Agentic AI with Google Cloud - This workshop introduces the concept of multi-agent systems and their practical application using LangGraph. You'll learn to build individual agents with tools for database interaction and internet searching via the Gemini API, and then orchestrate them in various configurations, including collaborative setups. The workshop covers utilizing multiple LLMs for diverse roles, generating dynamic content, and deploying a complete multi-agent system on Google Cloud, providing a comprehensive understanding of agent development and deployment.

Build a contextual Yoga Poses recommender app - This codelab guides you through building a comprehensive Yoga app using Google Cloud. You'll create a scalable pose database in Firestore and enhance search with the Vector Search Extension for contextual matching. You'll explore using Gemini 2.0 Flash to generate pose descriptions and multimedia content. The lab also covers grounding information by integrating Google Search. You'll learn to create Firestore collections, CRUD applications, generate embeddings, and perform similarity searches.

• Java Codelab
• Python Codelab
• Node.js Codelab

Here's what you can expect to learn – think of it as leveling up your agent game:

Building Your First Agent with LangGraph: We'll get our hands dirty building your very own agent using LangGraph, a popular framework. You'll learn how to create tools that connect to databases, tap into the latest Gemini 2 API for some internet searching, and optimize the prompts and response, so your agent can interact with not only LLMs but existing services. We'll also show you how function calling works.

Agent Orchestration, Your Way: We'll explore different ways to orchestrate your agents, from simple straight paths to more complex multi-path scenarios. Think of it as directing the flow of your agent team.

Multi-Agent Systems: You'll discover how to set up a system where your agents can collaborate, and get things done together – all thanks to an event-driven architecture.

LLM Freedom: Use the Best for the Job: We're not stuck on just one LLM! You'll see how to use multiple LLMs, assigning them different roles to boost problem-solving power using cool "thinking models."

Dynamic Content? No Problem!: Imagine your agent creating dynamic content that's tailored specifically for each user, in real-time. We'll show you how to do it!

Taking it to the Cloud with Google Cloud: Forget just playing around in a notebook. We'll show you how to architect and deploy your multi-agent system on Google Cloud so it's ready for the real world!

This project will be a good example of how to use all the techniques we talked about.