I Read Hermes Agent’s Memory System, and It Fixes What OpenClaw Got Wrong

**I Read Hermes Agent’s Memory System, and It Fixes What OpenClaw Got Wrong**

If you’ve ever wondered how AI agents actually remember things, you’re not alone. Manthan Gupta has been digging into this exact question, and in his latest breakdown he explores the memory architecture behind Hermes Agent. Instead of guessing from behavior, he went straight to the source code. You can read his full thread here:
https://x.com/manthanguptaa/status/2034849672985288957?s=52

What he found is surprisingly thoughtful.

Hermes doesn’t rely on one big memory bucket. It uses **four distinct layers**, each with a clear role. First, there’s a tiny, curated prompt memory stored in simple text files. We’re talking about a compact, high‑value snapshot of durable facts, things like user preferences or stable conventions. Not a diary. Not a running log. Just the essentials.

Then there’s a searchable SQLite archive for past sessions. Instead of stuffing long histories into every prompt, Hermes retrieves old conversations only when needed. It’s a bit like keeping your desk clean and using the filing cabinet only when you actually need a document.

It doesn’t stop there. Hermes also supports **skills**, which act as procedural memory. This is the “how to” layer. If the agent learns a workflow, it can store and reuse it later. And optionally, there’s Honcho, a deeper user modeling layer that adds cross‑session continuity without breaking prompt stability.

That last part is key. Hermes is obsessed with keeping the system prompt stable for caching. If something doesn’t need to live in the prompt all the time, it gets pushed to a tool or a retrieval layer. That’s a practical, production‑ready mindset.

Compared to systems like OpenClaw, which lean heavily on searchable memory logs, Hermes feels more structured. More layered. More intentional.

And honestly, this layered approach feels like where serious AI agents are headed next. Not bigger memory. Smarter memory.