What Is Hermes? The Self-Improving AI Agent Coming for OpenClaw

The AI agent space has a new contender, and it’s learning faster than anyone expected. Hermes Agent, built by Nous Research, launched as an open-source autonomous agent that doesn’t just execute tasks but improves itself every time it runs.

Since its release, Hermes has accumulated over 5K GitHub stars and sparked a debate that’s splitting the AI tinkering community: Is it actually better than OpenClaw?

The short answer is: it’s complicated. The longer answer involves understanding what each tool was designed to do, and why using both might be the real answer.

What Hermes Agent actually is

Hermes Agent is an open-source autonomous AI agent built by Nous Research that is designed to learn over time instead of starting from scratch every session. Nous built the Hermes-3 model family trained on Llama 3.1 with their Atropos RL stack.

The agent ships with 47 built-in tools, a persistent memory system that grows across sessions, a pluggable memory backend architecture, MCP server integration, voice mode support across all platforms, and—this is the headline feature—a skills system that lets the agent create, update, and delete its own procedures.

So when Hermes figures out a non-trivial workflow, it saves the approach as a reusable skill document. The more you use it, the more capable it gets, and this is the core differentiator.

The latest release added background task auto-notifications, free MiMo v2 Pro on Nous Portal, and live model switching across all platforms. It supports Claude, ChatGPT, Qwen, and local open-source models. That means it’s possible for people running it entirely on Qwen 3.5 locally, which the open-model community has been doing with reportedly strong results.

Hermes Agent vs. OpenClaw: What are the differences?

The comparison with OpenClaw is inevitable but often misframed. OpenClaw and Hermes Agent solve overlapping problems with fundamentally different philosophies.

Architecture and philosophy

OpenClaw is ecosystem-first—it was built to integrate with everything, and its massive development resources (backed by OpenAI’s acquihire of its creator Peter Steinberger and significant Nvidia development support) allow it to ship daily updates across native plugin support for Cursor, Claude Code, and other IDEs (that’s integrated development environments, for those unfamiliar with the jargon).

Hermes is learning-first—its entire architecture revolves around the agent improving itself through use. The self-improvement loop isn’t a feature bolted on; it’s the reason the project exists.

What it boils down to is: OpenClaw is using AI to be your jack of all trades. Hermes aims at being the absolute best at something after a few iterations.

Performance

In our tests, Hermes moves faster than OpenClaw, even on the same model. The agent’s lightweight architecture, a simple terminal screen, means lower latency on tool calls, faster response times, and less overhead.

OpenClaw has had documented performance issues before, though the team has been addressing them. Hermes has been consistently snappy.

That said, OpenClaw is a much bigger project, used by many more people, who document many more situations.

Subagents and multi-agent workflows

OpenClaw has invested heavily in subagent architecture—spinning up specialized agents for specific tasks. Hermes supports the Agent Communication Protocol, or ACP, allowing it to communicate directly with OpenClaw or other agents, but orchestration is not its focus.

The practical upshot: You can run both side by side, with OpenClaw orchestrating and Hermes executing, or have them working on different parts of a project simultaneously. Claude on the front end, ChatGPT on the back end, both working in parallel.

OpenClaw is a good orchestrator, handing out tasks to different agents, whereas Hermes is a great solo executor.

If you’re an advanced user, you could use both in tandem. If you just want something practical, your decision will depend on what you want to do with your AI assistant.

Capabilities

This is where the philosophies diverge sharply. OpenClaw begins as a middle layer, giving you a “dumb” agent which you must shape to your image and likeness and make it as powerful as you want based on all the skills you install.

This often means either you or the model go to a repository with preconfigured “skills” created by other users and shared with the community.

Hermes comes with learning tools built in, so it feels more usable from scratch. It also supports the skills format, so you can ask the model to learn a specific skill.

However, unlike OpenClaw, this light-weight program has the ability to create and modify its own skills as you interact with it. For example, we asked the model to gain access to our account. It worked with us through the whole process and once it succeeded, it proceeded to create its own skill instead of relying on a third-party, previously written skill.

For novice users, the model creating its own skill means they are not exposed to potentially unsafe third-party skills that may inject prompts or do other weird stuff. For more advanced users, it means their agent can potentially end up with a workflow, knowledge, and behavior that is more tailored to their personality and requirements.

Messaging and deployment

Both agents support Telegram, Slack, Discord, WhatsApp, and command line interfaces among other messaging platforms. OpenClaw has more mature native integrations with development tools. Hermes has more flexible deployment options—it runs on anything from a Raspberry Pi to a cloud cluster, and the serverless path costs nearly nothing when idle.

Pricing

Both are fully open-source and free to run. Your only cost is the API calls to whatever model you use and the infrastructure to host it, or whatever you spend on electricity should you choose to run a local model instead.

Documentation and community.

OpenClaw’s docs benefit from a larger team and longer development history.

Hermes’s documentation is structured and functional—covering installation, quickstart, configuration, tools, memory, skills, MCP integration, and voice mode—but it reads like it was written by engineers for engineers. There’s less hand-holding.

That said, both have improved over time and bug fixing is not a pain anymore, especially considering how engaged their communities are.

OpenClaw has the larger community and more extensive documentation, driven by its bigger team and longer runway. Also, given how huge it is culturally, a lot of companies are building on top of it, making things much easier to run.

Right now, Xiaomi has MiMo Claw, Minimax has MaxClaw, Ziphu AI has AutoClaw, etc. Even Nvidia offers an OpenClaw solution for enterprise purposes. You won’t see that with Hermes, at least not yet.

