Get in Touch

Course Outline

Introduction to Hermes Agent

  • Understanding Hermes Agent and its distinction from IDE copilots
  • The concept of self-improving agents and closed learning loops
  • Architecture overview: backends, platforms, and tools

Installation and Setup

  • Installing Hermes Agent locally
  • Deploying within Docker containers
  • Remote deployment via SSH, Daytona, Singularity, and Modal
  • Configuring API keys for OpenAI, Anthropic, OpenRouter, and Nous Portal

Interacting with the Agent

  • CLI interface and essential commands
  • Setting up and using the Telegram bot
  • Integrating with Discord and Slack
  • Establishing WhatsApp connectivity

Built-in Tools

  • Web search and content extraction
  • File operations: reading, writing, editing, and searching
  • Executing terminal commands and bash scripting
  • Image generation and vision analysis
  • Text-to-speech functionality

Persistent Memory

  • Cross-session memory using FTS5 recall
  • LLM summarization for maintaining long-term context
  • Memory search and retrieval techniques

The Skills System

  • Defining skills and the creation process
  • Maintaining skill persistence across sessions
  • Accessing community skills and agentskills.io

MCP Integration

  • Connecting to MCP servers
  • Programmatically extending tool capabilities

Scheduled Automations

  • Utilizing the built-in cron scheduler
  • Configuring recurring tasks and reports
  • Delivering automation results across platforms

Developer Automation Use Cases

  • Autonomously executing terminal commands
  • Spawning isolated subagents
  • Managing parallel workstreams and batch processing

Security and Best Practices

  • Implementing approval modes for commands and edits
  • Ensuring data privacy on self-hosted infrastructure
  • Maintaining environment isolation

Production Deployment

  • Running Hermes Agent on a $5 VPS
  • Adopting serverless deployment patterns
  • Monitoring agent health and logs

Troubleshooting

  • Resolving common installation issues
  • Debugging tool failures
  • Tuning memory and performance

Summary and Next Steps

  • Recap of key capabilities
  • Resources for continued learning
  • Transitioning to advanced Hermes topics

Requirements

  • Basic familiarity with command-line terminals and Linux commands
  • Understanding of software development workflows
  • General knowledge of AI and large language models

Audience

  • Software developers seeking to integrate AI agents into their workflow
  • DevOps engineers exploring autonomous tooling
  • Technical team leads evaluating AI agent platforms
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories