How to Install and Set Up Hermes Agent: Complete Beginner’s Guide (v0.14.0, May 2026)

Hermes Agent v0.14.0 is out as of May 2026, and the install process is genuinely simple — a single command, no prerequisites, automatic dependency management. This guide covers everything you need to go from nothing to a running, configured Hermes Agent: installation on Linux, macOS, and Windows, model selection, first-run configuration, and the five most useful things to set up on day one. It also covers what NOT to do, which most guides skip.

What You Need Before You Start

Hardware minimum: 2 CPU cores, 4 GB RAM. The official recommendation. On 2 cores / 2 GB you will see instability under load. A $6/month Hetzner CX22, a $6 DigitalOcean Droplet, or any comparable VPS works fine. If you are running locally on a Mac or Linux machine with 8+ GB RAM you will have no issues.

You need an API key for a language model. Hermes is model-agnostic. The most common choices in 2026 are:

• Claude Haiku — fast, cheap, reliable tool-calling. Best for most use cases on a budget. Get key at console.anthropic.com
• DeepSeek — strong reasoning at low cost, excellent for technical tasks
• Claude Sonnet — higher capability for complex reasoning, worth it for skill-heavy workflows
• Local models via Ollama — fully air-gapped, no API costs, lower capability on complex tasks

The Hermes 4.3 model (Nous Research’s own, built on ByteDance Seed 36B, trained via their Psyche decentralised network) is worth testing if you want maximum compatibility with the skill format. Most users default to Claude Haiku for cost efficiency.

Installation

Linux and macOS

One command installs everything:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/install.sh | bash

This pulls the installer, checks your environment, installs Python dependencies, sets up the SQLite database, and drops a hermes command in your PATH. No separate Python version management, no virtual environment setup required.

macOS via Homebrew

brew install hermes-agent

pip

pip install hermes-agent

Note: As of May 2026, PyPI serves v0.13.0. For v0.14.0, use the git installer above. PyPI will catch up.

Windows (Early Beta)

Windows native support arrived in v0.9.0. Use the PowerShell installer:

irm https://raw.githubusercontent.com/NousResearch/hermes-agent/main/install.ps1 | iex

This requires WSL2 for full functionality. Native Windows is marked early beta — use Linux in production.

First Run

After install, run:

hermes

On first launch, Hermes opens an interactive setup flow asking for your LLM provider and API key. You can also set these directly:

hermes config set llm.provider anthropic
hermes config set llm.api_key sk-ant-...
hermes config set llm.model claude-haiku-4-5

Start a session:

hermes chat

Give it a complex task — something that requires multiple steps and tool calls. After it completes, check your skill library:

hermes skills list

You should see a new skill entry. This confirms the learning loop is working.

Five Things to Set Up on Day One

1. Connect Your Primary Messaging Platform

Hermes supports 14+ platforms. For most people, Telegram is the fastest to set up:

hermes connect telegram

This generates a bot token setup flow. Once connected, you can interact with your agent from your phone. Conversations are continuous — context from your desktop session is available on mobile.

2. Configure the Autonomous Curator

The Curator runs on a schedule to maintain your skill library. Set it to run daily:

hermes curator config --schedule daily
hermes curator run  # run manually to test

3. Set Up MCP Server Mode (if using Claude Desktop or Cursor)

If you use Claude Desktop, Cursor, or VS Code with Continue, expose your Hermes sessions as an MCP server:

hermes mcp serve

Then point your MCP client at the local endpoint. You now have Hermes memory and skills available inside your existing tools.

4. Create Your First Profile

If you work on multiple projects or want a work/personal separation:

hermes profile create work
hermes profile create personal
hermes profile use work

Each profile is completely isolated. Skills and memories do not cross between profiles.

5. Install the DevOps Skill Pack

The community has published skill packs for common use cases. The DevOps pack is one of the most polished:

hermes skills install devops-pack

This gives Hermes pre-built skills for server management, deployment workflows, Docker operations, and infrastructure checks — ready to use immediately without waiting for the agent to learn them from scratch.

What NOT to Do

• Do not run on 2 GB RAM — instability under load is well-documented. 4 GB minimum.
• Do not expose your Hermes instance publicly without authentication — the agent has file system access. Restrict to your local network or use SSH tunnelling.
• Do not install community skill packs without reviewing them — skills are executable code. The agentskills.io ecosystem is young. A Koi Security audit of a different skill platform found malicious entries. Review before installing.
• Do not use Hermes to process regulated data (PHI, PII) without a data mapping exercise first — the memory system stores everything. Know what is being stored and where before pointing it at sensitive data.

Updating

hermes update

This pulls the latest release, runs any database migrations, and restarts the service if running in server mode. Your memories, skills, and configuration are preserved. Check the changelog before major version updates — v0.9.0 introduced breaking changes to the skill format that required a one-time migration.

Getting Help

The official documentation is at hermes-agent.nousresearch.com. The GitHub Discussions board is active. The community Telegram group (linked from the docs) is the fastest path to answers. For the detailed Chinese-language handbook — “Hermes Agent: From Beginner to Mastery” v260407 — search GitHub for the PDF; it covers every subsystem in 63 pages and is the most complete technical reference available.

Hermes Agent rewards the first two hours of setup with a compounding return: every complex task it completes makes the next one faster. That is a different value proposition from every other AI tool in the current landscape, and it is why the GitHub star trajectory looks the way it does.

Hermes Agent is MIT-licensed, open source, and available at github.com/NousResearch/hermes-agent.