This repository contains examples for learning Google's Agent Development Kit (ADK), a powerful framework for building LLM-powered agents.

You only need to create one virtual environment for all examples in this course. Follow these steps to set it up:

# Create virtual environment in the root directory
python -m venv .venv

# Activate (each new terminal)
# macOS/Linux:
source .venv/bin/activate
# Windows CMD:
.venv\Scripts\activate.bat
# Windows PowerShell:
.venv\Scripts\Activate.ps1

# Install dependencies
pip install -r requirements.txt

Once set up, this single environment will work for all examples in the repository.

1. Create an account in Google Cloud https://cloud.google.com/?hl=en
2. Create a new project
3. Go to https://aistudio.google.com/apikey
4. Create an API key
5. Assign key to the project
6. Connect to a billing account

Each example folder contains a .env.example file. For each project you want to run:

1. Navigate to the example folder
2. Rename .env.example to .env
3. Open the .env file and replace the placeholder with your API key:

   GOOGLE_API_KEY=your_api_key_here
   

You'll need to repeat this for each example project you want to run.

Here's what you can learn from each example folder:

Introduction to the simplest form of ADK agents. Learn how to create a basic agent that can respond to user queries.

Learn how to enhance agents with tools that allow them to perform actions beyond just generating text.

Example of using LiteLLM to abstract away LLM provider details and easily switch between different models.

Learn how to use Pydantic models with output_schema to ensure consistent, structured responses from your agents.

Understand how to maintain state and memory across multiple interactions using sessions.

Learn techniques for storing agent data persistently across sessions and application restarts.

See how to orchestrate multiple specialized agents working together to solve complex tasks.

Build agents that maintain and update state throughout complex multi-turn conversations.

Implement event callbacks to monitor and respond to agent behaviors in real-time.

Create pipeline workflows where agents operate in a defined sequence to process information.

Leverage concurrent operations with parallel agents for improved efficiency and performance.

Build sophisticated agents that can iteratively refine their outputs through feedback loops.

For more detailed information, check out the official ADK documentation:

• https://google.github.io/adk-docs/get-started/quickstart

Need help or run into issues? Join our free AI Developer Accelerator community on Skool:

• AI Developer Accelerator Community

In the community you'll find:

• Weekly coaching and support calls
• Early access to code from YouTube projects
• A network of AI developers of all skill levels ready to help
• Behind-the-scenes looks at how these apps are built