one common trap right away, because both camps stumble over it. Telegram bots have privacy mode enabled by default: in a group, a bot sees only commands and replies directed to it. To make it read everything, turn privacy mode off with @BotFather, then remove the bot from the group and add it again. Telegram remembers the privacy setting from when the bot was added and doesn’t update it by itself. That’s Telegram’s behavior; the agent has nothing to do with it, and the rule is the same for OpenClaw and Hermes.
## Which should you choose if you want memory and skills that accumulate on their own?
Key takeaway
Hermes Agent has the edge here. Both have memory in the form of markdown files, but Hermes manages it more rigorously (separating environment facts from the user profile) and writes its own skills after complex tasks. OpenClaw also keeps memory and offers ready-made skills from its catalog, but it doesn’t create them itself.
In my opinion, this is the most important row for anyone who wants an agent “for the long haul.” Both handle memory honestly and transparently: it’s made up of ordinary markdown files in the working folder that you can open and read yourself. No black box. OpenClaw has files about who you are and what the agent remembers from past conversations. Hermes splits memory across two files: one contains the agent’s own notes about the environment, and the other is your profile—how to respond to you and what you like. Both are inserted at the start of every conversation.
The difference is in skills. Hermes has a built-in cycle: after a complex task, the agent writes down a procedure for itself and applies it on its own the next time. It also searches its own past conversations for answers. OpenClaw has skills too, but uses a different model: you install ready-made ones from the project's catalog, where third-party skills are checked for safety before installation. OpenClaw doesn’t write new procedures for itself.
For me, that’s the fork in the road. With skills plus memory in Hermes, an agent that remembers context and accumulates procedures works noticeably better after a month than on installation day. If that kind of accumulation matters to you, look at Hermes.
## Which should you choose for models: any provider or a local model?
Key takeaway
Both work with any model, including local models through Ollama, so they’re equally flexible. The difference is how convenient they are to get started with: Hermes has a native Nous Portal subscription for 300+ models and sign-in through existing ChatGPT, Claude, or Grok subscriptions. OpenClaw has no native subscription; you connect your own keys or a local model.
It’s important to clear up a common concern here: neither agent is tied to a single provider. OpenClaw and Hermes both connect to the model you choose: Claude, GPT, Gemini, Qwen, DeepSeek, or a local model running on your own hardware. The agent doesn’t think on its own; it just passes the request to the model you selected. They’re equally flexible in this respect.
The difference is in how convenient the first step is. Hermes has Nous Portal, a native subscription from its developers that covers more than three hundred models and immediately provides web search, images, and voice generation. You can also sign in through a subscription you may already have: ChatGPT, Claude Max, SuperGrok, Qwen, or GitHub Copilot. Confirm the sign-in in your browser, and you pay exactly what you paid before, with no new subscription.
OpenClaw doesn’t have its own subscription. You connect a provider key (Anthropic, OpenAI, Google) and pay for actual usage, or install a local model through Ollama and pay nothing for tokens. A nice detail: if Claude Code is already installed and configured on your machine, OpenClaw can use that sign-in, so you don’t need a separate key.
Both have the same practical limitation with local models. They cost nothing in money, but they require capable hardware, and small, compressed models cut off context and handle long tasks less well. Hermes also has a strict requirement: the model must have a context window of at least 64 thousand tokens, or Hermes won’t start with it. A local model is enough to try things out, but for daily work, cloud models are still more reliable for both agents.
## Which is easier to install, OpenClaw or Hermes Agent?
Key takeaway
Both install with one command on macOS and Linux and have a Windows option. Hermes is a little friendlier to newcomers: one installer installs both the command line and the desktop app, and the `hermes doctor` command clearly shows what’s wrong. OpenClaw offers three Windows installation paths, which is more about flexibility than simplicity.
There’s almost no difference on macOS and Linux: in both cases, installation comes down to one line in the terminal that downloads everything needed. Then you run a check command: `openclaw doctor` for OpenClaw and `hermes doctor` for Hermes. Both show what was installed, what’s missing, and how to fix it, so anyone who isn’t afraid of the terminal can install either in a couple of minutes.
The difference is on Windows. Hermes has two options: download the Hermes Desktop installer and launch it like a regular program, or run one line in PowerShell. The installer immediately installs both applications—the command line and the chat window—and gives newcomers an easy way in.
OpenClaw has three Windows options: the Windows Hub app (still in beta), the PowerShell installer, or installation inside WSL2 as the most compatible option. That means more flexibility, but also more forks in the road where you can get stuck. If the words “distribution” and “systemd” mean nothing to you, choose Windows Hub for OpenClaw; it sets up the required environment itself.
