Hermes Agent

Overview

Hermes Agent is an open-source AI agent built by Nous Research that features a built-in self-improvement learning loop. Unlike static AI agents, Hermes Agent creates skills from experience, improves during use, and builds a model of users across sessions. With over 213,000 GitHub stars, it has rapidly become one of the most popular agent projects, representing a major shift toward adaptive, learning-capable AI agents.

Hermes Agent is designed to be platform-agnostic and model-flexible, supporting deployment from a $5 VPS to GPU clusters. It runs across multiple messaging platforms including Telegram, Discord, Slack, and WhatsApp, making it accessible wherever your team already communicates. The project emphasizes keeping the core small and readable while providing powerful automation capabilities.

Built by Nous Research, the team behind the Hermes model family, this agent leverages their deep expertise in open-source AI to deliver a self-improving agent that gets better the more you use it.

Features

• Self-Improving Learning Loop: Creates skills from experience and improves during use
• Cross-Session Memory: Builds a model of users across sessions for personalized interactions
• Multi-Platform Support: Telegram, Discord, Slack, WhatsApp, and more
• Model Flexibility: Use any model - Nous Portal, OpenRouter, OpenAI, or local LLMs
• Terminal Interface: Full terminal with multiline editing and slash-command autocomplete
• Deployment Flexibility: From $5 VPS to GPU clusters
• Skill System: Automatically generates and stores skills based on interactions
• Memory Persistence: Long-term memory across sessions and conversations
• Tool Integration: Support for MCP servers, web search, file operations, and shell commands

Installation

Quick Install (Linux/macOS)

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

Quick Install (Windows PowerShell)

iwr https://hermes-agent.nousresearch.com/install.ps1 -UseBasicParsing | iex

Manual Installation

# Clone the repository
git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent

# Install dependencies
npm install

# Or use Docker
docker pull nousresearch/hermes-agent
docker run -it nousresearch/hermes-agent

Quick Start

Basic Terminal Usage

# Start the agent
hermes-agent start

# Configure your preferred model
hermes-agent configure --model openai/gpt-4

# Start a conversation
hermes-agent chat

Chat Platform Integration

# Telegram setup
telegram:
  bot_token: "YOUR_BOT_TOKEN"
  enabled: true

# Discord setup
discord:
  bot_token: "YOUR_BOT_TOKEN"
  channel_id: "YOUR_CHANNEL_ID"
  enabled: true

Core Concepts

Learning Loop

Hermes Agent operates on a closed learning loop:

1. Execute: Perform tasks using available tools and skills
2. Observe: Analyze the results and user feedback
3. Reflect: Identify what worked well and what could be improved
4. Learn: Create or update skills based on successful patterns
5. Persist: Store learned skills for future use

Skill Generation

When the agent encounters a new type of task, it can automatically generate a skill:

User: "Analyze this sales data and create a report"
Agent: Executes task, identifies patterns
Agent: Automatically creates 'sales-reporting' skill for future use
User: "Create a report for last quarter"
Agent: Uses the previously learned skill

Memory System

The agent maintains multiple memory layers:

• Session Memory: Short-term context for the current conversation
• Skill Memory: Learned skills and procedures
• User Model: Preferences and patterns learned from interactions
• Long-term Memory: Persistent knowledge across sessions

Advanced Features

Custom Model Configuration

providers:
  - name: nous-portal
    base_url: "https://nousportal.ai/api"
    api_key: "${NOUS_API_KEY}"
    model: "nous-hermes-2"

  - name: openrouter
    base_url: "https://openrouter.ai/api"
    api_key: "${OPENROUTER_KEY}"
    model: "anthropic/claude-3.5-sonnet"

  - name: openai
    base_url: "https://api.openai.com/v1"
    api_key: "${OPENAI_KEY}"
    model: "gpt-4"

Skill Management

# List all learned skills
hermes-agent skills list

# View a specific skill
hermes-agent skills view sales-reporting

# Manually create a skill
hermes-agent skills create --name "data-analysis" --prompt "Analyze data and create charts"

# Export skills
hermes-agent skills export --output skills.json

Multi-Agent Setup

agents:
  - name: "research-agent"
    role: "Research and data gathering"
    memory: "research"
    
  - name: "coding-agent"
    role: "Code generation and testing"
    memory: "development"
    
  - name: "assistant-agent"
    role: "General assistant"
    memory: "general"

Examples

Personal Assistant

User: "Set up my daily briefing"
Agent: Creates a morning briefing skill that gathers news, calendar, and tasks
User: "Brief me" (next morning)
Agent: Delivers personalized daily briefing using learned skill

Code Review Automation

User: "Review this pull request"
Agent: Analyzes PR, learns your team's coding standards
User: "Review PR #456"
Agent: Uses learned review patterns for faster, more accurate reviews

Business Intelligence

User: "Monitor competitor pricing"
Agent: Creates automated monitoring skill
User: "How are competitors doing this month?"
Agent: Provides analysis using accumulated data and learned patterns

Use Cases

• Personal AI Assistant: 24/7 intelligent assistance across messaging platforms
• Development Team Support: Automated code review, documentation, and testing
• Customer Service: Intelligent chatbots that learn from interactions
• Research Assistant: Automated literature review and analysis
• Business Automation: Workflow automation that improves over time
• Education: Personalized learning assistants that adapt to student needs

Pros

• ✅ Self-improving agent that gets better with use
• ✅ Cross-platform messaging integration
• ✅ Flexible model support (Nous, OpenAI, OpenRouter, local)
• ✅ Persistent memory across sessions
• ✅ Deployable from $5 VPS to GPU clusters
• ✅ Active development by Nous Research
• ✅ Massive community with 213,000+ stars

Cons

• ✅ Requires API keys for cloud models
• ❌ Learning loop may need monitoring for sensitive environments
• ❌ Higher resource usage than static agents
• ❌ Configuration complexity for advanced features
• ❌ Skill quality depends on training interactions

When to Use

Hermes Agent is ideal for:

• Teams needing adaptive automation: When tasks evolve and require learning
• Multi-platform communication: When you need agents across Slack, Discord, Telegram
• Personal productivity: Building a personal AI assistant that learns your preferences
• Long-running automation: Tasks that benefit from accumulated knowledge
• Model flexibility: When you want to switch between different LLM providers

Resources

• GitHub Repository
• Official Documentation
• Nous Portal
• Hermes Models