# Hermes Agent vs OpenClaw: 2026 Comparison By Amir Teymoori --- If you're picking a self-hosted AI agent in 2026, two names dominate the conversation. [OpenClaw](https://amirteymoori.com/openclaw-clawdbot-moltbot-ai-llm-agent/) is the GitHub phenomenon that briefly became the most-starred repo on the platform. [Hermes Agent](https://github.com/nousresearch/hermes-agent) is the newer entrant from Nous Research that grew so fast it has its own migration tool just for OpenClaw users. Both are open-source, both run on your server, and both let a chatbot in Telegram or Slack actually do things on your machine. They diverge sharply in how they handle memory, sandboxing, and security, and that's where the choice gets real. This guide compares them on the things that change a buying decision, not the spec sheets. ## The Quick Verdict | Use case | Pick | |---|---| | Need it running today, biggest integration list, broad community | **OpenClaw** | | Want an agent that learns from each task and gets faster the longer it runs | **Hermes Agent** | | Switching after the OpenClaw RCE in February | **Hermes Agent** (built-in `hermes claw migrate`) | | Running fully offline with [local models](https://amirteymoori.com/ai-agents-and-mcp-servers-explained-the-future-of-intelligent-automation/) on Ollama | Either one | ## Background: How We Got Here OpenClaw is the side project that became a category. Austrian developer Peter Steinberger released it as Clawdbot in late 2025, renamed it to Moltbot after a trademark scuffle with Anthropic, then settled on OpenClaw in early 2026. By March it had crossed 247,000 stars and overtaken React for the top spot on GitHub. Steinberger then joined OpenAI in February to lead personal-agent research, and the project moved to a community foundation. Then came the security wave. In early February, runZero published [CVE-2026-25253](https://nvd.nist.gov/vuln/detail/CVE-2026-25253), an unauthenticated remote code execution flaw with a CVSS score of 8.8. Tens of thousands of unpatched OpenClaw instances were compromised before the patch caught up. The trust shock left an opening, and Nous Research walked through it. Hermes Agent shipped on February 25 under MIT license. By April it had 64,000 stars and a steady stream of OpenClaw refugees. The pitch was simple: an agent that learns from experience and improves the more you use it. ## Two Different Bets OpenClaw is a **doer**. Send a Telegram message, and it turns it into shell commands, file edits, browser actions, calendar entries, or smart-home calls. It has hands and very few opinions about how to use them. Hermes Agent is a **learner**. It does the same things, but every time it solves a non-trivial task, it writes a [skill document](https://hermes-agent.nousresearch.com/docs/user-guide/features/skills): a reusable Markdown file that captures the approach, the dead ends, and the edge cases. Next time something similar comes up, the agent loads that skill instead of reasoning from scratch. Nous Research calls this the closed learning loop. Solve, document, retrieve, improve, repeat. Their internal benchmarks show research tasks completing about 40% faster after a few weeks of skill-building, with no prompt tuning involved. ## Architecture OpenClaw runs as a single process with broad permissions on the host. Tools, integrations, and platform adapters all live in the same address space. That's what made the February CVE so painful: a single flaw in the HTTP listener gave attackers access to everything. Hermes Agent splits things up. The platform adapters (Telegram, Discord, Slack, WhatsApp, Signal, Matrix, CLI, email) all flow into a single gateway process, which talks to the agent runtime through a controlled interface. Tool execution happens inside one of five sandbox backends: local, [Docker](https://amirteymoori.com/devops-for-ai-from-docker-containers-to-production-deployments/), SSH, Singularity, or Modal. Container hardening and namespace isolation are the default. If you need to fan out work, Hermes ships isolated subagents with their own conversations, terminals, and Python RPC, so a long pipeline doesn't pollute the main context. It also includes Atropos, a small reinforcement-learning framework for [fine-tuning](https://amirteymoori.com/fine-tuning-llms-with-lora-a-practical-guide-for-2025/) your tool-calling models on your own [agent](https://amirteymoori.com/ai-agents-and-mcp-servers-explained-the-future-of-intelligent-automation/) data. That's a research-grade feature, but you can ignore it until you need it. ## Models Both agents are model-agnostic. The difference is the default ecosystem. OpenClaw was originally built around Anthropic's Claude family and grew outward to support OpenAI, Gemini, and a long tail of providers via OpenRouter. Local inference works through Ollama, llama.cpp, or vLLM. Hermes Agent leans into the open ecosystem. It ships with native support for the [Nous Portal](https://hermes-agent.nousresearch.com/) (Hermes 4 70B and 405B, plus the free MiMo v2 Pro), an integrated MiniMax M2.7 partnership, and the same OpenRouter and local routes. That gives it more out-of-the-box options without