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README.md

layout default
title Hermes Agent Tutorial
nav_order 42
has_children true
format_version v2
source_repo https://github.com/nousresearch/hermes-agent
categories
ai-agents
personal-ai
multi-platform
rl-training
related_tutorials
openclaw-tutorial
mem0-tutorial
taskade-tutorial
agno-tutorial
last_updated 2026-04-12

Hermes Agent Tutorial

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:

  1. Continuity — sessions persist, memories accumulate, skills compound. The agent you run today is smarter than the one you ran last week.
  2. 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.
  3. 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)

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

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)     │                       │
└──────────────┴──────────────────┴───────────────────────┘

Chapters in This Tutorial

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.