OpenClaw also has a new installation trap when using npm: recent npm versions block package installation scripts by default, and installation stops without a special flag. Hermes has no such step; its installer fetches Python, Node, and everything else on its own. So for sheer ease of the first launch, I’d give Hermes a slight edge, but anyone willing to open a terminal once can manage either one.
## Which is safer, and what should you know before the first launch?
Key takeaway
Their security models are the same: one trusted owner per agent, access through pairing codes, and no exposing the agent to the public internet. OpenClaw has a built-in security audit command that checks access and ports. Scams around high-profile projects are a separate issue: download both agents only from official sources.
The basic principle is the same and easy to understand for both. An agent has access to your machine: files, commands, and browser. That means whoever can message it can act on your computer through it. So the rules are the same for OpenClaw and Hermes: use pairing codes or an access list, don’t expose the agent publicly without a need, and require confirmation for dangerous commands.
One detail about OpenClaw: the built-in command `openclaw security audit` checks for open access, overly broad tool permissions, weak tokens, and exposed ports, and with the `--fix` flag it repairs some findings automatically. Hermes has its own approach: dangerous commands ask for confirmation by default; there’s a full-trust mode that you enable yourself, deliberately, and you can run the agent in a container or on a separate machine for full isolation.
And here’s one rule that isn’t about software, but can cost real money. Scammers circle high-profile open-source projects. When OpenClaw was hastily changing its name, squatter bots snatched up the old addresses in seconds and started promoting crypto scams. So download both agents only from their official websites and GitHub repositories. Any “OpenClaw token” or “Hermes token” is a scam; neither project has any cryptocurrency.
## How much does each cost, and where can you really save?
Key takeaway
Both tools are free: they’re open source. You pay only for the model that does the thinking. The difference is that Hermes has its own Nous Portal subscription starting at 20 dollars and lets you sign in through subscriptions you already have, while OpenClaw offers only your own keys or a local model with no monetary cost. Both can cost nothing through Ollama.
It’s easy to get confused here, so I’ll break it down. You don’t have to pay for OpenClaw or Hermes as software: both are open source. The only money goes to the model you connect to the agent. And that’s where the options diverge.
Hermes has four options, two of which let you avoid starting a new subscription. First is Nous Portal, starting at 20 dollars per month, a native subscription for 300+ models plus search, images, and voice generation. The second, which many people overlook, is signing in through a subscription you already have: ChatGPT, Claude Max, Grok, Qwen, or Copilot. Third is your own API keys. Fourth is a local model, free in monetary terms.
OpenClaw essentially has two options: your own provider key, with payment for actual usage, or a local model through Ollama, with no payment for tokens. It has no native subscription like Hermes, and no sign-in through an existing subscription either, apart from the nice exception of Claude Code already configured on your machine.
The takeaway on cost is simple. If you already have a ChatGPT or Claude Max subscription and don’t want to pay twice, Hermes will save you the cost of a separate subscription by letting you sign in with it. If you pay the provider directly with a key or use a local model, there’s no cost difference between the agents: your money goes to the model, and both agents are free.
## Which agent should you choose for your task?
Key takeaway
A short breakdown by scenario. Many messengers and a large community: OpenClaw. Memory, self-writing skills, and a desktop app: Hermes. Already have a ChatGPT or Claude subscription: Hermes saves you a second subscription. Only Telegram and nothing more: choose either; they’re equal here.
So you don’t have to reread everything above, I’ve put the options in a table by task. There are no promises here: you decide based on your situation, because everyone has different hardware, habits, and existing subscriptions.
| Your task | Choice | Why |
|----|----|----|
| One agent across many messengers at once | OpenClaw | More channels out of the box, including iMessage and WhatsApp |
| An agent that remembers context and accumulates skills | Hermes Agent | Structured memory and self-writing skills |
| Already have a ChatGPT or Claude Max subscription | Hermes Agent | Sign in through it, with no second subscription |
| Need an app with a window over the terminal | Hermes Agent | Ready-made app for macOS, Windows, Linux |
| Telegram only, nothing else | Either | They’re nearly equal for Telegram |
| The gentlest possible Windows installation | Hermes Agent | One installer installs everything at once |
| A large, active community and many ready-made skills | OpenClaw | More stars and ready-made skills from its catalog |
| Local model processing | Either | Both support Ollama; a fully local workflow also requires local tools and no external messenger/cloud calls |
How to read this: if your request is “I want one bot in all my chats,” OpenClaw has the edge. If your request is “I want an agent that runs on a server, remembers me, and works better every week,” Hermes has the edge. Everything else comes down to which subscriptions you already have and whether you’re afraid of the terminal.
