Hermes Agent Tutorial

June 22, 2026 · View on GitHub

NousResearch's self-hosted personal AI agent with persistent memory, autonomous skill creation, 20+ platform gateway, and a closed reinforcement-learning loop that turns every conversation into fine-tuning data.

What Is Hermes Agent?

Hermes Agent is the successor to OpenClaw — NousResearch's production-grade, self-hosted personal AI agent designed to run 24/7 on your own hardware or cloud infrastructure. With 65,972 GitHub stars and an MIT license, it represents the current state of the art in open-source agent frameworks that combine a richly layered memory system, a multi-platform messaging gateway, and a reinforcement-learning pipeline that continuously improves the underlying models through real usage.

Unlike ephemeral chatbot wrappers, Hermes is built around three design principles:

Continuity — sessions persist, memories accumulate, skills compound. The agent you run today is smarter than the one you ran last week.
Reach — one agent, 20+ platforms. Whether you message through Telegram, Discord, Slack, WhatsApp, Signal, Email, Matrix, Feishu, DingTalk, or a raw webhook, the same memory and skill set is available.
Closed learning — every real interaction is a potential training example. trajectory.py records tool calls and outcomes in Atropos RL format; those trajectories can be fed directly into NousResearch's fine-tuning pipeline to improve future model behavior.

Current Snapshot (auto-updated)

repository: nousresearch/hermes-agent
stars: about 200k
GitHub release reference: v2026.6.19 (checked 2026-06-22; release metadata on GitHub)

Who Should Read This Tutorial

Audience	What You Will Get
Individual developers	A self-hosted AI assistant with memory that actually persists across sessions
Platform builders	A messaging gateway you can point at any of 20+ chat platforms with a single config
ML researchers	A live data-generation pipeline producing Atropos-format RL trajectories from real agent interactions
DevOps / infra engineers	Six swappable terminal backends (local, Docker, SSH, Daytona, Singularity, Modal) for isolated task execution
OpenClaw users	A clear migration path: `hermes claw migrate` imports your memories, skills, and config

Why This Track Matters

Hermes Agent is a useful study target because it combines several hard agent problems in one repo: memory, platform routing, scheduling, tool execution, and training-data capture. That makes it a strong map for readers who want to move from a local assistant to an always-on agent system.

What You Will Learn

By the end of this tutorial, you will understand how Hermes structures persistent memory, how the messaging gateway routes conversations across platforms, how scheduled jobs and subagents run, and how interaction traces become reinforcement-learning data.

Mental Model

Think of Hermes as a personal AI operating system. The agent core decides what to do, the memory layers preserve context, the gateway lets users reach it from many channels, and the training loop converts real usage into improvement data.

Architecture at a Glance

cli.py
└── hermes_cli/
    ├── agent/               # LLM core
    │   ├── prompt_builder.py
    │   ├── context_engine.py
    │   ├── memory_manager.py
    │   ├── skill_utils.py
    │   ├── trajectory.py
    │   └── smart_routing.py
    ├── gateway/             # 20+ platform messaging
    │   ├── telegram.py
    │   ├── discord.py
    │   ├── slack.py
    │   ├── whatsapp.py
    │   ├── signal.py
    │   ├── email.py
    │   ├── matrix.py
    │   ├── api_server.py
    │   └── ...
    ├── cron/                # Scheduler + jobs
    │   ├── scheduler.py
    │   └── jobs/
    ├── environments/        # RL training, benchmarks, subagents
    │   ├── hermes_swe_env/
    │   ├── tblite/
    │   └── batch_runner.py
    └── acp_adapter/         # Agent Communication Protocol server

Three Memory Layers

┌─────────────────────────────────────────────────────────┐
│                    Memory Architecture                   │
├──────────────┬──────────────────┬───────────────────────┤
│   Episodic   │    Semantic      │     Procedural        │
│              │                  │                       │
│ FTS5 SQLite  │  MEMORY.md       │  SKILL.md files       │
│ session      │  USER.md         │  (auto-created and    │
│ search +     │  Honcho user     │   self-improved by    │
│ LLM summary  │  modeling        │   the agent)          │
│ injection    │  (dialectic)     │                       │
└──────────────┴──────────────────┴───────────────────────┘

Chapter Guide

Chapter	Title	Key Topics
1	Getting Started	Install, `hermes setup`, `~/.hermes/` layout, first conversation, OpenClaw migration
2	The TUI and Conversation Interface	curses UI, slash commands, SOUL.md persona, context files, skin system
3	Agent Core: Prompt Building, Context Engine, Model Routing	prompt_builder.py, context_engine.py, smart_model_routing.py, credential_pool.py
4	Memory, Skills, and the Learning Loop	Three memory layers, memory_manager.py, FTS5, Honcho, SKILL.md, agentskills.io
5	The Messaging Gateway	20+ platform drivers, session routing, delivery pipeline, API server mode
6	Cron Scheduling, Subagents, and Automation	scheduler.py, cron commands, subagent spawning, terminal backends
7	RL Training and Trajectory Generation	trajectory.py, Atropos, benchmark envs, tool-call parsers, data pipeline
8	ACP, MCP, Migration, and Ecosystem	ACP server, MCP integration, agentskills.io, OpenClaw migration, Nix/Docker deploy

Quick-Start (TL;DR)

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

# Run setup wizard
hermes setup

# Start the TUI
hermes

Key Differentiators vs Other Agent Frameworks

Feature	Hermes Agent	LangChain	AutoGPT	CrewAI
Persistent episodic memory (FTS5)	Yes	Plugin-dependent	Partial	No
Autonomous skill creation	Yes	No	No	No
20+ platform gateway	Yes	No	No	No
RL trajectory generation	Yes	No	No	No
Closed fine-tuning loop	Yes	No	No	No
Self-hosted, MIT license	Yes	Yes	AGPL	MIT
Six terminal backends	Yes	No	No	No
ACP multi-agent protocol	Yes	No	No	No

License and Attribution

Hermes Agent is released under the MIT License by NousResearch. This tutorial is an independent educational resource; it is not officially affiliated with NousResearch.

Source References

nousresearch/hermes-agent