Getting started with Google Agent Development Kit (ADK)

Download PyCharm for Windows, install it, and complete authentication to launch. Import IntelliJ settings or skip, then create a new Python project and run the main file.

Create your first agent with the Google ADK by setting up a PyCharm project, installing requirements, and testing the time agent via the ADK UI using a Google API key.

Explore tool calling by building an order tracking agent with the track order tool, passing an order ID, and retrieving status and the expected date for order updates.

Demonstrate multi-tool calling in the Google Agent Development Kit by adding track order and cancel order functions. Validate order status before cancellation and process refunds.

Introduces the MCP architecture, an open standard that lets ai models securely talk to external tools and services. Explains how the host, MCP client, transport, and servers work together.

This lecture demonstrates connecting an agent to a MySQL MCP server by setting up Ubuntu, creating a demo database, and using ADK web to query employees by department and salary.

Build a university information database using sqlite with an MCP server, create tables, seed data with faker, and implement an interactive chat agent without the adk web UI.

Streamlit provides a shareable web interface for Google ADK agents, wrapping the agent in a Python-based frontend beyond the terminal and ADK web UI.

Discover how Streamlit gives instant UI from Python, built-in charts, reactive execution, session state, and one-click deployment for ADK agents.

Explore practical Streamlit quickstart examples, from a hello world app to user input, simple calculator, data frame filtering, and interactive charts.

Convert the previous MCP server into a Streamlit-based sqlite agent within a Streamlit app; explore page config, markdown, sidebar, prompts, session state, and caching.

Develop a rag application using redis vector store with cosine similarity, upload PDF documents via a stream data application, generate embeddings with a sentence transformer, and perform semantic search.

Develop a rag application using redis vector store, adding search and list document tools, and run with a streamlit UI and ADK agent runner to index and query uploaded PDFs.

Explore the A2A protocol in the Google ADK, enabling networked agent-to-agent communication via http json rpc, and learn when to use or avoid it.

Create a2a server and client, expose a remote greeting agent via the a2a protocol, and test with http requests using uvicorn to access greeting, weather, and time tools.

deploy a2a agent on cloud run by building and pushing a docker image, then configure cloud run with app url and api key for library book search and calendar events.

Create and test a cloud run a2a agent with a client using Google ADK, configure calendar and library agents, run ADK web, and troubleshoot prompts and billing session limits.

Explore how the Google Agent Development Kit (ADK) handles memory across short-term, persistent, and long-term memory with Vertex AI Memory, and distinguishes sessions from memory for continuity, personalization, and scalability.

Use the In-Memory Session Service for local development with a simple, zero-configuration setup that stores session data in ram. Avoid persistence; ideal for tests and short-lived stateless tasks.

Develop a persistent memory system with Google ADK and SQLite, recalling and saving user preferences by id across restarts via a travel agent app.

Explore Vertex AI Session Service as a cloud managed, memory-integrated solution that scales automatically, preserves cross-session recall, and enables production-grade enterprise security and compliance with minimal code changes.

Build a sequential email processing pipeline with an email classifier, sentiment analyzer, response drafter, and quality checker using Google ADK.

Create a loop agent example project with Google ADK, featuring a product writer agent and QA feedback with QA auditor checks and an exit loop controlling four iterations.

Explore a parallel workflow with the Google ADK by running flight, hotel, and attraction researchers in parallel, then synthesize a final trip plan in a sequential pipeline.