Hermes versus OpenClaw!

Key Takeaways

Hermes Agent versus Openclaw, which AI super agent should you use? You're running multiple AI tools, switching between apps, copying and pasting all day, and still nothing is actually automated. You keep hearing about AI agents, agents that work while you sleep, agents that remember everything, agents that actually do things, but you try one, it breaks, or it forgets everything the next day, or it only works inside one specific app. Sound familiar? You're not alone. Right now, there are two open-source AI agents blowing up the space. One is called Openclaw, the other is Hermes Agent. Both are free. Both run on your own machine. Both are getting thousands of downloads. But, they are not the same tool, and choosing the wrong one could waste you a lot of time. By the end of this video, you'll know exactly what each one does, what makes them different, and which one is right for you. Stay with me. Hey, I'm Julian. I'm an AI educator, and I help people actually learn and use AI tools in their work, not just talk about them, actually use them. Today, we're going deep on Hermes Agent versus Openclaw. I've gone through both the documentation and the official sources, so everything you're hearing is accurate and up-to-date as of 2026. Quick heads-up, by the end of this video, I'm also going to share where you can get all the notes, SOPs, and use cases from today, plus access to a huge community using these tools right now. So, keep watching. Let's start with Openclaw because a lot of people have already heard of it. Openclaw was built by Peter Steinberger, an indie developer from the Apple developer world. He started this as a personal project, and it exploded. The tagline is simple, the AI that actually does things. It lives on your computer, Mac, Windows, or Linux. You talk to it through whatever chat app you already use, WhatsApp, Telegram, Discord, Slack, Signal, or iMessage. You send a message from your phone, and it works on your machine. It has persistent memory, it remembers you across sessions, and if you need it to do something it can't do yet, it writes a new skill to handle it. There's a community hub called ClawHub, where people share skills, think mini programs or workflows your agent can run. It supports over 50 integrations, Gmail, Calendar, GitHub, Obsidian, Spotify, browser automation, and more. Installation is one command. It sets up everything, including Node.js. There's also a beta companion app for macOS if you prefer a menu bar experience. Open-source, MIT license, free to use. You bring your own API key, Claude, GPT, or local models. The project was formerly known as ClaudeBot, and before that, Molbot, so if you've seen those names, same thing, just rebranded. Now, let's talk about the newer challenger, Hermes Agent. Hermes is built by Nous Research. This is not a solo developer project. Nous Research is an AI lab. They're the team behind the Hermes, Nous, and Psyche model families. These are people who train AI models for a living. That matters, and we'll get to why in a second. The GitHub repo already has over 8,100 stars and over 1,000 forks with 142 contributors as of March 2026. This thing scaled fast. The tagline is an agent that grows with you. Also open-source, MIT license, Python-based. Installs via a single curl command on Linux, macOS, or WSL 2 on Windows. Same messaging platforms as Openclaw, plus email. It has over 40 built-in tools, web search, browser automation, file system access, code execution, image generation, text-to-speech, memory, scheduling, and a lot more. It works with Open Router, which gives you access to over 200 models. It also works with News Portal, OpenAI, or any OpenAI compatible endpoint. Massive model flexibility, but here's the thing that sets Hermes apart, and this is the part you actually want to pay attention to. Hermes has a built-in learning loop, not just memory, an actual closed learning loop. This is the most important part of this video. Both tools remember things, but Hermes goes further. It doesn't just store what you tell it, it creates new skills from experience automatically. After it completes a complex task, it generates a skill for it. The next time something similar comes up, it already has a procedure. It doesn't start from scratch, and those skills self-improve while it uses them. The agent nudges itself to refine what it learns. It builds a deepening model of who you are across sessions. Quick pause here. When I first started using tools like these, I was genuinely overwhelmed. So many agents, so many settings, so many ways to set it up wrong. That's when I found this community called AI Profit Boardroom. With over 2,000 members all focused on learning AI together and sharing what actually works. It taught me which workflows save time versus which ones waste it. The community shares real use cases and practical implementations. If you're serious about using AI to improve