These implementations give users the chance to run a cloud instance, deploying agents with the push of a button. Interacting with a cloud agent that runs on a separate machine could be a bit more limited than giving an agent access to your local PC, but still a very powerful option for everyday users.

Hermes is growing fast—its Discord is active, the docs are solid and improving, and the community skews toward technical tinkerers who contribute back. But it’s still just a nice terminal in the end.

OpenClaw feels like a polished product. Hermes feels like a workshop.

Security

Both agents can operate with meaningful system access, so deployment choices matter. Hermes runs with the privileges of the account that launches it, and its own security docs explicitly say not to run the gateway as root. OpenClaw has a more developed sandbox model on paper, but its own repo also shows ongoing hardening work around skill permissions, writable skill directories, and sandbox defaults.

In practice, neither should be treated as “safe by default” in production without careful isolation, least-privilege configuration, and review of any third-party integrations or skills.

You must be extremely careful when dealing with agents. Skills may be infected, models may hallucinate, and things can go sideways. If you don’t set up the proper safeguards, things can go very, very wrong.

For example, in one of our “morning briefing” routines, the model hallucinated names, orders, and started working on writing emails to AI influencers and setting up bets on Polymarkets. The routine was simply news research and a Telegram message with its picks.

This may (and probably will eventually) happen with either Hermes, OpenClaw, or any agentic AI tool you use.

How to install and run Hermes

Installing Hermes is very simple. Still it’s not the same as installing an app the way you’d normally think.

Instead of running an installer, you must open your terminal and copy and paste this command:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

That single command handles everything—Python, Node.js, ripgrep, ffmpeg, the repo clone, virtual environment, and the global hermes command. By the end, you’re ready to chat. If you’re on macOS or Linux (including WSL2 on Windows), this is all you need. After it finishes, reload your shell and run the command:

source ~/.bashrc

From there, setting up your agent is simple.  Type the command:

hermes setup

Once you do, you will be able to configure everything in one go.

If you want to try different models, choosing your AI provider is just as simple. Just write this command in the terminal:

hermes model

This command will walk you through the process of selecting the best model. Nous Portal offers zero-config OAuth login. OpenRouter gives you access to dozens of models with a single API key. If you want to run entirely on open models, you can plug in Qwen, DeepSeek, or any OpenAI-compatible endpoint—including running a local model on your own hardware. Hermes requires at minimum 64K tokens of context, which every major model in 2026 supports.

The whole setup takes under five minutes. No Docker, no YAML files, no dependency hell. The installer was clearly designed by people who have fought through enough broken setup scripts to know what not to do.

To start interacting with Hermes, you just need to type one simple and done! Everything will happen in the terminal. So, simply type this:

hermes chat

How to connect Hermes to Telegram

This is where things get interesting. Once you connect Hermes to Telegram, you essentially have a personal AI assistant in your pocket, accessible from any device, anywhere, with voice memo support and group chat capabilities.

The setup takes about two minutes. Open Telegram and search for @BotFather—that’s Telegram’s official bot for creating bots.

Send this command to Botfather so it guides you through the process of creating your own bot:

 /newbot

Once the bot replies, the set up process is straightforward. Give your a name (something like “MyHermesbot” or whatever you want your assistant to be called), and BotFather will give you a bot token. Copy that token. Remeber that all telegram bots need to have a name that ends in “bot.”

When you are done and have created your Telegram bot, open your terminal for Hermes and run the command:

hermes setup

That will walk you through the configuration. Paste your bot token when prompted, and the gateway takes care of the rest. The gateway is a single process that connects all your messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) simultaneously. You don’t need to set up each one separately if you only want Telegram.

Once the gateway is running, open your new bot in Telegram and send it a message. That’s it. You’re now talking to Hermes from your phone. It can send you scheduled task results, receive voice memos that get auto-transcribed, handle file uploads, and even work in group chats where you can @mention it.

The voice memo feature alone is worth the setup. You can send a voice message saying “summarize today’s top Hacker News stories and send me the top three”—and it will process the audio, execute the task, and respond with text. No typing required.

It even lets you run “hermes claw migrate” in case you want to ditch OpenClaw and move everything to Hermes. Other commands with more specific features are available on the project’s Github page and documentation.

Is Hermes the right thing for you?

If you’re a developer or tech geek who wants an agent that improves over time, runs on cheap infrastructure, and supports open models, Hermes is the clear pick. It will also be the better option if you use an agent to do the exact same thing over and over again.

That said, if you need the most polished experience with the broadest IDE integrations and the largest community, OpenClaw remains the safer choice.

If you’re serious about AI productivity, the real answer is running both. The multi-agent workflow isn’t a compromise. It’s a competitive advantage. Having one agent orchestrating while another executes, using different models for different tasks, and having a backup when one goes down, produces results that neither agent achieves alone. Hermes’s self-improvement loop means it gets better every day you use it. OpenClaw’s ecosystem means you’re never short on integrations.

For specific use cases: content creators and journalists will find Hermes’s skill system invaluable—it turns repetitive research and writing workflows into automated pipelines that improve daily.

Developers benefit from the multi-agent setup, splitting workloads between different models on tasks each handles best for cheaper.

Data scientists and ML engineers get built-in tools for fine-tuning and reinforcement learning that OpenClaw simply doesn’t offer.

And anyone running on a budget could potentially use Hermes with LMStudio on their potato PC to run an agent that costs nothing to operate.