How to Build a Personal AI Assistant with Hermes Agent

The era of relying solely on closed-source, cloud-based chatbots is ending. With the release of Hermes Agent by Nous Research, developers and power users now have the tools to build their own persistent, highly personalized AI assistants that run locally or on private servers.

Here is how you can start building your own personalized AI agent, and why your historical chat data is the secret to making it truly yours.

1. Setting up the Hermes Agent Framework

Hermes Agent is built to be flexible. You can connect it to local models (like Hermes 3 running via Ollama) or cloud providers via OpenRouter. Because it supports multi-agent architectures, you can assign different “personas” to handle different tasks—one for coding, one for web research, and one for managing your calendar.

2. Enabling Tool Use

What makes Hermes Agent powerful is its ability to interact with the world. You can give your agent access to:

• A Python execution environment for data analysis.
• Browser automation tools to scrape websites.
• API keys to read and write to your Notion or Obsidian workspace.

3. Training It With Your Data (The Pczio Method)

An AI is only as good as the context it has. If you have been using ChatGPT, Claude, or Gemini for the past year, you already have a goldmine of context: your coding style, your business plans, and your writing tone.

To transfer this knowledge to your Hermes Agent:

1. Use the Pczio ChatGPT Downloader or Pczio Gemini Downloader.
2. Select your most valuable chat threads and export them as JSON or Markdown.
3. Place these files into your Hermes Agent’s vector database or persistent memory folder.

By feeding your Hermes Agent with perfectly formatted Markdown files from your past interactions, you skip the “cold start” problem. Your open-source agent will immediately understand your context and operate exactly how you want it to.