I’ve been doing some experiments with a modified Qwen3.5-9B runtime on a 4090. I’m testing a mapped/tiered KV setup where most old context (90%) can sit in normal RAM, while the GPU keeps a small hot working set and only pulls back the regions it actually needs.
I'm still new to Hermes but I've used LM Studio and Ollama a lot.

The main objective is to add massive kv cache runway to local processes.

My Setup / context

• RTX 4090, 24 GB VRAM
• Ryzen 9 7900X
• 64 GB system RAM
• Windows 11
• Running local models regularly
• Current test model: Qwen3.5 9B
• Running the 9B in bf16 for the current experiments
• Also already running Qwen3.8 27B Q8_0 in LM Studio, but it is a tight fit on 24 GB VRAM
• Interested in Hermes as an actual local agent environment, not just one off chat inference
• Main concern: longer running agent sessions and context growth on consumer hardware

Questions for Qwen3.5-9B / Hermes users

• Anyone running Qwen3.5 9B locally with Hermes?
• What hardware are you using?
• What quant are you using?
• What context size do you normally run?
• How does it behave once sessions get genuinely long?
• Does KV/VRAM pressure become a problem for you?
• Do you reduce context because of VRAM?
• Do you notice tokens/sec dropping as context grows?
• How is Qwen3.5 9B for long running agent sessions?
• How is its tool use?
• How is instruction following?
• How is coding?
• How well does it retain information from far back in a session?
• Any failure modes that only show up after long conversations?
• Anyone compared 9B with the 4B or 27B in Hermes?
• Anyone using CPU/RAM KV offload already?
• If so, how bad is the PCIe/latency penalty?

I don't expect anyone to answer all of these questions but any responses are appreciated.

This is still mostly solution in a search for a problem but very satisfying side vibe coded thing. last tools and cron jobs are closest so far to feeling more connected to what Hermes is doing in background

this post is basically a love letter to the Nous team, Peter Steinberg who got me into AI agents, AI in general, and of course you (my dear readers).

I have never talked about my past as openly as I am talking about it today. Only one year back I still worked full-time as an industrial mechanic in a factory. But let's start from the beginning.

I was really bad at school, so the only way to get an apprenticeship was to become a mechanic and start working in a factory. So I did. At first it was pretty good, I even liked it. But towards the end of my apprenticeship I started to feel that this was not what I envisioned for myself and not the job I wanted to do for the rest of my life or raise children with. I developed the deep desire to start my own business (probably a deep insecurity from childhood because I was always the weird kid, the kid that could not get what he wants for himself. I always needed help or somebody else. I also was far from the smartest). Now I believe that if you imagine a specific life for yourself and do not settle for anything else and work hard, you will reach it 100%. That is the faith I have in life.

So I started my own business on the side. Started selling merchandising products for a German car YouTuber. I sold around 15 clothing items over the span of around 8 months. But I had to close that because it did not work out and my partner was not on the same wavelength as me. Lost about 1500€ on that project. After that I tried selling websites because I always liked designing websites. I actually started doing that when I was around 15 years old. I even walked around in industrial areas throwing flyers of my web design business into post boxes. But all did not work.

One day I heard about OpenClaw and from the first minute I knew I needed to have that. I love tracking everything digitally. I have been wearing a Whoop for like a year, also been logging my meals in a food logging app. I even log every cent I spend, even calculate how much gas my car consumes on 100km (around 13L/100km or 18.09 US MPG). I love creating systems and logically working paths and workflows, so having my own AI assistant that would connect to all that information and give me scheduled updates was a no-brainer to me. I tried OpenClaw for a few weeks before switching to Hermes because of the reliability and the promise of it being more cost-efficient.

I had high hopes for my AI agent but let me tell you it completely shattered all expectations. I fucking fell in love with computers, computer systems, websites, AI coding, generating web applications, tinkering with computers and the agent.

This agent has given me the ability to run a YouTube channel, blog, Reddit account, sport, nutrition, business and many other hobbies and interests, all while also studying my first semester of business school. Even though I have never even visited the in Germany normally mandatory Gymnasium to be able to start a bachelor's degree.

What my agent manages for me:

• tracking nutrition, sport, mental health
• creating new blog posts out of Reddit posts or YouTube videos. I write the text, Hermes just creates a new post for it and manages the websites and even my VPS server
• generating asset pictures for videos or thumbnails
• being my infrastructure advisor about my Hermes agent, VPS, Mac mini and home network
• being my content creation advisor. He can download full Reddit posts, transcribe YouTube videos with comments and vote numbers, thereby he can tell me what topics are hot and what questions I received most
• he can give me insight into how much time I spend on learning for my exams, even detailed statistics about time I spent on the specific topic
• helps me while tinkering and playing around with my tech projects

I'm so thankful for this opportunity. My business is now even centered around Hermes. I literally earn money by doing what I love, tinkering with my Hermes agent, developing new and better ways to set up those agents. And all that on the shoulders of the Nous team, OpenClaw team, various AI teams, and not the least you (the reader).

With AI it is now possible to learn anything just by trying over and over again. I have no friends right now, we all split up one way or another. I'm currently alone with my girlfriend, but she's not really into the technical stuff. With AI and the community of Reddit and YouTube I can learn pretty much anything now. Lately I try to solve every computer problem by coding my way out of it. Something I'm really proud of, and something I have fun doing every day. Thank you Hermes for making this possible.

I’ve been a daily Hermes Agent user and it’s basically taken over half my life (in a good way).

I’ve been using it for almost everything — managing my server, handling Sonarr/Radarr, and even tackling some pretty complex coding tasks. The costs have been getting a bit crazy since I’ve been running Opus 5, but damn has it been worth it. It’s helped me a lot more than I expected.

Anyone else deep in the Hermes + Opus rabbit hole?

I've been thinking about a problem with writing agents: we keep telling them things like “make this clearer,” “sound human,” or “be concise.”

Those aren't really writing systems. They're vague preferences, so the model eventually drifts back toward its default voice.

I built Agent Stylebooks around a different idea: take editorial systems that real organizations have spent years developing and turn their principles into reusable Agent Skills.

There are now 16 skills, including:

• google-developer-docs — API docs, tutorials and setup guides
• govuk — public-service and eligibility content
• gitlab-docs — concise engineering/product documentation
• github-docs — product workflows and troubleshooting
• kubernetes-docs — version-sensitive infrastructure docs
• mdn-web-docs — web technology explanations and references
• red-hat-docs — enterprise procedures and runbooks
• 18f-content — accessible digital public services
• microsoft-writing-style — product help and support copy
• mailchimp-content — human customer communication
• apple-interface-writing — buttons, alerts and UI copy
• cdc-clear-communication — public-health communication
• nhs-health-content — patient-facing health information
• sec-plain-english — financial/investor disclosures
• w3c-technical-reports — specifications and standards
• nasa-technical-writing — engineering and technical reports

The useful part for Hermes is that these aren't 16 giant prompts you keep pasting into chat. They're skills you install once and invoke when the artifact calls for one.

For example, the topic could be Kubernetes, but the correct style depends on the job:

$kubernetes-docs for a deployment tutorial.

$govuk For a government page explaining whether companies qualify for an infrastructure program.

$apple-interface-writing For the buttons and errors inside a cluster-management UI.

Same subject. Different writing problem.

You can inspect the catalog first:

npx skills add Neeeophytee/agent-stylebooks --list

Install one:

npx skills add Neeeophytee/agent-stylebooks --skill google-developer-docs

Or install the whole collection:

npx skills add Neeeophytee/agent-stylebooks

Then the actual instruction to Hermes can stay simple:

Use $google-developer-docs to turn these verified implementation notes into a setup guide.

The skills are also deliberately conservative about substance: changing style must not change facts, commands, versions, legal meaning or technical behavior. The repo includes source/provenance notes rather than just scraping style guides into prompt files.

It's free/open source(MIT):

Repo: https://github.com/Neeeophytee/agent-stylebooks

I'm especially curious how people here would use this with Hermes' own skill system. Which writing system is missing that you'd actually install?

GPT-5.6 Sol Pro costs 504× more per session than MiMo-V2.5 on Hermes.

I tested Hermes Agent’s new Bot Mode with 3 AI agents here’s what actually worked (and what didn’t)

Hermes Agent recently released Bot Mode, and I wanted to test whether it actually makes multi-agent collaboration easier or if it’s mostly a nicer UI for existing Hermes profiles.

So I built a small 3-agent Stocks research team:

🔎 Researcher — gathers evidence from public filings and market data
⚠️ Risk Analyst — challenges the numbers, assumptions, unsupported claims, and missing risks
📝 Thesis Editor — turns the reviewed evidence into a balanced final report

I only gave the task to the Researcher.

The Researcher gathered the evidence and handed it to the Risk Analyst. The Risk Analyst reviewed it and was supposed to hand the result to the Thesis Editor.

So instead of:

Human → Agent A → Human → Agent B → Human → Agent C

I was testing:

Human → Agent A → Agent B → Agent C → Human

And for the most part, it worked.

What I like about Bot Mode is that the agents aren’t just invisible calls in a workflow. Each bot has its own identity, role, tools, skills, memory, and persistent conversation, so you can actually inspect what each specialist is doing.

But there are some important limitations.

It’s also not a full workflow/DAG engine, and the handoffs don’t mean you’re getting guaranteed parallel execution.

My current mental model is:

🧠 Hermes Profiles = specialized isolated brains
👥 Bot Mode = gives those brains identities, persistent rooms, and communication
🗂️ Hermes Kanban = better when you need structured tasks, dependencies, and project orchestration

For complex multi-agent projects, I’d still lean toward Kanban. But for maintaining a group of persistent AI specialists that can talk and delegate to each other, Bot Mode makes the experience much easier to operate.

I recorded the entire experiment, including creating the bots, configuring them, the handoffs, the failed delegation, scheduled jobs, and the differences between Bot Mode, Profiles, and Kanban:

🎥 https://youtu.be/w3VI6zC4_0I

I’m curious how others building multi-agent systems think about this approach.