If you’re still deciding where to start with vibe coding, check out these two roadmaps: [which tasks you can really hand off to an AI agent](https://ailoot.ai/blog/en/business-tasks-for-ai-agents/) and [what to code with in 2026](https://ailoot.ai/blog/en/ai-coding-tools/). The tool comes second: first figure out what work you’re handing it.
## Can you use OpenClaw and Hermes Agent together?
Key takeaway
Yes, and that’s what I do. They don’t conflict: they’re two separate processes with their own settings, keys, and access lists. The only thing to watch is that they use different Telegram bots and, if both run on one machine, different ports and folders. Otherwise, they work side by side without issue.
The “either-or” choice isn’t actually required. I have both installed at the same time, and it works fine: each covers its own lane. Technically, they don’t know about each other—they’re two independent agents, each with its own working folder, keys, and list of people it replies to. You can install them side by side on both a laptop and a server.
There are a couple of small things to watch. First, if both use Telegram, create separate bots for them with @BotFather; otherwise, the token will compete for the same bot. Second, on one machine, check that they don’t use the same port or write to the same folder—each has its own environment. Nothing else special is needed; they don’t interfere with each other.
Here’s how I use them: I keep one as my main messenger agent at all times, and try the other for specific channels and tasks. If you’re unsure which to start with, install one first based on the table above, get through one clean conversation, and only then add the second—in that order, nothing breaks.
## What else do people ask about choosing between OpenClaw and Hermes Agent?
Key takeaway
Quick answers to common questions: both tools are free, both work well on a server but don’t require one for personal use, and both install on Windows. The main difference is their focus: OpenClaw is about messenger coverage, while Hermes is about memory and self-writing skills.
### Frequently asked questions
Which is better, OpenClaw or Hermes Agent?
There’s no absolute “better,” only “better for what.” If you need one agent across many messengers and a large community, OpenClaw is more convenient. If memory across sessions, self-writing skills, and a desktop app matter more, Hermes Agent is more convenient. They’re roughly equal for Telegram and for working with any model. I use both and choose based on the specific task.
Are both free?
Yes, both tools are free: they’re open source. You pay only for the model you connect to the agent. Hermes has its own Nous Portal subscription starting at 20 dollars and lets you sign in through existing subscriptions such as ChatGPT or Claude Max. OpenClaw offers your own provider keys with payment based on usage, or a local model through Ollama with no monetary cost. Both can cost nothing when you use a local model.
Do you need a server for OpenClaw or Hermes Agent?
A laptop is enough for personal use with either. You need a server only if you want the agent to reply in a messenger and do scheduled tasks while your computer is turned off. This is equally true for OpenClaw and Hermes: both run perfectly well on an inexpensive VPS.
Can you install both at once?
Yes. They’re two independent processes with their own settings, keys, and access lists; they don’t know about each other and don’t conflict. Just make sure they use different Telegram bots and, on one machine, different ports and working folders. Otherwise, they work side by side without issue—I do this myself.
### Sources
- [OpenClaw, official website](https://openclaw.ai)
- [OpenClaw, documentation](https://docs.openclaw.ai)
- [OpenClaw GitHub repository](https://github.com/openclaw/openclaw)
- [OpenClaw, Telegram setup](https://docs.openclaw.ai/channels/telegram)
- [OpenClaw, gateway security](https://docs.openclaw.ai/gateway/security)
- [OpenClaw, local models](https://docs.openclaw.ai/gateway/local-models)
- [Hermes Agent, official website](https://hermes-agent.nousresearch.com/)
- [Hermes Agent, documentation](https://hermes-agent.nousresearch.com/docs/)
- [Hermes Agent, skills system](https://hermes-agent.nousresearch.com/docs/user-guide/features/skills)
- [Hermes Agent, persistent memory](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory)
- [Hermes Agent, Telegram setup](https://hermes-agent.nousresearch.com/docs/user-guide/messaging/telegram)
- [Hermes Agent GitHub repository](https://github.com/NousResearch/hermes-agent)
- [Nous Portal, pricing and models](https://portal.nousresearch.com/)
Continue working with AI in [Ailoot](https://ailoot.ai/).
## Questions about OpenClaw or Hermes Agent: a task-based comparison
### Does running the agent locally keep every conversation off cloud services?
No. Local execution can keep configuration and history on your machine, but a cloud model receives the prompts and context sent to it. Messenger and external-tool traffic also leaves the machine; a fully local workflow requires a local model and local tools without those external services.
### What must be separated if I run OpenClaw and Hermes together?
Use separate Telegram bots, working folders, configuration, and ports where applicable. Start with one successful conversation in one agent before adding the second, and avoid having both processes write to the same files.
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