Hermes vs OpenClaw: learning or orchestrating, two visions of open-source AI agents

Since February 2026, two open-source projects have been vying for the attention of the AI agent community: Hermes, the self-learning runtime from Nous Research, and OpenClaw, the multi-channel orchestration framework that has been widely adopted since 2025.

Both are under the MIT license, both speak MCP and ACP, and both boast impressive GitHub stats.

Putting them head-to-head to declare a winner misses the point.

Hermes and OpenClaw answer different questions.

One aims to build an agent that learns on its own over time, while the other wants an agent that orchestrates quickly across many platforms.

This is why you might want to use both, depending on your goals.

Hermes and OpenClaw in 1 minute

OpenClaw boasts 345,000+ GitHub stars as of April 2026 and a library of 5,700+ community skills published on ClawHub.

The project has been around since 2025, is MIT licensed, and connects to 50+ channels (Telegram, Slack, Discord, WhatsApp, voice, Live Canvas).

It’s the agent you can plug in everywhere in under an hour.

Hermes plays in a different league: 64,000+ GitHub stars, launched in February 2026, MIT licensed, published by Nous Research (the same lab behind the Hermes-Llama models).

No skill library to install, no 50 plug-and-play channels.

Instead: a unified runtime that generates its own skills over time, stores them in markdown, and reuses them next time.

345,000 GitHub stars don’t equal 345,000 active users: it’s a visibility metric, not usage. The real questions lie elsewhere.

Peter Steinberger, creator of OpenClaw, was recruited by OpenAI in early 2026: we discussed this in the Anthem article on Steinberger and AI agents.

This hire accelerated the project’s maturity but also left the ClawHub community somewhat orphaned regarding governance issues.

Two philosophies: learning vs orchestrating

The fundamental difference between Hermes and OpenClaw can be summed up in one sentence.

OpenClaw is a reactive gateway: it receives a message, routes it to the right skill, and responds.

Hermes is a closed learning loop: it receives a task, executes it, analyzes its own trajectory, and writes a procedural skill for next time.

The operational consequence is simple.

An OpenClaw launched today and one launched in six months will behave the same, as long as no new skills are added to ClawHub.

A Hermes launched today and one launched in six months, on the same workflows, will be two different agents.

Hermes doesn’t aim to outdo OpenClaw in plug-and-play: it aims to improve over time, in your own context.

This is an explicit design choice by Nous Research.

The lab’s doctrine has been known since its papers on Hermes-Llama: prioritize cognitive autonomy over breadth of surface.

OpenClaw takes the opposite stance and embraces a gateway-first philosophy: one entry point, 50+ output channels, a skill library fueled by the community.

Both approaches are legitimate.

They just don’t target the same user profile or project type.

Architecture and technical environment

Both projects share a common technical base that deserves clarification.

MIT license for both: self-hosting without legal friction, forking allowed, commercial use OK.

Both support MCP (Model Context Protocol) to connect the agent to tools and databases.

Both support ACP (Agent Communication Protocol) for inter-agent communication.

This protocol compatibility is why the question “which to choose” is often poorly posed: both can coexist in the same stack.

On the model side, Hermes supports 300+ models for inference: Claude, GPT, Gemini, Llama, Mistral, Qwen, and the in-house Hermes-Llama models as a priority.

OpenClaw delegates the model question to MCP wrappers, making compatibility broader but less precise.

The gap widens in the community: OpenClaw benefits from a year’s head start and a ClawHub network effect that is its real moat.

Hermes catches up through curated lists like awesome-hermes-agent and a smaller but more expert community.

In terms of industry events, the return of NVIDIA GTC 2026 where Feynman and OpenClaw were in the spotlight also weighed in on OpenClaw’s institutional adoption by companies.

Hermes hasn’t had its GTC moment yet.

What you can actually do with each

Concrete use cases clearly separate the two agents.

With OpenClaw, value arrives quickly in three types of projects.

A multi-channel assistant that responds on Slack, Telegram, and WhatsApp without duplicating logic.

A customer support workflow that connects the finance, logistics, or CRM library from ClawHub in a few clicks.

A quick POC to demonstrate the feasibility of an agent over a wide scope before industrializing.

OpenClaw shines when the need is to “cover a wide area quickly.” Hermes shines when the need is to “dig deep into a single recurring flow.”

With Hermes, the promise is expressed in longer missions.

An agent that handles a long-horizon task from 30 minutes to several hours without losing track.

A work companion that remembers the conventions of a specific project and reapplies them without repetition.

A runtime that recovers from errors in the middle of a complex chain, rather than asking the user to restart everything.

The difference is most noticeable on the second and third use.

OpenClaw does the same as yesterday.

Hermes has built on yesterday and does a bit better today.

Performance: tokens, latency, recovery

Performance figures circulate widely on Reddit and X, with varying levels of accuracy.

The community-reported orders of magnitude in April 2026 are as follows.

OpenClaw operates around 1.8k tokens per turn thanks to its pre-packaged and prompt-optimized skills.

Hermes consumes more on initial turns, up to 8k tokens, as it generates its skills on the fly.

After a few sessions, the gap narrows significantly once Hermes skills are cached in markdown.

Hermes pays a high token cost on initial turns and amortizes it over time. OpenClaw pays little per turn but doesn’t improve.

On latency, OpenClaw is around 1.2 seconds per standard turn in a self-hosted setup.

Hermes is slower on learning turns but competes once skills are stabilized.

On error recovery in long-horizon, the Nous Research community published internal figures showing a 22% better recovery rate than agents without a learning loop, including OpenClaw.

