AI Agent Runtimes Explained: A Complete Guide to OpenClaw and Hermes Agent
An AI agent runtime is the software that turns a large language model into an autonomous assistant that can take actions, remember context, and run on its own without waiting to be prompted. The two most popular open-source AI agent runtimes right now are OpenClaw and Hermes Agent. This guide explains what they are, how they work, how they differ from tools like ChatGPT and Claude, what people use them for, and what you need to know about running one safely.
If you have heard the terms "AI agent," "OpenClaw," or "Hermes agent" and want a clear, complete explanation without the hype, start here.
What is an AI agent?
An AI agent is a large language model wrapped in software that lets it act, not just answer. A regular chatbot takes your question and returns text. An AI agent takes a goal and works toward it: it reads and writes files, runs commands, searches the web, sends messages, and calls other software, looping through those steps until the task is finished.
The clearest way to see the difference is state. A chatbot is stateless. It starts fresh every time and forgets the conversation once you close it. An AI agent is stateful. It maintains memory of what it tried, why something failed, and what it learned about you, and it carries that context from one session to the next.
Three shifts separate an AI agent from a chatbot:
Answering becomes acting. The agent executes real tasks instead of returning text.
Stateless becomes stateful. The agent remembers across sessions instead of starting over.
Prompted becomes proactive. The agent can run on a schedule and wake itself up, instead of waiting for you.
What is an AI agent runtime?
An AI agent runtime is the system that provides everything the language model needs to behave like an agent. A useful mental model is that an agent has a brain and a body.
The brain is the language model itself: Claude, GPT, Gemini, or an open model like Llama running on your own hardware. The brain does the reasoning, but on its own it can only produce words. It cannot touch anything.
The body is the runtime. It connects the brain to your files, calendar, messaging apps, terminal, and browser, and it turns a single question-and-answer exchange into a loop. That loop is the core of every agent runtime and works like this:
The model receives your request plus everything it knows about the situation.
It decides on an action and calls a tool, such as searching the web or reading a file.
The runtime executes that tool and feeds the result back to the model.
The model reviews the result and decides what to do next.
It repeats until the task is complete, then returns to you.
A chatbot does one pass. An AI agent runtime spins this loop as many times as the task requires, checking its own work along the way.
Around that core loop, a mature agent runtime adds four things: a tool system so the agent can act, a memory system so it retains context, a scheduler so it can run without you, and channels so you can reach it through the apps you already use. OpenClaw and Hermes Agent are the two best-known implementations of this idea.
What is OpenClaw?
OpenClaw is an open-source, self-hosted AI agent that runs on your own machine and communicates through messaging apps like WhatsApp, Telegram, Slack, Discord, and iMessage. It is the project that pushed AI agent runtimes into the mainstream.
OpenClaw started in November 2025 as a weekend project by Austrian developer Peter Steinberger, first named Clawdbot, then Moltbot, and finally OpenClaw. After well-known AI figures amplified it, the project went from zero to roughly a quarter-million GitHub stars in about two months, briefly making it the fastest-growing open-source repository in the platform's history. Steinberger was later hired by OpenAI, and OpenClaw moved into a foundation with OpenAI's support.
OpenClaw is model-agnostic, meaning you can connect it to Claude, GPT, Gemini, or a local open model and swap providers without rewriting your setup.
How does OpenClaw work?
OpenClaw is built around a long-running gateway. The gateway is a single always-on process that owns everything: which chat apps can reach the agent, who is allowed to talk to it, how messages get routed, and how tasks get executed. This is why OpenClaw is best described as a control plane, or communications infrastructure for a personal assistant. Its core bet is that the hard problem is routing and control, meaning who can reach your agent, from where, and with what permissions.
Two design choices define how OpenClaw works day to day.
Configuration through markdown files. OpenClaw sets an agent's personality and rules using plain markdown files in a workspace folder. Files named SOUL.md, AGENTS.md, TOOLS.md, and USER.md hold the agent's persona, operating instructions, tool conventions, and a profile of you. These are injected into the model's context on every turn. Memory works the same way, stored as readable, editable Markdown on disk, so you can open a file and see exactly what your agent believes about you.
Skills from ClawHub. OpenClaw is extended through skills, which are modular add-ons distributed through a community marketplace called ClawHub. ClawHub works much like an app store or a package registry. If you want your agent to handle a specific service or workflow, you install a skill. This is the source of both OpenClaw's flexibility and its biggest security challenge, covered below.
