OpenClaw vs Hermes Agent: Two Philosophies of AI Agents
OpenClaw has 300k+ GitHub stars and backing from half the tech companies in China. Hermes Agent has 6,000 stars and shipped its first release a month ago. On paper, this isn’t a comparison. In practice, they’re solving different problems so completely that the stars don’t matter.
What they have in common
Both are persistent AI agents that live on a VPS. You SSH into a $5 Hetzner box, install one of them, and now you have an AI that’s always on. It talks to you over Telegram, WhatsApp, Slack, Discord. It runs shell commands. It browses the web. It remembers your conversations. It does things while you sleep.
That’s where the similarities end.
OpenClaw: the operating system
OpenClaw thinks of itself as an OS for AI. A persistent gateway process that sits between an LLM and your local system, routing requests across channels, managing plugins, coordinating between multiple users and workspaces.
The architecture is TypeScript, event-driven, plugin-based. You pick your LLM backend — Claude, GPT, Gemini, local models, whatever. The agent itself is model-agnostic. The power comes from the ecosystem: ClawHub has over 10,000 community-built skills covering everything from calendar management to database queries to email automation.
It’s built for breadth. You want one agent that handles your team’s Slack, routes customer messages from WhatsApp, manages your calendar, and files your expenses? OpenClaw is designed for that. Multi-channel, multi-user, shared workspaces, team control surfaces.
Peter Steinberger (now at OpenAI) built it, open-sourced it under MIT, and it became GitHub’s fastest-growing repo — 180k stars in weeks. Then he joined OpenAI and moved the project to an open-source foundation. The community took it from there.
It’s mature. It’s battle-tested. It also got compromised.
The ClawHavoc incident
In early 2026, security researchers found that up to 20% of the skills in ClawHub — OpenClaw’s community marketplace — were malicious. They were delivering the AMOS stealer, a credential-harvesting trojan. Over 1,100 malicious skills. A separate vulnerability (CVE-2026-25253) allowed remote code execution through a WebSocket flaw, and scans found 135,000 exposed instances.
This is the tradeoff of a massive open ecosystem. Ten thousand skills means ten thousand attack surfaces. OpenClaw’s model-agnostic, plugin-everything approach makes it powerful and also makes it a target. The foundation responded with stricter skill verification, but the trust damage was done.
If you run OpenClaw, you audit your skills manually or you accept the risk.
Hermes: the agent that remembers
Hermes Agent comes from Nous Research — the team behind the Hermes model family. It shipped in February 2026. Python-based, MIT license, designed around one idea: the agent should get smarter over time.
The core difference is memory. Not conversation memory — every agent has that. Hermes has a multi-level system:
Session memory is standard context. Persistent memory is facts, preferences, and project details retained across sessions with full-text search and LLM summarization. Skill memory is the interesting one — when Hermes solves a hard problem, it automatically writes a reusable document describing the approach, indexes it, and refers back to it in future sessions. Over time, the agent accumulates a library of techniques specific to your workflows.
There’s also a dialectic user model (built on Honcho) that builds a deepening understanding of who you are — not just what you said, but how you work, what you prefer, what you tend to ask for.
The scheduling system supports both cron jobs and a heartbeat pattern. The heartbeat is a lightweight check every 15-30 minutes: “is there anything that needs attention?” Costs almost nothing in tokens. Your agent wakes up, checks on things, goes back to sleep. A cron job on top of a cron job.
Hermes ships with 40+ built-in tools — web search, terminal, file system, browser automation, vision, image generation, text-to-speech, code execution, subagent delegation. It has native MCP support, so it can connect to any MCP server at startup and register external tools automatically.
The execution model is flexible too — 6 sandbox backends: local, Docker, SSH, Daytona, Singularity (for HPC), and Modal (serverless). You can have your agent run commands locally on the VPS or inside isolated containers.
The philosophical split
OpenClaw is a platform. It’s designed for teams, businesses, organizations that want to deploy AI agents across their operations. The value scales with the number of channels, users, and integrations. It’s horizontal — do everything, connect everything.
Hermes is a companion. It’s designed for one person who wants a persistent AI that knows them deeply and gets better at their specific problems. The value compounds over time through accumulated memory and skills. It’s vertical — go deep with one user.
This isn’t a quality difference. It’s a category difference.
If you’re a solo developer who wants an agent that remembers your projects, knows your codebase, wakes up to check your deployments, and learns your debugging patterns over months — Hermes is built for that. OpenClaw could do it, but you’d be running a multi-tenant platform for one person.
If you’re a company that wants an AI handling customer support across WhatsApp, internal ops through Slack, and data pipelines through CLI — OpenClaw is built for that. Hermes could technically do it, but the personal memory model doesn’t map to a team context.
The security question
After ClawHavoc, security is part of the comparison whether you want it to be or not.
OpenClaw has a massive skill marketplace that was proven exploitable. The foundation is fixing it, but the architecture — a plugin system that downloads and runs community code — is inherently risky at scale.
Hermes generates skills locally from its own experience. There’s no marketplace to poison. The attack surface is your VPS, your LLM API key, and whatever tools you give it access to. That’s a smaller surface. The tradeoff is you don’t get 10,000 community skills — you get the ones your agent builds for itself.
NanoClaw, worth mentioning here, took a third approach — ~500 lines of TypeScript, runs everything inside Docker containers, built on Anthropic’s Agents SDK. It just partnered with Docker for production-grade sandboxing. If security is the priority above all else, that’s the minimal option.
The practical stuff
| OpenClaw | Hermes Agent | |
|---|---|---|
| Language | TypeScript | Python |
| Stars | 300k+ | 6k |
| Age | ~2 years | 1 month |
| LLM backend | Any (model-agnostic) | Hermes-3 default, swappable |
| Memory | Conversation persistence | Multi-level + skill generation + user modeling |
| Scheduling | Daemon + jobs | Cron + heartbeat |
| Sandbox | 2 backends | 6 backends |
| Channels | Telegram, WhatsApp, Slack, Discord, Gmail+ | Telegram, WhatsApp, Slack, Discord, CLI |
| Skill source | Community marketplace (10k+) | Self-generated from experience |
| Self-improvement | No | Yes (skill docs + evolutionary optimization) |
What I’d pick
For my use case — running autonomous agents that investigate data sources, register for services, and operate independently — I care about the agent being smart about my specific workflows, not about having 10,000 plugins. I want it to remember what worked last time and try that first next time.
That points to Hermes. But I also don’t want my agent downloading community skills from a marketplace where up to 20% of skills were flagged as malicious during the ClawHavoc incident. That also points to Hermes.
If I were running a business with a team that needed an AI assistant across five communication channels, I’d pick OpenClaw and audit every skill I installed.
Different tools. Different shelves. Pick based on what you’re building, not the star count.