your work and skills, check it out. Link in the description. Nous Research built this with RL training pipelines in mind. Hermes has batch trajectory tree generation and integration with Atripos for reinforcement learning research. That's way deeper than a typical personal assistant. If you care about how the agent actually gets smarter over time, this is the one designed for that. Another big difference is where these agents actually live. Openclaw is local-first. Your data stays on your hardware, simple. Hermes is server-first. It's designed to run on a VPS, a GPU cluster, or serverless infrastructure. You can run it for almost nothing when idle using Modal or Daytona. Picture this, you run on Hermes on a $5 VPS. You message it from your phone on Telegram while it works on a cloud machine you never SSH into yourself. It's not tied to your laptop at all. That's a fundamentally different setup. Hermes supports six terminal backends total, local, Docker, SSH, Singularity, Modal, and Daytona. Full sandboxed execution if you need it. Okay, back to the comparison because we still need to cover use cases and who should use which tool. With Openclaw, people are clearing inboxes, managing calendars, checking in for flights, automating tasks in Todoist, connecting to Obsidian notes, running Claude code or Codex sessions from their phone, and building custom skills for things like Whoop health data and RSS feeds. With Hermes, you do a lot of those same things, but it also handles heavier workloads, running scheduled reports, delegating to multiple sub-agents in parallel, nightly backups or weekly audits in natural language with no code. Hermes lets you spawn isolated sub-agents for parallel work streams, multiple tasks running at the same time not interfering with each other. That's useful for more complex pipelines. And if you're coming from Openclaw, Hermes has a migration tool built in. Run Hermes claw migrate, and it imports your memories, skills, API keys, messaging settings, and persona file. Easy to move over. One more thing worth knowing, the skill ecosystem is shared. Both tools work with ClawHub and the Agent Skills IO open standard. So, community skills built for one generally work for both. So, which one is actually right for you? Let me break it down simply. Choose Openclaw if you want something that's quick to set up, runs on your local machine, has a polished experience, and has a huge community already building workflows and skills for it. If you want something you can be using within an hour, and you're not super technical, Openclaw is probably the easiest starting point. Choose Hermes Agent if you want an agent that genuinely improves over time, you're comfortable with a server setup or running something on a VPS, you want maximum model flexibility, or you're technical and interested in the research angle. If you want something you can build seriously on top of, Hermes is a deeper platform. And if you're already using Openclaw and want to try Hermes, they literally built a migration tool for you, so nothing is stopping you from trying both. A few tips before we wrap up. The biggest mistake people make with both tools is rushing the onboarding. The memory and context system is only as good as what you put in. Tell it about yourself, your projects, your preferences up front. The more context you give it early, the better it performs. For Hermes, use the Hermes setup wizard. It handles everything at once and detects if you've used Openclaw before. For Openclaw, run the onboard command after install. It walks you through your persona and first messaging platform. For both, start with one messaging platform. Telegram is the most popular. Get comfortable there before adding more channels. All right, that's the breakdown. Openclaw if you want something local, fast to set up, and approachable. Hermes if you want something that runs on a server, improves itself, and is built for deeper work. Both are free. Both are open-source. Both are worth knowing about right now. If you're looking to dive deeper into AI tools and actually implement them in your work, I recommend AI Profit Boardroom. Over 2,000 people learning how to use AI effectively. Everyone shares real experiences, what's working, what's not. Which tools are worth your time, which ones to skip. No hype, just solid information and practical guidance from people doing the work. It's helped me stay on top of updates and figure out how to actually apply them. Link in description if you want to check it out. If you want the full process, SOPs, and 100-plus AI use cases like this one, join the AI Success Lab. Links in the comments and description. Get all the video notes from there, plus access to our community of 58,000 members who are crushing it with AI. If you found this useful, drop a like and subscribe. I'll put out videos like this regularly breaking down the tools that are actually worth your time. See you in the next one.