Hermes Agent Fact-Check: Solid Ground for a Deep-Dive Series

Over the last few series, I spent a lot of time zooming out and mapping agent systems.
That was useful, but one thing became pretty clear to me: the most interesting architecture is often easier to understand when you can point at a real system, inspect the code, trace the state, and ask what actually happens when something fails.
That is why I’m starting a new five-part series on Hermes Agent.
Not because I think Hermes is “the best” agent framework.
I chose it because it is open source, inspectable, and has enough real machinery to study the things I care about: gateways, sessions, prompt assembly, memory, skills, tools, delegation, scheduling, and security. The goal is to use one running system as a case study and extract patterns that apply well beyond Hermes.
Part 1 starts with a basic question:
When the same agent can be reached from a terminal, chat, or another client, where does continuity actually live?
The answer is not “the model.”
It is in the routing, session identity, runtime, persistence, and delivery contracts around it.
I trace that full path in:
Hermes Agent Architecture - Part 1: Gateway, Sessions, and the Agent Loop
Read it here: https://lnkd.in/gdxjv6n5
What open-source agent system would you want to see torn down this way next?

Confirmed accurate:

• Hermes Agent is a real, open-source, self-hosted AI agent framework built by Nous Research, released under the MIT license (released ~February 2026 per third-party trackers).
• The architecture claims match: it has a gateway layer bridging 20+ messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal, CLI, etc.) that all route through a shared session model, so continuity really does live in routing/session identity rather than "the model" — this is exactly how independent write-ups (Arize AI, DEV Community, Petronella) describe it.
• Sessions, memory (SessionDB, FTS5-indexed full-text session store, pluggable providers like Honcho/mem0), skills (procedural memory — writes reusable skills after completing tasks), subagents/delegation (spawnable isolated peer agents), cron-based scheduling, and security (plugin lifecycle hooks, sandbox pluggability) are all real, documented components of the system — so the list of topics you're planning to cover for the series maps onto actual architecture, not invented buzzwords.
• The framing "same agent reached from terminal, chat, or another client, state maintained across them" is a real, verified feature — confirmed by multiple sources describing session/config/memory syncing across desktop, CLI, and messaging gateways.

Can't verify:

• The article link (lnkd.in/gdxjv6n5) is a LinkedIn redirect I can't resolve or fetch, so I can't confirm the actual Part 1 article's content matches what's described — only that the underlying system claims are accurate based on independent sources.
• Details specific to your Part 1 (exact code traced, specific failure scenarios) aren't something I can check without the article itself.

Minor fluff:

• "I chose it because it is open source, inspectable, and has enough real machinery..." — this is framing/rationale, not a factual claim, so nothing to check, but it's doing rhetorical work (justifying the choice) rather than conveying new information.
• The closing question ("What system next?") is engagement-bait, standard for the format — not false, just not content.