OpenClaw + Hermes is INSANE

“Hermes is a newer one, and Hermes is faster. Hermes self improves. Hermes is a great brain for what you have going on with the open claw. Open claw is an autonomous agent, and a lot of people talk about it. And open claw is great for being the execution. Okay. You want it to be your arms. You want Hermes to be your brain.”

“Open claw is an autonomous agent, and a lot of people talk about it. And open claw is great for being the execution. Okay. You want it to be your arms. You want Hermes to be your brain. And the cool thing is you could have each of them hold each other in check. So sometimes my open call will forget things, but Hermes will kick it back into gear and say, hey, yo. Like, you actually forgot this. No. You actually can't do that right now.”

“And both of them together are a lot stronger than them separately. Right? A lot of people, like, say, oh, it's Hermes versus OpenClaw. It's OpenClaw versus Hermes. No. You wanna have both because there's a chain of accountability that wasn't there previously. And now if you have this on your agent fleet, things are gonna run a lot more smoothly.”

“Now I do wanna emphasize this again. Hermes learns from every session. So imagine this. After 20 to 30 tasks in any one domain, it's going to be measurably better than it was before. OpenClaw doesn't do that. And I've tried building a lot of that into my OpenClaw. I have a lot of skills and things like that to recursively improve, to do all these things. It doesn't do that reliably.”

“When Hermes and OpenClaw work together, I have actually had them doing an end to end SEO campaign. So Hermes might say, okay, here is an SEO AEO analysis on the single grain website. Let's take a look at that. And then what will happen is OpenClaw will do the work. OpenClaw is working on the best model. Okay. The best large language model in terms of the the version. So in this case, it's Opus, and it's using Opus, and it's coming up with all the ideas. It's doing all the analysis.”

“But when you have a central intelligence, and in this case, we use Obsidian for that. So Obsidian, we have all of our daily logs, our shared decisions, like, all these things happening within our agents, our ecosystem. These agents can pull directly from that, And that allows them to move a lot faster. That allows them to give you the right information. That's something that we didn't have before.”

“I would say that the open models are are pretty limited. So, you know, even us when when we try to optimize with our, like, our DGX Sparks, for example, and we try to put them on open models, you know, they're they're getting better, but they're not quite the best. Right? And so just know that when it comes to really strategic work, you're probably gonna be wanting to run on the Frontier models, which are going to cost you money.”

“The other thing too, is if you don't have good skill dot MD files, meaning that you don't have good processes within your company and you don't pass them over to, like, your Obsidian to have that central memory continue to get better over time in terms of how you make decisions, well, then this is not gonna be that helpful because garbage in garbage out.”