Hermes Agent just launched. Here's how it compares to OpenClaw for your business.

Nous Research dropped Hermes Agent in late February 2026. A month later, it already has 16,000+ GitHub stars, weekly releases, and a migration tool that imports your OpenClaw setup in one command.

That migration tool tells you everything. This isn’t a side project. It’s a direct play for the same users.

I’ve been running both for the past few weeks — on a VPS, on a Mac Mini, connected to Telegram. Here’s what actually matters if you’re a business owner trying to pick one.

What Hermes Agent is (and isn’t)

Hermes Agent is an open-source AI agent from Nous Research, the team behind the popular Hermes family of open-source AI models. It installs with a single command, runs on your own hardware, and connects to Telegram, Discord, Slack, WhatsApp, Signal, and email through one gateway process.

So far, sounds a lot like OpenClaw. The differences are under the hood.

It remembers things. Not in the “chat history” way every tool does. Hermes has a multi-level memory system: it stores facts about you across sessions, searches its own past conversations with full-text search, and builds what it calls a “dialectic user model” — basically a progressively deeper understanding of who you are and how you work. Close the terminal, reboot the server, come back two weeks later. It knows you prefer invoices exported as CSV, that your accountant’s name is Marta, and that you get annoyed when it asks for confirmation on routine tasks.

It creates its own skills. When Hermes solves a complex task — say, setting up a multi-step invoice pipeline — it writes a reusable “skill document” describing what it did and how. Next time you ask for something similar, it finds that skill and improves on it. These skill files follow the agentskills.io open standard, which means they work with Claude Code, Cursor, GitHub Copilot, and about 30 other tools. You can export them, share them, import skills other people wrote.

It’s model-agnostic. Connect it to Nous Portal (their own inference service), OpenRouter (200+ models), OpenAI, Anthropic, or run a local model through Ollama or vLLM. Switch providers mid-conversation with /model. One ironic detail: the default recommended model is Claude Opus via OpenRouter, not their own Hermes model. That says something about where open-source models stand for agent tasks.

Where Hermes beats OpenClaw

Memory that persists

OpenClaw forgets. Every session starts mostly fresh unless you manually configure persistence. I’ve set up OpenClaw for half a dozen businesses, and the single most common complaint is “I already told it this last week.”

Hermes doesn’t have that problem. The memory system isn’t perfect — one honest review on DEV.to called it “structured note-taking” rather than true intelligence — but it’s there, it works, and it makes a real difference for daily use. The trucking company owner who spent weeks training OpenClaw on their workflow? With Hermes, that knowledge accumulates automatically.

This matters most for businesses that repeat the same types of tasks: accounting firms processing the same invoice formats, dental practices handling the same appointment patterns, property managers fielding the same tenant questions week after week.

Cleaner security out of the box

I wrote about OpenClaw’s security challenges at length. The 386 malicious skills on ClawHub. The CVE that exposed 135,000 instances. The “lethal trifecta” warning from Palo Alto Networks.

Hermes has no marketplace full of community-submitted plugins to worry about. Docker execution runs with security hardening by default — read-only root filesystem, dropped capabilities, PID limits. It’s sandboxed from the start. No major public security incidents yet, though it’s also only a month old, so take that with appropriate caution.

Easier setup

OpenClaw setup is, as one salon owner I worked with put it, “a nightmare.” Hermes installs with one curl command:

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

Then hermes model to pick your AI provider, hermes gateway to connect your messaging platforms. A setup wizard walks you through it. It’s not five-minute easy like Claude Code Channels, but it’s dramatically less painful than OpenClaw’s manual wiring.

Built-in automation

Hermes has a cron scheduler. Tell it to send you a morning briefing, run nightly database backups, do a weekly competitor audit — the kind of routine admin work that eats hours every week. With OpenClaw, you’d wire this together yourself with system cron jobs, shell scripts, and prayer. With Hermes, it’s hermes cron and a natural language description.

Where OpenClaw beats Hermes

Way more messaging integrations

OpenClaw connects to WhatsApp, iMessage, Slack, Signal, Telegram, Discord, and Microsoft Teams. That’s 7+ platforms, with iMessage being a big deal for businesses with Apple-heavy teams.

Hermes supports Telegram, Discord, Slack, WhatsApp, Signal, and email. Close, but no iMessage. And the WhatsApp integration is newer and less battle-tested.

Massive community

OpenClaw has 247,000 GitHub stars and thousands of active users. When something breaks, someone on Reddit has already fixed it. When you need a specific integration — connecting to Xero, pulling data from Shopify, reading PDFs from a specific scanner — there’s probably a skill for it already.

Hermes has 16,000 stars and a growing but small community. Documentation has gaps. If you hit an edge case, you might be the first person to encounter it.

More mature for business operations

OpenClaw has been battle-tested in real businesses for months. The trucker’s load-email-to-invoice pipeline. The salon’s supplier categorization system. The Luxembourg company’s multi-language scheduling assistant. These workflows were built, broken, debugged, and rebuilt by actual business owners.