These figures come from the lab itself: take them with the usual caution regarding self-published benchmarks.

They remain consistent with the design: a runtime that reviews its own trajectory has more information to correct a drift than a gateway that routes without memory.

Limits and pitfalls

Neither project is without blind spots, and this is where promotional comparisons are the quietest.

For OpenClaw, the dominant issue in 2026 is ClawHub security.

The ClawHavoc campaign of January 2026 identified 335 to 341 malicious skills out of the 2,857 audited, about 12% of the catalog compromised.

The affected skills exfiltrated SSH keys, API tokens, or installed keyloggers like Atomic Stealer.

On broader audits, 1,184 confirmed malicious skills and up to 2,400 suspects were removed from the hub.

ClawHub is safer now than in the first quarter of 2026. An open ClawHub without filters remains a major supply chain attack surface. Installing a skill is executing third-party code with the agent’s privileges.

The CERT-FR issued an advisory in February 2026 against deploying OpenClaw on workstations without a strict allowlist, ClawNet, or Docker sandbox.

Another OpenClaw limitation is manual resets: no learning loop, no self-correction over time.

If a skill ages poorly or a channel changes its API, manual intervention is required.

For Hermes, the limitations are of a different kind.

The project is young, launched two months ago, and the integration breadth is low compared to OpenClaw.

No native GUI, no Live Canvas, no ready-to-use Telegram connector: you need to code your own interfaces.

The target is clearly power users, developers comfortable with CLI, capable of scripting their own integrations.

The learning autonomy also has a downside: you have to trust the skills the agent writes for itself.

In sensitive contexts (finance, health, legal), this opacity is a real barrier that Nous Research has yet to resolve properly.

Which one to choose or both?

The right question isn’t “which agent wins”.

It’s “what profile are you and what are you trying to achieve”.

Three scenarios cover most situations.

You need to quickly cover multiple channels with a single agent: OpenClaw is the obvious choice, provided you harden ClawHub with an allowlist and only trust official or audited skills.

You have a recurring workflow you want to see progress on its own over time: Hermes is more aligned, even if the user learning curve is steeper and integrations need to be built.

You’re building a serious agent stack: both coexist seamlessly via MCP and ACP.

OpenClaw in front for channels and multi-surface orchestration.

Hermes in back for long-horizon tasks that benefit from learning.

The real 2026 insight isn’t “Hermes vs OpenClaw”. It’s “how to combine a broad orchestrator and a deep runtime in the same infrastructure”.

If your need is simpler and you’re primarily looking for a general-purpose prompt-driven assistant, the question shifts elsewhere: managed agents like Codex or Claude Code, with the latest developments in GPT-5.5 and recent field feedback that also push the line between “self-hosted open-source agent” and “provider-managed agent”.

In this case, Hermes and OpenClaw simply aren’t the right category of answer.

Open-source agents have their place, but it’s specific: sensitive environments where self-hosting is non-negotiable, teams wanting control over the system prompt and skills, projects where the lack of provider dependency is worth more than managed convenience.

For everything else, the match is between a ChatGPT Business, a Claude Enterprise, or a Codex, and the relevant comparisons are elsewhere.

FAQ

Are Hermes and OpenClaw compatible with each other?

Yes, both support MCP and ACP, allowing them to coexist in the same stack: OpenClaw as a multi-channel orchestrator, Hermes as a long-horizon runtime, communication via ACP.

Which consumes fewer tokens?

OpenClaw is more economical per turn (about 1.8k tokens) thanks to its pre-packaged skills, Hermes consumes more initially (up to 8k) but amortizes this expense over time once its skills are auto-generated.

Is ClawHub safe to use in April 2026?

After the ClawHavoc campaign of January 2026, moderation has been strengthened but the supply chain risk remains high: installing a third-party skill is executing external code, verify the source, use ClawNet, ClawDex, or an allowlist, and limit to official or audited skills in sensitive environments.

Can Hermes replace OpenClaw in a multi-channel deployment?

Not in plug-and-play, Hermes lacks native connectors for Telegram, Slack, or WhatsApp: you’ll need to code these integrations or route them via an upstream OpenClaw, which often means using both together rather than replacing one with the other.

How many GitHub stars for Hermes and OpenClaw in April 2026?

OpenClaw has 345,000+ stars, Hermes 64,000+ stars since its launch in February 2026: remember that a GitHub star measures project visibility, not the number of active users or code quality.

What is the real advantage of Hermes’ learning loop?

The agent reviews its own trajectory after each task, generates persistent procedural skills in markdown, and reuses them next time: in recurring workflows, token consumption decreases and error recovery improves (about 22% according to Nous Research), at the cost of higher initial complexity.

Does OpenClaw require ClawHub to be useful?

No, the core OpenClaw works without ClawHub and already includes the 50+ channel connectors: ClawHub adds business depth (finance, CRM, logistics) via community skills, but you can build your own skills locally without ever touching the public hub, eliminating the supply chain risk.

Can both be self-hosted on a single server?

Yes, a Linux VM or WSL2 with Docker is enough to run OpenClaw and Hermes side by side: plan for at least 16 GB RAM if you’re running a local model, less if you’re calling hosted models via API.

What license protects Hermes and OpenClaw?

Both projects are under the MIT license: commercial use allowed, forking allowed, no obligation to publish your modifications, unlike an AGPL which would require sharing your contributions.

Which is recommended for a beginner in AI agents?

OpenClaw is more accessible for installation and initial results thanks to its Live Canvas GUI and ready-to-use skills, Hermes requires more CLI proficiency and security rigor, but offers a more formative learning curve for those wanting to understand how an agent truly learns.