The same thesis — open-source DIY vs. governed and batteries-included.
Nous Research's Hermes is an open-source agent that lives on your server, remembers what it learns, and turns solved workflows into reusable skills. QUAN 2.0 shares that exact thesis — and ships it as a finished, governed product rather than a framework to assemble.
The short version. Hermes and QUAN want the same things — a server-resident agent that remembers and compounds skill over time. They differ on how you get there. Hermes hands you open weights and an open framework: maximum control and zero lock-in, at the cost of assembling, hosting, governing and maintaining it yourself. QUAN hands you the finished article: governance, multi-model routing, connectors and UIs already wired, at the cost of being closed and owned. QUAN even installed Hermes' creative skill pack — they're more kin than competitors.
A finished platform: memory + learning loop, a house-skills runtime, multi-model routing that includes frontier closed models, connectors, native and web UIs, roles, approvals and audit — all assembled, hosted, and ready to use.
An open-source framework built around open-weight Hermes models (14B / 70B / 405B). You own the whole stack: self-host anything, fine-tune, export RL trajectories, and run with no vendor lock-in. Persistent memory and skill-building are first-class.
An honest side-by-side. Yes, partial, or no — with a note where it matters.
Hermes is for teams that want to own every layer: open weights you can fine-tune, an open framework you can rewrite, no dependency on any API. QUAN is for teams that want the outcome now — the governance, the connectors, the UI and the multi-model routing already built — and accept a closed, owned stack in exchange.
Because Hermes ships open weights, you can run it air-gapped, fine-tune it on your own data, and generate training trajectories from real work. QUAN deliberately routes to the strongest model per task — including frontier closed models — which is more capable per token but not something you own.
“Lives on your server, remembers, builds skills” describes both systems. QUAN's bet is that most teams want that thesis delivered, not assembled — with approvals and audit so it's safe to share — and it borrows freely from Hermes' design, including its skill packs.
No comparison is worth much if it only flatters one side. Here's where Hermes is the better choice.