What is Hermes Agent?
Hermes Agent is an open-source AI agent runtime built by Nous Research, the lab known for the Hermes family of fine-tuned language models. It launched in late February 2026. While it is smaller than OpenClaw by adoption, it is the more architecturally ambitious of the two, and its tagline captures the whole idea: "the agent that grows with you."
The defining feature of Hermes Agent is that it improves the more you use it. Where OpenClaw builds a gateway and puts an agent inside it, Hermes builds a runtime with a learning loop at the center.
How does Hermes Agent work?
Hermes Agent is Python-native, and the agent's own execution loop is the thing everything else is organized around. You can run it from a command line, a built-in terminal interface, or the same messaging apps OpenClaw supports.
Its distinctive mechanism is a reflective phase that runs after each completed task. The agent analyzes how it solved the problem, extracts the reusable pattern, and writes itself a new skill file documenting the method. The next time a similar task appears, it pulls from its own skill library instead of reasoning from scratch. In effect, Hermes writes its own instruction manual as it works and builds a deepening model of your preferences and methods over time.
The practical result is that Hermes needs less manual configuration the more you use it, because it teaches itself your workflows. The tradeoff is that you are trusting it to learn the right lessons. OpenClaw takes the opposite approach, keeping you in control by making every instruction an explicit file you author yourself.
How are OpenClaw and Hermes different from ChatGPT and Claude?
This is the most common question, and the answer clears up most of the confusion around AI agents.
ChatGPT and Claude, as most people use them, are session-bound chatbots. They are stateless, reactive, and confined to producing text inside a single session. They are extraordinarily capable brains without a persistent body. You supply the continuity, you re-explain your context each time, and you copy the output somewhere useful yourself.
OpenClaw and Hermes Agent are bodies looking for a brain. Neither one contains an AI model of its own. They connect to Claude, GPT, Gemini, or a local model and wrap it in persistence, tools, scheduling, and reach. The intelligence is borrowed. What they add is memory that survives, the ability to take real actions, and a heartbeat that lets the agent operate without you present.
Two honest caveats keep this from being a clean binary. First, the line is blurring, because session-native tools are adding persistence and coding assistants increasingly save notes across sessions. Second, an AI agent is only as good as the model you connect. A persistent runtime around a weak model just makes poor decisions more autonomously. The runtime multiplies the model's quality in both directions.
OpenClaw vs Hermes Agent: key differences
Both are open-source, MIT-licensed, connect to the same major chat platforms, and use the same SKILL.md format for extensions. They diverge on what the product is organized around.
Dimension | OpenClaw | Hermes Agent |
|---|---|---|
Core design | Gateway and control plane | Self-improving runtime |
Language | TypeScript | Python |
Best at | Orchestration across many agents and channels | Recurring tasks that should improve over time |
Memory | File-backed Markdown you edit directly | Learns and stores procedures automatically |
Skills | Mostly human-authored, installed from ClawHub | Written by the agent itself through reflection |
Setup | Fast, running in about 30 minutes | Slower, more assembly, 2 to 4 hours |
Failure mode | Noise: too much context, agents overlapping | Narrowness: weak on problems it has not seen |
The short version: choose OpenClaw when the problem is orchestration, routing, and running multiple agents across channels. Choose Hermes Agent when the problem is automation that should compound and get better with repetition.
There is also a forward-looking wrinkle. The two can already talk to each other. Because the ecosystem is converging on a shared skill format and a federation protocol, an OpenClaw agent and a Hermes agent can exchange messages and delegate tasks across the boundary without either knowing what the other is running. Increasingly the answer to "which one should I pick" is to run both and let each do what it does best.
What are AI agent runtimes used for? Common use cases
The people getting real value from OpenClaw and Hermes Agent tend to run jobs, not one-off questions. Common use cases include:
Research monitoring. An agent watches a set of sources overnight and delivers a briefing in the morning.
Personal operations. An agent triages messages, drafts replies, and manages a calendar across your chat apps.
Coding on a remote machine. An agent lives on a server, works inside your actual project files, and remembers your codebase's conventions between sessions.
Back-office automation. An agent reconciles data, watches a dashboard, or files recurring reports, and improves as it learns the pattern.
