Atomic Agent, Hermes, and OpenClaw are three open-source, MIT-licensed AI agents that share a common core loop but are built around different goals. OpenClaw is an assistant that lives in every messenger you use. Hermes is a self-improving agent that learns from its own runs. Atomic Agent is local-first — it runs a quantized model on your own machine by default. That last point is the real dividing line.
If you have used any of the three, you have probably wondered which one you actually want. They share a surprising amount of DNA, and each is genuinely good at something the others are not. This is an honest comparison from someone who builds one of them: we read all three codebases, and we will tell you where each wins.
What do these three agents have in common?
More than you would expect. All three are open source under the MIT license, and all three run the same fundamental loop: send context to a model, let it call tools, feed the results back, repeat until the task is done. Under the hood, the overlap is real:
- Browser automation through Playwright.
- SKILL.md playbooks — reusable, human-written procedures the agent can load.
- MCP support for connecting external tools and data sources.
- Approval gates so a human can confirm before risky actions run.
- Cron and scheduling for recurring jobs.
- SQLite as the local store, and Telegram as a chat surface.
All three are also single-user tools — built for one operator running their own agent, not multi-tenant SaaS. So the question is not “which one has features” — they all do. The question is what each was built around.
How do Atomic Agent, Hermes, and OpenClaw compare?
| Atomic Agent | Hermes | OpenClaw | |
|---|---|---|---|
| Philosophy | Local-first agent | Self-improving agent | Assistant in every messenger |
| Runs local by default | Yes — quantized model on your machine | No — cloud-oriented | No — defaults to a cloud model |
| Language / stack | TypeScript (~82K lines) | Python (larger codebase) | TypeScript monorepo (very large) |
| Memory | Own 5-layer local memory + reflection + voting | Pluggable external providers + FTS5 session search | Plugins (LanceDB, wiki, files) |
| Security model | Approval gates + shell-command filter + NDJSON traces | Honest OS isolation (Docker / SSH / Modal backends) | Approval gates across channels |
| License | MIT | MIT | MIT |
| Best for | Private, offline, no-token-bill local work | Learning-from-experience and provider breadth | Multi-channel reach and native apps |
Which is truly local-first?
This is the cleanest distinction between the three, so it is worth being precise. A local-first agent runs its model, its reasoning loop, and its tool execution on your own hardware by default — no cloud round-trip, no API key, no per-token bill.
Only Atomic Agent is built this way. Its whole architecture is organized around llama.cpp: stable-prefix KV-cache reuse to avoid recomputing shared context, GBNF grammar-constrained decoding so tool calls are always well-formed, and its own “TurboQuant” quantization to fit capable models on ordinary machines. Run it and, by default, a quantized model executes on your CPU or GPU. Nothing leaves the box. You can read how the pieces fit together in the architecture docs.
Hermes is cloud-oriented by default and supports roughly 29 LLM providers — its strength is breadth of models, not running one locally. OpenClaw defaults to a cloud model (gpt-5.5). Both can be pointed at a local endpoint, but neither is designed around it the way Atomic Agent is. If local-first is a hard requirement — privacy, offline use, or simply no token bill — the field narrows to one.
Which is most accurate?
We ran the head-to-head we could run cleanly: Atomic Agent against Hermes on GAIA Level 1 (53 tasks), same hardware and the same local model for both. Atomic Agent scored 69.8% versus Hermes’ 58.5% — a gap of 11.3 percentage points, 37 of 53 tasks solved, and roughly 1.6× faster per task (217s vs 351s median). The full methodology, including the model, context window, and per-task breakdown, is public: GAIA-L1-EXPERIMENT.md.
To be transparent: OpenClaw was not in that benchmark. This result is Atomic Agent vs Hermes only, and it reflects local-model conditions specifically — the setup Atomic Agent is tuned for. It is one benchmark, not a universal verdict. But on the workload it measures, the numbers are the numbers, and the methodology is open for anyone to rerun.
Atomic Agent, in a paragraph
Atomic Agent (ours) is the local-first option, written in TypeScript and built end to end around llama.cpp. It ships its own five-layer local memory — profile, notes, links, lessons, and procedures — with reflection and voting on top, all backed by local SQLite. Its security model is approval gates plus a shell-command filter plus NDJSON execution traces you can replay to catch drift. Pick it when the model must stay on your machine.
Hermes, in a paragraph
Hermes (from NousResearch, in Python) is the self-improving one, and it is genuinely good at it. It curates new skills from its own experience and compresses trajectories into training data, so it gets better as it runs. It is also the breadth champion: roughly 29 LLM providers and many platform adapters, plus pluggable external memory (Honcho, Mem0, Supermemory) and FTS5 session search. It is refreshingly honest that the real security boundary is OS isolation, and it leans on Docker, SSH, and Modal terminal backends to provide it.
OpenClaw, in a paragraph
OpenClaw (a large TypeScript monorepo) is the assistant that is everywhere. It reaches roughly 23 channels, ships native iOS, Android, and macOS apps, and adds Canvas and voice on top. It treats devices as nodes — your phone’s camera or screen becomes something the agent can use — and brings Codex integration, 143 extensions, and the ClawHub ecosystem. If you want one assistant reachable from every surface you own, this is the one built for it.
Which should you pick?
There is no single winner here; there is a fit for each.
- Pick Atomic Agent if you want local-first. Your data must not leave your machine, you want it to keep working offline, or you are done paying a per-token bill for long agent runs. It is the only one of the three designed around running a quantized model locally, and it is the one we benchmarked on that workload.
- Pick Hermes if you want an agent that learns from experience and you want maximum model-provider flexibility. If “gets better the more it runs” and “works with almost any LLM” are your priorities — and you are comfortable being cloud-oriented — Hermes is built for exactly that.
- Pick OpenClaw if you want an assistant in every channel with polished native apps and a large extension ecosystem. If reach and a great mobile experience matter more than running locally, OpenClaw is the most complete answer.
It is worth noting the space is actively competitive: Hermes even ships a hermes claw migrate command to import setups from OpenClaw. These teams watch each other, and the tools improve because of it.
The bottom line
All three are strong, MIT-licensed, and built on the same core loop — so pick by what they are organized around. OpenClaw is reach. Hermes is self-improvement and provider breadth. Atomic Agent is the local-first one, and on the GAIA Level 1 head-to-head under local-model conditions it edged out Hermes by 11.3 points while running about 1.6× faster.
If keeping the model on your own machine matters to you, the choice is easy — and Atomic Agent takes about five minutes to try. Start with the quickstart guide, or read the code on GitHub.