
Learn to build ai agents with Google's ADK, starting from basics and progressing to memory, tools, web search, and deployment in the cloud.
Explore why AI agents matter and how Google's ADK enables developers to build memory-enabled, multi-step agents that plan, act, use real tools and APIs, and deploy to Cloud Run.
Explore how ADK uses agents, tools, and workflows to coordinate tasks, with agents as brains, tools as hands, and workflows as the plan, including sequential, parallel, and smart decision types.
Set up your environment by creating a Google Cloud project, generating an ADK-course API key via Google AI Studio, and installing Python and VS Code to run Google ADK locally.
Create your first ai agent HelloAgent with adk, configure Gemini 2.5 flash via Google AI, and run agent.py to chat through the web interface.
Create custom tools for an agent with the ADK, building a calculator tool described by a docstring, test it, and see the agent use it to solve math prompts.
Learn to empower AI agents with built-in tools in Google ADK, including Gemini model integration, using Google search to perform live internet queries and run a search-enabled agent.
Build a weather agent that fetches live data from the internet using the requests library. Connect a weather tool to your ADK agent to query cities like New York.
Learn how state and sessions enable memory in adk, using implicit state and automatic memory to remember conversation history within a temporary session.
Explore how short-term memory works in ADK through a context window that feeds the conversation to Gemini, enabling the agent to remember names and follow-ups within a session.
Save your AI agent's memory for long-term use by enabling the adk run --save-session flag. Resume from a session file like user1.session.json to recall previous conversations.
Move memory from a local JSON file to the cloud with Vertex AI Memory Bank Service, ensuring persistent, scalable memory and smart retrieval for many users.
learn to use Vertex AI memory bank service with Google ADK to build agents in AI Studio, following system instructions and plain Python functions for reliable, copy-paste ready code.
Learn to build and adapt a complex agent with Vertex AI memory bank for long-term memory, run via ADK web, using set user name and memory search tools.
Learn to implement the set username tool in a Vertex AI powered agent, storing user names in session memory to personalize learning and track progress across sessions.
Learn to implement long-term memory for a tutoring agent with Vertex AI memory bank, using set user name and asynchronous memory tools to personalize learning across sessions.
Publish your Python tutor agent to Google Cloud Run using the ADK deploy Cloud Run command, supplying project id, region, and service name, then share a link to run it.
Build Practical AI Agents with Google ADK, Gemini & Python
Chatbots can answer questions. AI agents can do much more.
They can use tools, access information, maintain context, remember previous interactions, coordinate specialized agents, and complete multi-step tasks.
In this hands-on course, you’ll learn how to build practical AI agents using Google’s Agent Development Kit (ADK), Gemini, and Python—starting with a simple agent and progressively building toward a multi-agent application ready for deployment on Google Cloud Run.
You don’t need previous experience with AI agents or agent frameworks. If you know basic Python and want a structured, practical introduction to Google ADK, this course takes you from your first working agent to tools, sessions, state, memory, multi-agent workflows, and cloud deployment.
What You’ll Learn
By the end of the course, you’ll be able to:
Build AI agents with Google ADK and Gemini
Create and connect custom tools
Use built-in tools and give agents access to external capabilities
Manage sessions, state, and context
Implement short-term memory and persistent long-term memory
Design multi-agent systems with specialized agents
Coordinate agents to complete multi-step workflows
Understand the architecture and core concepts behind Google ADK
Prepare and deploy AI agent applications to Google Cloud Run
Adapt the course concepts to your own AI-agent projects
What You’ll Build
You won’t just watch demonstrations—you’ll progressively build a working AI-agent system.
You’ll start with a simple “Hello Agent” and gradually add new capabilities:
Basic Agent → Tool-Enabled Agent → Information Access → Sessions & State → Memory → Multi-Agent Workflow → Cloud Deployment
Each project builds on the previous one, so you can see why each capability is needed and how the architecture evolves as the agent becomes more powerful.
Who Is This Course For?
This course is designed for:
Python developers who want to build AI agents
AI and machine-learning enthusiasts
AI engineers and developers
Cloud developers
Students learning about agentic AI
Beginners who want a practical introduction to Google ADK
Developers interested in building applications with Gemini and Google Cloud
You do not need previous experience with Google ADK or another AI-agent framework.
Requirements
Before starting, you should:
Understand basic Python syntax
Be comfortable writing functions and working with Python packages
Know how to install Python libraries and run Python programs
No previous AI-agent experience is required.
You’ll also need access to the relevant Google AI and/or Google Cloud services used throughout the course. Any applicable account, billing, quota, or service requirements will be explained clearly before you use them.
Why Learn Google ADK?
Understanding an agent framework is more valuable than memorizing isolated code snippets.
Throughout the course, you’ll learn not only how to build agents with Google ADK, but also how the main components fit together—tools, context, state, memory, sessions, and agent orchestration.
This gives you a foundation you can use to design and adapt AI-agent applications for your own projects.
Start Building AI Agents
If you want to move beyond simple chatbot applications and learn how modern AI agents are built, this course gives you a practical path from your first Gemini agent to a deployable multi-agent application.
Preview the introduction and a selected implementation lesson to see the teaching style, coding walkthroughs, and projects you’ll build throughout the course.
Start building AI agents instead of only watching them.