Treating Hermes as a replacement for OpenClaw might be biased from the start.

They were never intended to become one thing

The OpenClaw official documentation places Gateway in a very prominent position. Its definition is quite direct: a long-running Gateway holds all message planes, and the control plane clients, Web UI, and Node devices all connect to this Gateway via WebSocket. Furthermore, only one Gateway on a host manages the core message connections; sessions, like those used by WhatsApp, are explicitly monopolized by it.

What does this mean? It means that OpenClaw’s worldview is not “first have an agent, then connect several entry points to it,” but rather “first have a persistent communication and control plane, and then organize the agent runtime, session, skills, delegate, and sub-agent within this plane.”

Hermes is not like this. Its architecture diagram clearly shows these entry points: CLI, Gateway, ACP, Batch Runner, API Server, and Python Library, all converging into the same AIAgent core. Session storage uses SQLite + FTS5, and the tool backend, provider resolution, and prompt builder are all organized around this core.

So, in terms of architecture, OpenClaw is more like a “resident personal assistant system”; Hermes is more like an “agent runtime platform,” and the messaging entry point is just one extension surface of it.

One that treats the Gateway as the protagonist, and one that treats the Agent as the protagonist

This architectural difference will ultimately translate into a difference in user experience.

OpenClaw’s strength is in making the concept of “how to have a long-term, identifiable, bounded, and routable assistant across many channels” very robust. It has default DM pairing, security policies, routing by channel/account/peer, multi-agent binding, delegate mode, and even organizational-level issues like “who sends on behalf of, which account receives, and where it appears in the group” are specifically documented.

In other words, what OpenClaw wants to solve first is the existence problem. This agent must first act like a real person, or like a real organizational assistant, living on platforms such as Telegram, Discord, Slack, WhatsApp, Signal, and WebChat. Only then can you talk about whether it will write code or perform task distribution.

Hermes’ focus is more on “capability accumulation.” While it certainly has Gateway and can connect to platforms like Telegram, Discord, Slack, and WhatsApp, the most prominent terms in its official narrative are not routing or accounts, but persistent memory, skills, MCP, sub-agents, toolsets, as well as batch processing, trajectory export, and RL training.

This is very crucial. The core problem Hermes needs to solve is not “how to connect an AI to as many messaging platforms as possible,” but rather “how to let this agent gradually develop its own skills, memory, and reusable capabilities, and even be able to conveniently use them for training data and experiments.”

So I would prefer to summarize it like this:

OpenClaw is a communication-first personal assistant system.

Hermes is an agent-core-first self-hosted agent platform.

Memory, Skills, and Ways of Extension, It’s Not One Flavor

Many people will say, don’t both support skill, memory, and plugin? That’s right, they both do. But the focus is different.

In OpenClaw’s agent runtime, the workspace is a very heavy concept. Files like AGENTS.md, SOUL.md, TOOLS.md, BOOTSTRAP.md, IDENTITY.md, and USER.md are directly injected into the context in the first round of a new session. Skill loading also has an entire set of priorities: those in the workspace, those in the project, those in the personal directory, those in ~/.openclaw/skills, and bundled ones can all stack up. Essentially, it is building a unified long-term assistant environment of “personality + rules + workspace + channel.”

Hermes’s skills system is clearly more geared towards “procedural memory.” The official documentation states that skills are on-demand knowledge documents, following progressive disclosure, and are compatible with the open standard of agentskills.io, all unified under ~/.hermes/skills/. Furthermore, the agent itself can modify and delete skills. This feels less like “giving the assistant a few instruction manuals” and more like “crystallizing past actions into reusable capability blocks.”

Memory is the same. Hermes has built-in, very restrained MEMORY.md and USER.md, plus a session search using SQLite + FTS5, and it also supports 8 types of external memory providers. This design is quite interesting; instead of writing “long-term personality environment” in great detail right away, it separates short-term memory, retrieval-based recall, and external long-term memory.

OpenClaw is not without memory, nor is it without advanced capabilities like sub-agents, multi-agent systems, or delegation. On the contrary, it has plenty of these. However, based on its documentation structure and default mental model, it feels more like managing a continuously online assistant workspace; Hermes feels more like managing a continually evolving agent capability graph.

Why is the ecosystem drifting further and further apart?

In the ecosystem, on the surface, everything looks like “open source + extensible,” but in reality, they are quite clearly branched out.

OpenClaw’s ecosystem is broader and leans more towards the external access layer. Official plugins can expand channels, model providers, tools, skills, speech, web fetch, web search, memory, while community plugins and ClawHub are also growing around “connecting more platforms, connecting more accounts, connecting more workflows.” It even explicitly supports bundle formats like .codex-plugin, .claude-plugin, and .cursor-plugin. This move is very smart; essentially, it means absorbing the shells of other ecosystems first, thereby enlarging its entry points.

Looking at the OpenClaw documentation, you’ll find that it writes very extensively about sub-agents, delegates, organizational agents, thread binding, DM pairing, and group mentions. This indicates its ecosystem goal is not just for single-machine tinkering, but rather to become a truly long-running agent operating system.

Hermes’s ecosystem is more like “externalizing core capabilities.” It has a Python plugin system, a memory provider plugin, a context engine plugin, and MCP server integration, as well as open skill standards like Skills Hub and agentskills.io. Coupled with training-related interfaces such as batch runner, trajectory export, and Atropos, Hermes’s ecosystem naturally attracts two types of people.

One type is those who use it as a personal agent.

Another category is those who use it as a research, training, experimental, or workflow substrate.

The needs of these two types of users are very different, but the Hermes architecture can cover both. So you might feel that while Hermes’ ecosystem may not spread as widely in terms of “number of channels” and “message presence” as OpenClaw, I am more inclined to feel that its potential in the agent substrate area will be greater.

There’s also a pretty interesting detail. The official Hermes README doesn’t just have hermes claw migrate; it also features a community project, HermesClaw, which is used to run both the Hermes Agent and OpenClaw on the same WeChat account simultaneously. This signal is quite clear: Hermes isn’t pretending that it has no connection with OpenClaw; it is acknowledging the existence of this user migration path and then productizing this path as well.

My Final Judgment

If what you are looking for is a “personal assistant system that truly lives on the messaging front,” one that requires accounts, channels, routing, delegation, organizational boundaries, long-term online presence, and strong control planes, then OpenClaw’s path is more complete and feels more like a mature communication foundation.

If you are looking for an “agent core that becomes more like your own workbench the more you use it,” and what you value is accumulated skills, memory composition, MCP, plugins, sub-agents, training data, and subsequent moldability, then Hermes is more convenient.

So, stop simply asking “Can Hermes replace OpenClaw?”. That question is a bit rough.

A more accurate way to ask is:

Do you want an AI assistant that lives within a messaging network, or an AI platform that lives within local workflows and agent runtimes?

If you think this through, the choice won’t be so agonizing.

References

Writing Notes

Original Prompt

Prompts: The differences between Hermes and OpenClaw, architectural differences, differences in design philosophy, and ecosystem differences.

Writing Idea Summary

Remove the common question “Alternative Comparison” and directly set the main thread to “What is the core focus of the two projects?”.
For the architecture section, prioritize looking at the main entry points and core components in the official documentation, rather than secondary community summaries.
In the design philosophy section, avoid vague value judgments and focus directly on specific designs such as Gateway, Agent Core, memory, skill, and delegate.
For the ecosystem section, focus on comparing extension directions, not star ratings, popularity, or community hype wars.
Fact verification must be based on official repositories and documentation visible as of 2026-04-15.