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Hermes Agent Tutorial Series: Self-Improving AI Agent with Learning Loop

Hermes Agent is a self-improving AI agent built by Nous Research. It creates skills from experience, improves them during use, maintains persistent memory, and runs on Telegram, Discord, CLI, or cloud infrastructure. This series covers installation, memory system, skills, messaging gateways, multi-model configuration, and advanced integrations.

Series Preface

If you’re looking for an AI agent that learns from experience, remembers across sessions, and runs anywhere—not just on your laptop—Hermes Agent might change how you think about AI assistants.

Hermes is built by Nous Research with a unique architecture: it’s the only agent with a built-in learning loop. It creates skills from complex tasks, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions.

Why Hermes Agent?

Most AI assistants share the same limitation: they forget everything when the session ends. Claude Code, Cursor, and other desktop tools are powerful but ephemeral—each conversation starts fresh.

Hermes Agent is built on a different idea: an agent that evolves with you.

flowchart TD
    A[User interacts with Hermes] --> B[Agent stores experience]
    B --> C[Memory nudges trigger]
    C --> D[Knowledge persisted]
    D --> E[Skills auto-created]
    E --> F[Skills self-improve]
    F --> A

    style B fill:#e1f5ff
    style D fill:#e8f5e9
    style E fill:#fff3e0
    style F fill:#f3e5f5

Key Differentiators

Feature Hermes Agent Claude Code Cursor gstack
Persistent Memory ✅ Honcho dialectic ❌ Session-only ❌ Session-only ❌ Session-only
Cross-Platform Messaging ✅ Telegram/Discord/Slack/Signal ❌ Desktop only ❌ Desktop only ❌ Desktop only
Auto Skill Creation ✅ From experience ❌ Manual ❌ Manual ✅ Skills framework
Skill Self-Improvement ✅ During use ❌ Static ❌ Static ❌ Static
Model Flexibility ✅ 200+ via OpenRouter ✅ Claude only ✅ Multiple ✅ Claude
Serverless Deployment ✅ Modal/Daytona ❌ Laptop only ❌ Laptop only ❌ Laptop only
Session Search ✅ FTS5 + LLM summary ❌ No history ❌ No history ❌ No history

Overview of the 9 Tutorials

This series contains 9 detailed tutorials, from quick start to advanced integrations:

Quick Win (Article 1)

  1. First 5 Minutes with Hermes Agent — Install, configure, and get your first response in 5 minutes

Differentiators (Articles 2-3)

  1. Memory System: Persistent Memory and User Modeling — Honcho dialectic, memory nudges, cross-session recall
  2. Skills System: Creating and Managing Skills — Skills Hub, skill creation workflow, self-improvement

→ G1 Graduation: Basic Competency — Agent runs, remembers, has skills

Deployment Options (Articles 4-6)

  1. Messaging Gateway: Multi-Platform Integration — Telegram, Discord, Slack, WhatsApp, Signal setup
  2. CLI and TUI Complete Guide — Terminal interface, keybindings, slash commands
  3. Multi-Model Configuration and Switching — Provider setup, model switching, cost optimization

→ G2 Graduation: Operator — Agent deployed with multi-model support

Advanced Topics (Articles 7-9)

  1. Terminal Backends: Local and Cloud Deployment — Local, Docker, SSH, Daytona, Modal backends
  2. Cron Scheduling: Automation and Reports — Built-in cron, natural language scheduling
  3. MCP Integration: Extending Hermes Capabilities — MCP servers, custom integrations

→ G3 Graduation: Master — Full system integration complete

Who is this series for?

  • ✅ New to AI agents — Build good habits with a learning-capable assistant
  • ✅ Existing Claude Code users — Extend capabilities beyond desktop
  • ✅ Bot developers — Deploy AI to Telegram/Discord/Slack communities
  • ✅ Teams — Shared agent with persistent memory across members
  • ✅ Researchers — Trajectory generation, RL environments for agent training

Prerequisites

  • Basic terminal/CLI knowledge
  • One platform for messaging (optional: Telegram, Discord, etc.)
  • Basic understanding of LLM concepts (optional)

How to use this series

  1. Start with Article 1 — Get Hermes running in 5 minutes
  2. Explore differentiators — Memory (Article 2) and Skills (Article 3) are Hermes’s unique value
  3. Choose deployment path — Messaging (Article 4) or CLI (Article 5) based on your use case
  4. Go advanced — Backends, Cron, MCP as needed

What you’ll achieve

By completing this series, you’ll have:

  • A Hermes agent that remembers your preferences across sessions
  • Skills that improve as you use them
  • Deployment on Telegram, Discord, CLI, or cloud infrastructure
  • Multi-model routing for cost optimization
  • Automated tasks via cron scheduling
  • Extended capabilities via MCP integration

Series navigation:

Repository: github.com/NousResearch/hermes-agent

Documentation: hermes-agent.nousresearch.com/docs

Community: Discord