What if you could build an AI agent that can:
✅ Think and reason
✅ Search the web
✅ Read and write files
✅ Generate reports and dashboards
✅ Run entirely on your own machine
Without:
❌ OpenAI API keys
❌ Anthropic subscriptions
❌ Monthly AI bills
❌ Sending your prompts and files to third-party servers
That's exactly what I built.
In this tutorial, I'll show you how to create a fully local AI agent stack using:
🤖 Hermes Agent
🧠 Qwen 3.5 9B via Ollama
🔎 SearXNG
The result is a powerful AI agent that costs $0 to operate, keeps your data private, and gives you complete control over your AI infrastructure.
🎥Full video walkthrough:
🤔 Why Build a Local AI Agent?
Most AI agents today depend on cloud APIs.
Every prompt, file, and conversation gets sent to someone else's servers.
For many use cases, that's perfectly fine.
But what if you're working with:
🔒 Sensitive business information
🔒 Private research data
🔒 Customer documents
🔒 Internal company knowledge
🔒 Personal notes and files
In those scenarios, privacy matters.
A local AI agent means:
✅ Your data never leaves your machine
✅ No third-party access to your prompts
✅ No API costs
✅ No rate limits
✅ Full ownership of your stack
And thanks to modern open-source models, local AI is becoming surprisingly capable.
🏗️ The Architecture
Our stack consists of three components.
🤖 Hermes Agent
Hermes Agent is an open-source AI agent framework developed by Nous Research.
Instead of just chatting with an LLM, Hermes turns the model into a true agent with:
- Memory
- Tool usage
- Workflows
- File access
- Web search
- Task execution
Think of it as the operating system for your AI agent.
🧠 Qwen 3.5 9B via Ollama
Next comes the brain.
We're using Qwen 3.5 9B running locally through Ollama.
Ollama makes it incredibly easy to run modern open-source language models on your machine.
The model handles:
- Reasoning
- Planning
- Decision making
- Report generation
- Tool calling
And because it's running locally, every token stays on your hardware.
🔎 SearXNG
The final piece is SearXNG.
SearXNG is a privacy-focused meta search engine.
Instead of tracking users like traditional search providers, it aggregates results from multiple search sources while preserving privacy.
For AI agents, this means:
✅ Web search capabilities
✅ No tracking
✅ Self-hosted infrastructure
✅ Complete control
⚡ What Makes This Stack Interesting?
Most developers assume AI agents require expensive cloud infrastructure.
But with this setup:
💰 API Cost = $0
🔒 Data Privacy = 100%
⚙️ Infrastructure Ownership = 100%
🛠️ Customization = Unlimited
Everything runs locally.
Everything remains under your control.
🎯 Real Demo
To test the setup, I gave the agent a simple task:
Find the latest AI news and create an HTML report.
Here's what happened.
Step 1
The agent used SearXNG to search the web.
Step 2
It gathered and synthesized information from multiple sources.
Step 3
It generated a structured HTML report.
Step 4
The file was saved locally on my machine.
No cloud APIs.
No external AI providers.
No third-party processing.
Just a fully local AI agent doing real work.
🔥 The Best Part: It Scales
One thing I love about this architecture is that it grows with your hardware.
Starting point:
🧠 Qwen 3.5 9B
Future upgrades:
🚀 Larger Qwen models
🚀 70B parameter models
🚀 400B parameter models
🚀 Multi-GPU setups
The architecture stays exactly the same.
You simply swap in a more capable model.
The only real limitation is your hardware.
💡 Potential Use Cases
Developers are already building some fascinating things with local AI agents.
Examples include:
📚 Research assistants
📄 Private document analysis
💻 Coding assistants
📈 Market research workflows
📰 News aggregation systems
📋 Report generation pipelines
🏢 Internal company knowledge assistants
🔬 Scientific research agents
🔒 Privacy-first enterprise AI solutions
Because everything is self-hosted, these use cases become much easier to justify from a security and compliance perspective.
🌍 Why Local AI Is Becoming a Big Deal
The AI industry spent the last few years moving everything to the cloud.
Now we're seeing another trend emerge:
Bringing AI back to the device.
Open-source models are improving rapidly.
Consumer hardware is becoming more powerful.
Agent frameworks are becoming more capable.
As a result, local AI is no longer just a hobby project.
It's becoming a practical option for real-world applications.
The combination of:
🤖 AI Agents
🧠 Open Models
🔒 Privacy
💰 Zero API Cost
is incredibly compelling.
💬 What Would You Build?
If you had a fully private AI agent running entirely on your own machine...
What would you build?
A coding assistant?
A research agent?
A private knowledge system?
A business automation workflow?
Let me know in the comments. I'm always curious to see what developers are creating with local AI.
Top comments (1)
🚀 Local AI is getting surprisingly powerful.
Do you think most AI agent workflows will still rely on cloud APIs 2 years from now, or will open-source models + local hardware become the default?
Interested to hear different perspectives 👇