hermes-agent VS ollama

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:

Download: Go to https://ollama.com/ and click on the download link for your operating system.

Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine.

To solve these, we built a stack that prioritizes integrity over ease. The centerpiece is Ollama, running the mxbai-embed-large model locally. This is the engine that translates human thought into high-dimensional coordinates.

Ollama for model serving

Fully offline: Point it at Ollama or LM Studio. Zero cost, nothing leaves your network.

Recommended hardware: The RTX 3060 with 12 GB VRAM is the budget king here — all these models fit with room to spare for KV cache overhead, even Gemma 4:12B (which needs ~8.5–9 GB with overhead). An RTX 4060 Ti 16 GB gives you more headroom. On the Apple side, any M2 or M3 MacBook with 16 GB unified memory handles these models comfortably via Ollama's Metal backend.

This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b. Same tools, same workflows, no data leaving your network.

Related: 35B MoE on 2× 1080 Ti · Ollama

The sample is a tiny console app running entirely against a local Ollama model — no cloud keys, and every HTTP call is traced so I can see exactly what goes over the wire (complete sample code). There's a single skill on disk:

irm https://ollama.com/install.ps1 | iex