01What the Hermes AI Agent Is and How It Works
The Hermes AI agent is an AI operating system, not just another chatbot. It remembers past conversations, creates reusable skills, learns from experience, and improves itself over time. Instead of answering one prompt and forgetting, it builds a long-term profile of your working style, project knowledge, and workflows.
Hermes runs continuously in the cloud or locally. It watches your successful workflows and converts them into reusable skills. The self-improving loop works like this: save an experience, generate a skill, improve that skill, and reuse it automatically.
Next time you need that workflow, you do not prompt again — the agent already knows how to do it.
It also stores your preferences, past conversations, documentation, and projects. This makes it a permanent teammate, not a stateless tool. You can host it on your own machine using Docker, SSH, Modal, a cloud VPS, or a GPU server.
Everything is open source.
02Main Features of the Hermes AI Agent and Laguna S 2.1
The biggest feature is the self-improving skill system. Hermes watches your workflows, then saves them as reusable skills. Over weeks, it literally gets smarter.
It combines memory, skill creation, and automation into one system. Think of ChatGPT memory, Cursor rules, Claude projects, MCP, and AutoGPT all combined.
Laguna S 2.1 is the new brain from Poolside. It is an open-weight coding model built specifically for software engineering and autonomous agents. It uses a mixture-of-experts architecture with 118 billion total parameters, but only 8 billion active during inference.
That makes it very efficient. The context window goes up to 1 million tokens, depending on the provider, with common 256,000-token deployments. This lets your agent ingest entire code bases without forgetting.
Laguna supports thinking mode, reasoning, planning, tool calling, and multi-step execution. Its benchmarks are strong: 70.2% on Terminal Bench 2.1, 40.4% on Deep SWE, 78.5% on SWE Bench Multilingual, and 59.4% on SWE Bench Pro. These numbers place it among leading coding models while using far fewer active parameters.
03How to Set Up the Hermes AI Agent with Laguna S 2.1
Julian Goldie shows how to run Hermes inside Agent OS, a pre-built dashboard he created. The setup takes five steps and replaces Hermes’s default model with Laguna S 2.1 through OpenRouter.
- Open the Hermes control room inside Agent OS. This is where the system manages API keys and providers for you.
- Add OpenRouter as the provider. Drop your OpenRouter API key in, and Agent OS routes Hermes’s requests through it.
- Select Laguna S 2.1 as the model. Go to OpenRouter, search for “Laguna S 2.1”, grab the model string, and set it as Hermes’s primary model in the control room.
- Set a fallback model. Julian always configures a second model as a fallback, with Ollama as a last-resort local backup.
- Connect the memory layer. Agent OS logs every Hermes exchange into an Obsidian vault and pulls context back out. This gives Hermes full context about your business, goals, and projects.
After these steps, Hermes runs on Laguna’s reasoning with persistent memory. The whole system is open source and can run locally.
04How to Use the Hermes AI Agent with Laguna S 2.1 in Agent OS
Once set up, Agent OS gives you a mission control dashboard. You see every agent, live status, full chat history, and a memory section that logs your goals, daily journals, and every conversation. Hermes now has a self-improving skill system, a 256k to 1 million context coding brain, and persistent memory about your actual business.
To use it, you simply tell Hermes what you need done. Because it remembers past work, you do not have to repeat yourself. Hermes can automate workflows, generate code, write documentation, or manage projects.
It watches your successful actions and converts them into reusable skills. Over time, you rely less on prompting and more on the agent knowing what to do.
Julian explains that your role changes from doing tasks to operating the system that does them. The dashboard lets you monitor every agent, adjust settings, and review memory. Everything runs locally on your machine, so your data stays yours.
05Results, Tests, and Comparisons Shown in the Episode
Julian compared ChatGPT, Claude Code, Cursor, and Devon against the Hermes plus Laguna combination. ChatGPT has partial memory, no self-learning, good coding, and is closed. Claude Code has limited memory, no self-learning, excellent coding, and is closed. Cursor gives project rules, partial learning, excellent coding, and is closed. Devon has memory and partial learning with excellent coding, but it is also closed.
Hermes plus Laguna gives you real memory, genuine self-learning, excellent coding, and it is fully open weight. The Laguna benchmarks show it outperforms models several times its size. It scored 70.2% on Terminal Bench 2.1 and 40.4% on Deep SWE.
The efficient mixture-of-experts architecture uses only 8 billion active parameters, far fewer than competitors that use 16 billion, 32 billion, or even 64 billion active parameters.
The upside is a fully open ecosystem with persistent memory, self-improving skills, and strong coding benchmarks. The downside is more setup than a hosted tool, and it is evolving fast.
06Tips, Limits, and Mistakes to Avoid with the Hermes AI Agent
Julian notes that the main limit is setup complexity. Most people’s AI setup is disconnected tools — ChatGPT in one tab, Claude in another, no memory between them. Disconnected tools give disconnected results. A system gives compounding results. Agent OS fixes that by putting everything in one dashboard.
The Hermes AI agent is best suited to technically comfortable users right now. It is evolving fast, and real-world performance still needs more testing beyond benchmark scores. To avoid mistakes, always set a fallback model in case the primary goes down.
Julian recommends Ollama as a last-resort local backup.
Also, connect the memory layer properly. Without persistent memory, Hermes loses context between sessions. The Obsidian vault integration keeps all history accessible.
Finally, do not expect a plug-and-play experience like ChatGPT. This is a more powerful but more involved system.
07Who the Hermes AI Agent Is For and Why It Matters
The Hermes AI agent is for people who want to automate their business with AI agents and are comfortable with a bit of technical setup. It matters because it changes your role from doing tasks to operating a system that does the tasks. Julian compares it to owning a hammer versus running a construction company.
If you manage multiple projects, codebases, or workflows, a single dashboard with memory and self-improving skills saves enormous time. The combination with Laguna S 2.1 gives you a coding brain that can handle entire repositories. Everything is open weight, so you control your data and deployment.
Julian packaged the entire Agent OS into a zip file, which includes the mission control dashboard, Hermes integration, OpenRouter and Laguna configuration, Obsidian memory layer, goals and journal system, plus 115 prompts. It is available inside the AI Profit Boardroom for those who want to skip the wiring.
08How Agent OS Ties the Hermes AI Agent and Laguna Together
Agent OS is a local-first operating system Julian built for managing Claude, OpenClaw, Hermes, and any other agent. On the dashboard, you see every agent, live status, full chat history, and memory. It routes Hermes’s requests through OpenRouter, logs exchanges into Obsidian, and pulls context back out.
When Laguna is selected as the brain, Hermes has both a self-improving skill system and a top-tier coding model. The memory layer gives Hermes full context on your business, goals, and projects. Julian says this combination is a serious one: Hermes’s memory plus Laguna’s reasoning.
The zip file includes everything pre-built. You download it, install it, and you are running. This turns weeks of setup into minutes.
The 30-day roadmap helps you go from zero to a fully operational agent stack.
