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These playbooks provide step-by-step tutorials for building AI agents on AMD AI PCs. Each explains implementation details and architectural decisions.
Built for AI PCs: All agents run locally on Ryzen AI hardware (NPU/iGPU acceleration) for private, fast AI processing.
What makes playbooks different?Unlike reference documentation, playbooks are journey-based tutorials that:
  • Start with a working example in 5 minutes
  • Build complexity incrementally
  • Explain what happens “under the hood”
  • Include production deployment patterns
  • Provide reusable code you can extend

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Available Playbooks

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Hardware Advisor Agent

Build an agent that analyzes your system hardware and recommends which LLMs you can run locally.

Building a Hardware Advisor

15-20 minutesBuild an agent that advises on LLM capabilities based on hardware.
  • Quick start (5 min working example)
  • LemonadeClient SDK integration
  • GPU/NPU detection (Windows & Linux)
  • Memory-based recommendations

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Document Q&A Agent with RAG

Build an agent that searches, indexes, and answers questions about documents. Split into 3 progressive parts for easier learning.

Part 1: Getting Started

15-20 minutesBuild your first agent with RAG capability.
  • Quick start (5 min working example)
  • Understanding components
  • Steps 1-3: Basic → RAG → File Discovery

Part 2: Advanced Features

15-20 minutesAdd monitoring, sessions, and customization.
  • Tool mixins pattern
  • File monitoring & auto-indexing
  • Session persistence
  • Custom tools & specialization
  • Real-world examples

Part 3: Deployment & Optimization

15-20 minutesDeploy and optimize your agent.
  • Agent intelligence & reasoning
  • Advanced patterns
  • Web API & CLI deployment
  • Performance tuning
  • Troubleshooting

Voice Interaction

Integrate Whisper (ASR) and Kokoro (TTS) for voice interaction.

The Flagship GAIA Agent

Conversation, documents, data, web, memory, and skills in one agent.

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Prerequisites

All playbooks assume you have:
  • Python 3.10 or higher
  • uv package manager (recommended)
  • A project folder for your agent code

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Learning Path

Start with the Document Q&A Agent to learn the foundational concepts used throughout the other playbooks.

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Contributing Playbooks

If you’ve built an agent that demonstrates useful patterns, consider contributing a playbook.
Playbook template coming soonWe’re creating a template to make it easy for the community to contribute playbooks. Check the GitHub repo for updates.

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Feedback

Help us improve these playbooks!