Free Local AI Agent: Hermes + Ollama Setup Guide

How to setup Hermes with Ollama — and why it's the best free local AI agent stack right now.

Most AI agents cost real money to run.

API tokens add up fast.

A serious daily-use agent on cloud can hit £100–£500 a month easy.

Local agents fix that.

Hermes + Ollama is the combo I use to run a free local AI agent that handles 80% of my daily work.

Here's the full setup.

Why Free Local Wins

Three solid reasons.

1. No bills. Local models cost nothing per token.

2. Full privacy. Your data stays on your machine.

3. Always on. No rate limits, no outages, no internet needed after install.

The catch?

Local models aren't quite as smart as the best cloud models.

For 80% of work — research, writing, automation, scraping — you won't notice the gap.

What You Need

Two things.

1. Hermes. The AI agent itself. Free, open source.

2. Ollama. The local model runner. Free, open source.

That's it.

No paid tier.

No subscription.

The Full Setup

I'll walk you through every step.

Step 1 — Install Ollama

Go to ollama.com.

Download for Mac, Windows, or Linux.

Run the installer.

When it's done, Ollama runs in the background as a local API server.

You can verify it's running by visiting http://localhost:11434 — should show "Ollama is running".

Step 2 — Pick your model

Browse Ollama's model library.

Best free local picks for Hermes:

DeepSeek (small, agent-focused)
Gemma 4 (~7GB, ultra-light)
Qwen 3.6 (general-purpose)
Nemotron 3 Nano Omni (28GB, Nvidia's new agentic model)

For a free local AI agent on a normal laptop, I'd go Gemma 4 first.

Step 3 — Download the model

Open terminal.

Run the install command from the model's Ollama page (e.g. ollama run gemma4).

Wait for the download.

That's the longest part — 5–20 minutes depending on model size.

Step 4 — Install Hermes

If you haven't yet, follow the standard Hermes install on its GitHub.

If terminal commands intimidate you, copy the install line and ask Claude Code or Codex to run it for you.

I cover that shortcut in Free Claude Code.

Step 5 — Connect Hermes to Ollama

Open Hermes config.

Add your local Ollama as a provider:

Provider: ollama
URL: http://localhost:11434
Model: gemma4

Save.

Restart Hermes.

Step 6 — Send a message

Open Hermes.

Pick the Ollama provider.

Send "hello — are you working?"

If you get a reply, you're done.

🔥 Want my full free local AI agent stack? Inside the AI Profit Boardroom, I share my exact Hermes + Ollama config, recommended models per task, system prompts, and a 2-hour Hermes course. Plus weekly live coaching where you can share your screen and we'll set this up together. 2,800+ members already inside. → Get the stack here

Hardware Requirements

Be honest about what your machine can handle.

Light laptops (8GB RAM): Stick to small models like Gemma 4 (7GB).

Mid-range (16GB RAM): DeepSeek, Qwen 3.6 work great.

High-end (32GB+): Run Nemotron 3 Nano Omni or larger Qwen variants.

I run a Mac Studio so I can throw bigger models at it.

But if you're on a normal MacBook or Windows laptop, Gemma 4 is plenty.

What You Can Build With This Stack

Once Hermes + Ollama is running, you've got real AI agent powers.

Things I use mine for:

Daily SEO content drafts.
Email triage.
Web scraping with the browser skill.
Research summaries.
Tweet drafts and captions.
Code reviews.

Hermes has 70+ skills, all of which work with Ollama models.

Cover more skill use cases in Hermes Agent Workspace.

How Free Stays Free

Two costs to watch out for.

1. Electricity. Running models locally uses your CPU/GPU. It's pennies per day, not pounds. But on a laptop, it'll heat up.

2. Storage. Models take disk space. Gemma 4 is 7GB. Nemotron 3 is 28GB.

Otherwise — completely free, every day, forever.

Free Cloud Alternatives Within Hermes

If your hardware can't handle local models, Hermes also supports free cloud tiers.

Recommended free cloud combos:

Kimi K2.5 free tier
GLM 5.1
Qwen 3.5 Cloud
Z AI

These have token limits but they're genuinely usable.

I covered some of these in Kimi K2.6 Agent Swarms.

Comparing Free Local Vs Free Cloud

Quick breakdown.

Setup	Cost	Speed	Privacy	Limits
Local Ollama	Free	Hardware-dependent	Full	None
Free cloud	Free	Fast	None	Token caps
Paid cloud	$$$	Fastest	None	Higher caps

For a daily-use free local AI agent, Ollama wins.

For occasional use, free cloud is fine.

Common Setup Mistakes

Three things people get wrong.

1. Picking too big a model.

Start small. Gemma 4 first. Upgrade later if needed.

2. Forgetting to keep Ollama running.

Hermes needs Ollama running in the background. Close Ollama and Hermes loses access.

3. Running models that don't fit RAM.

If your laptop has 8GB and you try to run a 28GB model, you'll crash.

Match the model size to your machine.

The Real Win

I shifted 80% of my Hermes workload to free local Ollama models.

API bills went from £200/month to under £20.

The remaining 20% I run on cloud — for tasks that genuinely need bigger models.

That's the win.

A free local AI agent that handles most work, with cloud as a backup when needed.

🚀 Want my full Hermes automation system? The AI Profit Boardroom includes a 2-hour Hermes course, daily training drops, and weekly screen-share coaching. Plus a 6-hour OpenClaw course if you want to compare both AI agents. 2,800+ members building real automations. → Join here

FAQ — Free Local AI Agent With Hermes And Ollama

Is Hermes + Ollama really free?

Yes — both tools are free, and local models cost nothing per token.

How much RAM do I need?

8GB minimum for small models like Gemma 4.

16GB is comfortable.

32GB+ if you want the bigger models.

Does it run on Windows and Linux?

Yes — Ollama supports Mac, Windows, and Linux.

Hermes works on all three.

Will my agent work offline?

Yes — once Ollama and the model are installed, you can run fully offline.

How fast is local vs cloud?

Cloud is generally faster on first response.

Local can match it for short responses on smaller models.

Can I run multiple local models at once?

Yes — Ollama can host multiple models.

Switch between them in Hermes whenever you need.

Is the agent quality good enough for real work?

For research, writing, scraping, automation — yes.

For very complex reasoning, you might still want cloud.