Hermes Agent Tutorial

Hermes Agent was developed by Nous Research and officially released in February 2026.Open-source self-evolving AI Agent, released under the MIT License.

Hermes runs on your own server or local machine, maintains persistent memory across sessions, and proactively learns and distills reusable skills after completing each task—Smarter with use。

Nous Research Official Slogan --The agent that grows with you.

Who should read this tutorial?

This tutorial is intended for the following types of readers:

  • Developers / EngineersWant a local AI assistant that can persistently remember project context and automatically accumulate workflow experience, rather than re-explaining codebase structure, naming conventions, and deployment processes from scratch every session.

  • ResearchersNeed an intelligent assistant that can track research progress across sessions, automatically organize literature information, and execute long-term research tasks.

  • Efficiency tool enthusiastsHope to truly embed AI Agent into daily workflows — connect to common platforms such as Telegram, Slack, and Discord, and set up scheduled automated tasks.

  • AI/ML practitionersResearchers interested in Agent architecture, or those who need to use Hermes to batch-generate tool-calling trajectories for reinforcement learning training data.

  • Users who prioritize data privacyAll data stays on the local machine — no telemetry, no tracking, no cloud lock-in.

Prerequisites before reading

This tutorialNot requiredAI research background or deep machine learning knowledge. You need to have:

Skills Requirement level Description
Basic command line operations Required Ability to execute commands and set environment variables in the terminal
Python basics Familiarity is enough Know how to install packages with pip and read simple scripts
Basic API concepts Familiarity is enough Know what an API Key is and how to apply for one
Git basics Optional Used in advanced chapters (plugin development)

Operating system requirements: Linux, macOS, or Windows WSL2 (choose one of three).

Core features

Hermes features:

  • 🧠 Persistent memory— Cross-session three-tier memory + Honcho user modeling, understands you better the more you use it
  • ⚡ Skill system— Automatically create/improve Skills,/learnLearn commands with one click from the documentation
  • 🔌 Rich tools— 70+ built-in tools: file system, web browsing, code execution, vision, voice
  • 🌐 Multi-platform access— Telegram, Discord, Slack, WhatsApp, Signal, and 15+ platforms
  • 🔒 Privacy first— All data stored locally, no telemetry, no mandatory cloud dependency
  • 🤖 Model-agnostic— Supports 200+ models, switch with a single command
  • ⏰ Scheduled tasks— Built-in Cron scheduling, supports cross-platform message delivery
  • 🔬 Research-ready— Batch trajectory generation, ShareGPT format export, RL training integration

Related resources

Official resources

Resources Links
Official documentation hermes-agent.nousresearch.com/docs
GitHub repository github.com/NousResearch/hermes-agent
Skills Community Hub agentskills.io
Model providers Nous Portal

Learning resources

Existing platforms and popular frameworks:

Core requirements Recommended tools Key advantages
Miaoda, generate applications with one sentence Miaoda Official Website Zero code — generate requirements with one sentence and generate the application
Dazi, desktop-level AI agent Dazi Official Website A desktop-level AI agent for individuals and teams that can see the screen, operate software, and process files
MonkeyCode, an AI application development platform. MonkeyCode Official Website Create tasks directly in the platform, let AI code, and use terminal, file management, and preview in the cloud development environment
Xiao Yunque, the AI video generation feature of Jianying (CapCut) Jianying - Little Skylark ByteDance's self-developed Seedance 2.0 video model + Seedream 5.0 image model, paired with the Doubao large model for copywriting understanding
QoderWorkDesktop-level AI Agent QoderWork You state the requirement, it delivers the result.
Automated triggers and system integration n8n Wide integration coverage, self-hostable, can connect through common internal systems
Deep customization controllable by developers Dify
LangChain 		The former provides a complete open-source solution; the latter is suitable for building complex reasoning chains
Multi-role collaboration and task decomposition AutoGen
CrewAI 		The former emphasizes dynamic collaboration; the latter drives processes with a clear role system
Autonomous task execution Agent AutoGPT An early phenomenal open-source Agent project, emphasizing goal-driven, autonomous task decomposition, and looped execution (Plan → Execute → Reflect)
other extensions