hermes-agent VS openclaw

NousResearch publishes a detailed security policy for Hermes Agent. It is unusually clear about what the project treats as load-bearing and what it does not. If you operate Hermes in the cloud, read it first; this post is the operator-friendly companion, not a replacement.

OpenCode seemed perfectly workable as a programming assistant. As personal assistants, they all fall short. It's too difficult to really shape their output.

I was briefly impressed with OpenClaw a few times, but ultimately was turned off by not being able to get the models to stop being so damnably verbose. I thought I made progress for a while by having it tweak its soul, iterate, switch models, iterate, switch models, fuse the results, iterate... but ultimately it's all forgotten early in each session. And then one day it killed itself by rebuilding the container it was inside.

Hermes apparently has some plagiarism issues they're trying to cover up [0] and I was deeply unimpressed with their janky, flickery CLI that force-enables a bulky obnoxious header.

Nanoclaw and nanobot seemed fine, but not notably different. There were some common bugs and glitches that caused some minor data loss while configuring nanobot. After that I just deciding to start hacking my own together.

What I really want in a harness is being able to actually control and rewrite the entire context window, like Zed's Text Threads before they obnoxiously and inexplicably removed what, to me, was their most powerful and distinguishing feature.

[0] https://github.com/NousResearch/hermes-agent/issues/10232

I've opened a design discussion to argue this out before anyone writes a line of it, because a surprise PR to a security-sensitive module is the wrong way to start. Feedback from people who've thought about supply-chain trust is what I appreciate.

Source: https://github.com/NousResearch/hermes-agent

Hermes Agent is an open-source AI agent framework.

I opened my Hermes Agent logs and found something I did not know existed: an auxiliary: block with twelve background tasks. Compression, web extraction, vision, session search, skills matching — all running silently every time I typed a message. Every task was set to provider: auto. And because I had no API keys for the fallback chain, every one silently fell back to kimi-k2.6, my one-trillion-parameter main model.

The adjacent tools worth tracking: the OpenAI Chrome plugin, BrowserCode, Autobrowse, browser-harness, Pi browser extensions, and Hermes browser skills. [13] [14] [6] [15] [16] [12] The category is logged-in browser work: support queues, internal tools, research, scraping, QA, admin ops, and anything where the useful data sits behind a session.

hermes-telemetry solves both by giving you real-time observability and automatic budget enforcement for Hermes Agent.

If you haven't encountered it yet: Hermes Agent is an open-source agentic system from Nous Research. The key differentiators that caught my attention:

I run OpenClaw on a Hetzner CAX ARM VPS. It talks to me over Signal and does a morning press review. Three gotchas on that box are worth writing down: one money trap in the model-routing layer, and two silent failures that each left the briefing dead for days. In case someone is staring at the same thing.

The base stack (layers 1–2) is built into OpenClaw — conversation compression, native memory files, and semantic search work out of the box. The long-term engine (layer 3) requires additional setup: Mem0 needs a vector store, Cognee needs a graph database, Graphiti runs on FalkorDB.

OpenClaw — github.com/openclaw/openclaw

For context, I checked the public repos directly: openclaw/openclaw and NousResearch/hermes-agent. OpenClaw has the bigger gravity right now. Hermes has the more interesting agent-runtime thesis.

Set up OpenClaw or a local LLM (Ollama or LM Studio) to index your vault and answer questions via Telegram or WhatsApp, as a private assistant that never sends your data to the cloud.

Haven’t noticed much significant progress in LLMs myself in 6 months (significant as in new or vastly improved capabilities or understanding, not new releases, there are plenty of those).

So what’s happening with openclaw, the biggest experiment in agentic, vibe coded by the agents themselves? The thing that was so hot a few months ago.

https://github.com/openclaw/openclaw/pulse?period=daily

279 commits to main from 77 authors in the last 24 hours.

Why is there so much churn and how could you trust it with your data? This is changes in ONE day!

If these are useful changes, surely it’d be superhuman by now given months of dev work.

What are people using this for?

⚔️ How it compares with two adjacent open-source projects: OpenClaw and GoClaw — when to pick which

OpenClaw is that thing. It's an open-source personal AI assistant with 367K GitHub stars, a plugin ecosystem, and connectors for every chat platform you can name. The pitch: "Your own personal AI assistant. Any OS. Any Platform."