The common thread is a task you want handled continuously and improved over time. If your need is "answer this well," a chatbot is the right tool. If your need is "keep doing this for me," you are in agent-runtime territory.
Are AI agent runtimes safe? Understanding OpenClaw security
Security is the most important thing to understand before running any AI agent runtime, and it is the part most overviews skip.
The strength of an AI agent, its willingness to take real action with real credentials on your real machine, is exactly what makes it hard to secure. You are handing autonomous software access to your files, your terminal, and your logged-in services.
OpenClaw's rapid growth made this concrete. In early 2026, researchers disclosed a one-click remote-code-execution flaw (tracked as CVE-2026-25253, rated high severity) that could compromise an OpenClaw instance if the user simply visited a malicious webpage, even on setups bound only to the local machine. It was patched quickly, but scanning firms found tens of thousands of exposed instances, many misconfigured to accept connections from the open internet and leaking API keys and chat histories.
The larger structural problem was the skills marketplace. Because anyone could publish a skill, and installed skills run with the same broad access as the agent itself, ClawHub became one of the largest AI-agent supply-chain attack surfaces yet seen. Security teams found hundreds of malicious skills, at one point roughly a fifth of the registry, most disguised as legitimate tools such as crypto utilities that quietly installed credential-stealing malware. Attackers kept adapting even after automated scanning was added.
The takeaway is not to avoid these tools, but to treat an AI agent runtime as untrusted code execution with persistent credentials, which is close to Microsoft's published guidance. In practice that means:
Run it isolated, in a container or a dedicated machine, never on the laptop holding your sensitive data.
Give it scoped, disposable credentials rather than your primary logins.
Review skills before installing them, the same way you would a stranger's script.
Keep it off the open internet, behind a firewall.
Newer runtimes like Hermes leaned into safety from the start, with sandboxing, filesystem snapshots that roll back before destructive actions, and command scanning before execution. But no design removes the core bargain: capability and attack surface are the same thing viewed from two directions.
Much of this setup and hardening work can be avoided by running a managed version instead of a raw self-hosted install. Clawapp is a managed OpenClaw desktop app for Windows and macOS that gives you OpenClaw's capabilities without the manual configuration, so you can start using an AI agent without wiring up the gateway, credentials, and isolation yourself.
Which AI agent runtime should you choose?
Pick based on the problem, not the hype:
Choose OpenClaw if you want a mature, always-on assistant that lives in your chat apps, routes across multiple channels, and coordinates multiple agents.
Choose Hermes Agent if you want a leaner runtime for repeated tasks and workflows that should improve over time, especially on a remote machine.
Consider running both, since they can federate, using OpenClaw for orchestration and Hermes for learning-heavy automation.
Whichever you choose, isolate it, scope its credentials, and vet every skill before installing.
Get started with OpenClaw the easy way
If you want OpenClaw's power without the setup and hardening, try Clawapp, a managed OpenClaw desktop app for Windows and macOS. It handles the configuration for you, so you can go from download to a working AI agent in minutes instead of assembling and securing a self-hosted stack yourself. Download it at clawapp.saharaai.com.
Frequently asked questions
Is OpenClaw free? Yes. OpenClaw is open-source and MIT-licensed. You self-host it, so your costs are infrastructure and whatever you pay the model provider you connect.
What models work with OpenClaw and Hermes? Both are model-agnostic. They connect to Claude, GPT, Gemini, and local open models running through tools like Ollama, so you can swap the underlying brain without rebuilding your agent.
Is Hermes Agent better than OpenClaw? Neither is universally better. OpenClaw is stronger for orchestration and multi-channel control. Hermes is stronger for recurring automation that improves over time. They solve different problems.
Can OpenClaw and Hermes work together? Yes. They share a skill format and a federation protocol, so agents built on each can exchange messages and delegate tasks to one another.
Are AI agents the same as ChatGPT or Claude? No. ChatGPT and Claude are the language models, the brains. AI agent runtimes like OpenClaw and Hermes are the body that gives a model memory, tools, scheduling, and the ability to act on your behalf.
Is it safe to run an AI agent runtime? Only with precautions. Treat it as untrusted code execution with real credentials: isolate it, scope its access, vet its skills, and keep it off the open internet. If you would rather not manage that yourself, a managed OpenClaw desktop app like Clawapp handles the setup for you on Windows and macOS.
