OpenClaw vs Hermes Agent: Which Personal AI Agent for a Local Model

OpenClaw and Hermes Agent are the two most popular open-source personal AI agents of 2026. Both run on your own computer, answer you through chat apps, run commands and schedule tasks, and both can be started on a local model with a single ollama launch command. They differ in what they connect to, how they remember, and how much they let the agent do before asking.

This comparison is for running either one on a local model. BFWR has not run either agent; the details below come from each project’s documentation and source code, read on 24 October 2026, and the memory figures are computed with our VRAM calculator. Our full guides cover each in depth: OpenClaw with a local model and Hermes Agent with Ollama.

Quick answer

• Choose OpenClaw if you want the widest reach: more than 20 chat apps, paired phones and computers, and a large plugin ecosystem. Plan to tighten its defaults, which let the agent run commands without asking.
• Choose Hermes Agent if you want an assistant that builds a memory of you and its own skills over time, with stricter defaults for who can message it.
• Either way, give your local model a 64K context and at least 24GB of GPU memory for a model that calls tools reliably.

Side by side

Setup on a local model

Both projects have an Ollama launcher that installs the agent, lets you pick a model and writes the configuration. The difference is the address. OpenClaw talks to Ollama’s native API and warns that the OpenAI-compatible /v1 URL “breaks tool calling”. Hermes uses exactly that /v1 URL, as most OpenAI-compatible tools do. Copying one agent’s setup into the other is the most likely way to end up with a model that prints tool calls as text instead of running them.

Context: the setting that decides whether it works

Both agents send long prompts: a system prompt plus the description of every tool, before your message. Ollama’s documentation says tasks like “agents, and coding tools should be set to at least 64000 tokens”, but by default gives only 4,096 tokens on GPUs under 24GB.

• Hermes Agent enforces it. Its docs say it “requires at least 64,000 tokens of context for agent use with tools. Smaller windows are rejected at startup”, and you set the context on the Ollama server, for example with OLLAMA_CONTEXT_LENGTH=64000.
• OpenClaw sets the context itself with every request, at “a 32,768-token runtime context” by default. Ollama’s OpenClaw page recommends “at least 64k tokens if using local models”, so set OpenClaw’s own contextTokens and params.num_ctx to 65536. Changing Ollama’s server setting alone does not change what OpenClaw asks for.

Which local model, and how much memory

Each project names a different first choice. OpenClaw’s managed llama.cpp setup says “Qwen3.8 is the first recommendation when its memory budget fits.” Hermes’s Ollama guide says “gemma4:31b is currently the best local option with tool-call support.” Both also list smaller options: Qwen3.5 4B and 9B for OpenClaw, Qwen3.5-9B on Macs for Hermes.

At a 64K context, computed with our VRAM calculator‘s formula (4-bit weights, 16-bit cache, plus runtime overhead):

So OpenClaw’s recommendation fits a 24GB card at 64K and Hermes’s needs a 32GB card or a Mac with 36GB or more. Gemma 4 26B-A4B is the closest substitute for Hermes on 24GB. Both agents warn about small models: OpenClaw’s FAQ answers “Usually no” to whether a local model is fine for casual use, and Hermes’s code warns that Nous’s own Hermes 3 and 4 models “are NOT agentic”.

Memory and skills

This is the clearest difference in what the two agents are for.

Hermes Agent is built around learning. It keeps a notes file about its environment and a profile of you, stores every session in a searchable database, and writes and improves its own skills. The docs say “By default the agent writes skills freely”, including from a background review after each turn, and memory writes also happen without asking unless you turn on approval. Everything lives in ~/.hermes/.

OpenClaw is built around reach. Its skills are instruction files you install, and its plugins add tools, chat channels, model providers and media generation. Paired devices add camera, screen and location. It remembers sessions, but self-improvement is not its focus.

Security defaults

Both projects say plainly that their agent can do real damage, and both assume a single trusted owner. Their defaults differ:

• Running commands. OpenClaw’s trust model says “host exec on `gateway`/`node` is allowed without approval prompts” by default, and sandboxing is off. Hermes also runs commands on your computer by default, but checks them against a list of dangerous patterns first, and its default smart mode has an AI model, by default your main model, approve, deny or escalate the matches. With a local model, that means the local model judges its own commands.
• Who can message it. OpenClaw sends unknown senders a pairing code and does not process their message until you approve. Hermes denies everyone until you list them: “If no allowlists are configured and `GATEWAY_ALLOW_ALL_USERS` is not set, all users are denied.”
• Network exposure. Both listen only on your own machine by default and require a token or key for their control interfaces.

Hermes’s SECURITY.md puts the underlying point most clearly: “The only security boundary against an adversarial LLM is the operating system.” For either agent on a local model, which has no provider safety filter, the strongest step is to run its commands in a container or sandbox. Our guide to securing a local LLM explains why.

Security track records differ in scale. OpenClaw’s repository had published 722 security advisories by 24 October, 14 of them critical, the latest batch on 11 September. The GitHub Advisory Database lists 10 for Hermes Agent, one of them high. OpenClaw’s much larger codebase and plugin surface account for much of the gap, but both point the same way: keep the agent updated.

Which to choose

• OpenClaw suits you if you live in several chat apps, want the assistant on your phone and other devices, or want the biggest plugin ecosystem. Budget time to apply its hardened baseline.
• Hermes Agent suits you if you want one assistant that gets to know you, through Telegram or another of its five chat apps, and you are happy with its stricter context requirement.
• On a 24GB card, OpenClaw’s recommended Qwen3.8 27B fits at 64K; for Hermes, use Gemma 4 26B-A4B or Qwen3.8 27B rather than Gemma 4 31B.

FAQ

Are OpenClaw and Hermes Agent free?

Yes. Both are MIT-licensed and free. With a local model there are no API costs; some optional tools, such as web search, need an online service.

Can I run both on the same Ollama install?

Yes, as long as the context is set for each: on the Ollama server for Hermes, and in OpenClaw’s own model settings for OpenClaw. Loading two large models at once needs memory for both.

Which is safer?

Hermes has stricter defaults for who can message it and checks commands before running them; OpenClaw runs commands without asking unless you change it. Neither is a security boundary on its own, so run either in a container for real isolation.

Related guides and tools

• OpenClaw with a local model
• Hermes Agent with Ollama
• DeepSeek Harness with a local model
• Ollama models and VRAM

Sources

• openclaw/openclaw and the OpenClaw documentation
• NousResearch/hermes-agent and the Hermes Agent documentation
• Ollama: OpenClaw, Hermes and context length

Updated 24 October 2026. Versions, defaults and advisory counts were read on that date. Both projects release often, so check their docs for changes.