configuration. For a personal user with [Ollama](https://amirteymoori.com/devops-for-ai-from-docker-containers-to-production-deployments/) and a recent open-weight model, both run at zero API cost. For a cloud setup, your monthly spend is essentially the LLM bill, not the framework. Pick the agent first, the model second. ## Security OpenClaw is a known target. After the February CVE, the project pushed a patch quickly, but the design itself still concentrates risk. Default configurations assume a trusted single-user environment, which doesn't match how most people deploy it. Hermes Agent has no published CVEs as of May 2026 and built three protections in from the start. Sandboxing is on by default. The platform gateway can't reach the agent runtime directly. And every community-submitted skill goes through a security scanner that checks for data exfiltration, [prompt injection](https://amirteymoori.com/hack-llm-chatbot-extract-system-prompt-identify-ai-model/), destructive commands, and supply-chain risks. That doesn't make Hermes invulnerable. New agent frameworks usually find their first CVE within a year, and you should expect this one will too. The architecture is built to contain the blast radius rather than prevent every flaw, which is the right bet long-term. If you're staying with OpenClaw, run it behind a firewall, keep auto-updates on, and treat it like any other internet-facing service. ## Skills and Memory OpenClaw remembers conversations. Hermes Agent remembers how it solved things. After a few weeks of real use, a Hermes deployment builds up a library of skill documents that cover your common workflows, plus a quietly accumulating user model that captures your communication style and your tool preferences. The agent retrieves the relevant skills automatically before answering, so a workflow you ran twice last week becomes a one-step request next month. OpenClaw can do the same task each time, but you have to provide the prompt and any context yourself. For solo developers and researchers, the compounding effect is what flips the decision toward Hermes. For teams running shared agents, the user model gets messier, but Hermes lets you spin up isolated profiles per user. ## Integrations and Platform Reach OpenClaw has the bigger out-of-the-box integration list, with deep support for Gmail, GitHub, Spotify, Obsidian, Twitter, Philips Hue, and dozens of other services. That's the head start showing up. Hermes Agent ships fewer prebuilt integrations, but it supports the [Model Context Protocol (MCP)](https://amirteymoori.com/ai-agents-and-mcp-servers-explained-the-future-of-intelligent-automation/) natively. Any [MCP server](https://amirteymoori.com/ai-agents-and-mcp-servers-explained-the-future-of-intelligent-automation/), and the ecosystem is growing fast, plugs in with a few lines of config. Recent releases added Matrix support, Discord channel controls, Signal media handling, and Mattermost file attachments, narrowing the gap. If your stack is mainstream, both work. If you need niche IoT or a specific consumer app, OpenClaw still has the edge. ## When to Choose Which Pick **OpenClaw** if you want the most integrations on day one, you run iMessage automations (still OpenClaw-only), you're happy patching weekly and locking down your network, or you already have a working setup that doesn't need to evolve. Go with **Hermes Agent** if you care about an agent that gets better the more you use it, you're running sensitive workflows and want sandboxing by default, you want to fine-tune your own [LLM](https://amirteymoori.com/ai-llm-glossary-120-terms/) on agent data using Atropos, or you're moving off OpenClaw after the CVE. ## Frequently Asked Questions ### Is Hermes Agent really faster than OpenClaw? For one-off tasks, throughput is similar. The 40% speedup Nous reports applies to repeated task families, where Hermes loads a relevant skill instead of reasoning from scratch. If your workload is mostly ad-hoc, you won't see it. If you have recurring workflows, you will. ### Can I run both at the same time? Yes, and many people do during migration. Run them on different ports, route different platforms to each (say, Telegram to Hermes, WhatsApp to OpenClaw), and compare outputs over a couple of weeks before fully switching. ### What happened to Peter Steinberger and OpenClaw? Steinberger announced in February 2026 that he was joining OpenAI to lead personal-agent research. OpenClaw moved to a community foundation but kept its release cadence. The founder transition combined with the CVE is a big part of why Hermes Agent's growth accelerated. ### Are there other competitors worth looking at? For research-grade agents, look at OpenAgents, AutoGen, and LangGraph. None of them target the personal-assistant niche the way OpenClaw and Hermes do, but they're worth knowing if you plan to build, not just use. ### Which is better for production deployments? Hermes Agent's sandboxing, skill scanner, and isolated subagents make it the safer default. OpenClaw can be hardened with care, but the default configuration assumes a trusted single-user environment. Treat that as a starting point, not a finished posture.