Hermes is a month old. The architecture is clean, the ideas are ambitious, but it hasn’t been through the grinder yet. The self-improving skills system needs volume to work — the DEV.to review noted it provides no advantage over stateless agents for one-off tasks. You need 50+ similar tasks before the pattern recognition kicks in meaningfully.

The Nous Research question

Before you commit to Hermes, you should understand who’s behind it.

Nous Research is a legitimate AI research lab. Their Hermes models have been downloaded 33+ million times. Meta and DeepSeek have cited their papers. They raised $65 million, with a $50M Series A led by Paradigm in April 2025.

They also have a crypto angle. Part of their business model involves a decentralized training system on the Solana blockchain and a NOUS token. Their billion-dollar valuation is token-based, not traditional equity. Co-founder Karan Malhotra has acknowledged the skepticism, saying there’s “a lot of room for grift” in crypto-AI but positioning Nous as a serious lab.

I bring this up because it affects long-term bets. If you’re building your business operations on an open-source tool, you want confidence that the team behind it will keep maintaining it for the right reasons. The MIT license means the code can’t be taken away — anyone can fork it. But active development depends on the company staying focused on the agent, not pivoting entirely to token infrastructure.

For now, the development pace is remarkable: four major releases in March 2026 alone. That’s faster than OpenClaw’s current cadence.

The philosophy difference nobody’s talking about

Nous Research builds their models to be “user-aligned” rather than “safety-aligned.” Their Hermes 4 model scores 57% on RefusalBench — meaning it responds to 57% of controversial or sensitive prompts — compared to 17% for GPT-4o and Claude Sonnet. Their pitch: your AI should do what you ask, period.

This philosophy flows into Hermes Agent. It’s designed for maximum user control, minimum guardrails.

Whether that’s a feature or a risk depends on your use case. For a personal productivity agent, flexibility is great. For a business agent that handles client communication, you might actually want guardrails. An agent that sends an inappropriate response to a client because it doesn’t have content filters is a liability, not a feature. Legal firms and healthcare practices in particular should think hard about this before choosing an uncensored-by-default platform.

OpenClaw is model-agnostic too — you can run uncensored models on it. But it doesn’t bake this philosophy into its DNA. With Hermes, “no restrictions” is a selling point. Make sure that aligns with how you plan to use it.

Migration is trivially easy

If you’re already on OpenClaw and curious, trying Hermes costs you nothing. Run:

hermes claw migrate

It auto-detects your ~/.openclaw directory and imports your persona files, memories, skills, messaging settings, workspace instructions, and API keys. I tested this on two different OpenClaw setups. Both migrated cleanly. You can run both side by side and compare.

So which one should you pick?

Pick Hermes Agent if:

• You’re starting fresh (no existing OpenClaw setup to protect)
• Memory and self-improvement matter to you — you run similar tasks daily and want the agent to get better at them over time
• You value clean security defaults over community ecosystem breadth
• You’re comfortable being an early adopter of a fast-moving project

Pick OpenClaw if:

• You need iMessage integration
• You rely on community skills and a large support community
• Your workflows are already built and working — switching costs real time
• You want the battle-tested option that’s been through every edge case

Pick both if:

• You’re exploring. Migration between them is painless. Run Hermes for new projects, keep OpenClaw for established workflows

Pick neither if:

• You just want to message an AI from your phone and don’t need deep automation. Claude Code Channels on Telegram is simpler, safer, and works in five minutes.

FAQ

Is Hermes Agent free? Yes. MIT license, same as OpenClaw. You pay for whatever AI model you connect it to — or nothing if you run a local model. I broke down real AI costs for small businesses in a separate post, and the numbers apply here too.

Can I use Claude with Hermes Agent? Yes. It supports Anthropic’s API directly and through OpenRouter. In fact, Claude Opus via OpenRouter is the default recommended model.

Does the self-improvement actually work? It works well for repeated task patterns. If you process invoices daily, it’ll get noticeably better at it over weeks. For one-off tasks, it’s no different from any other agent. The memory system is the more immediately useful feature.

Should I migrate from OpenClaw to Hermes right now? Not unless you’re hitting specific pain points — forgetting context, security concerns, setup frustration. Hermes is promising but young. If your OpenClaw setup works, let Hermes mature for another few months before making the switch.

What about Claude Code as a standalone agent? Claude Code is my other recommended tool, especially for businesses that want Anthropic-managed infrastructure. It doesn’t have Hermes’ persistent memory or multi-platform gateway, but it’s backed by a major company with a dedicated security team. I wrote a full comparison here. If you’re curious about running AI locally vs. in the cloud, the self-hosted vs. cloud cost comparison covers the hardware math.

Where I come in

I set up AI agents for European small businesses — accounting firms, restaurants, auto repair shops, logistics companies, and more. Hermes, OpenClaw, Claude Code — the tool depends on your business. I’ve migrated clients between all three and I know where each one shines and where it falls over.

Tell me what you’re trying to automate and I’ll tell you the fastest path to get there.