Google Agent Development Kit (ADK) - Python Samples

This repository contains a collection of sample agents built using the Google Agent Development Kit (ADK). Each sample is a self-contained application demonstrating different use cases and integrations.

General Prerequisites

• Python 3.x
• Google Cloud SDK
• Google Agent Development Kit (ADK)

Please refer to the individual agent directories for specific dependencies and configuration steps.

Available Agents

1. GCP Release Notes Agent

• Directory: gcp-releasenotes-agent-app/
• Description: An agent designed to answer questions about Google Cloud release notes. It connects to a MCP Toolbox for Databases service that queries a public BigQuery dataset.
• Features:
  • Demonstrates integration with a BigQuery-backed MCP Toolbox.
  • Includes instructions for deploying the toolbox service to Cloud Run.

For detailed setup and execution instructions, please see the GCP Release Notes Agent README.

2. Shop Search Agent

• Directory: shop-agent-app/
• Description: An agent that acts as a shopping assistant, using a tool to search for products in a catalog. It connects to a separate MCP server backed by Vertex AI Search for Retail.
• Features:
  • Illustrates how to connect an agent to a custom MCP server.
  • Provides a clear example of a retail or e-commerce use case.

For detailed setup and execution instructions, please see the Shop Search Agent README.

3. Agentic RAG

This section includes agents that implement the Retrieval-Augmented Generation (RAG) pattern using different Google Cloud database services for vector search.

QnA Agent with AlloyDB

• Directory: RAG/rag-with-alloydb/
• Description: An agent that implements the RAG pattern using AlloyDB for PostgreSQL for vector search.
• Features:
  • Demonstrates using AlloyDB as a vector store for RAG.
  • Includes data ingestion scripts for populating the vector database.
  • Provides instructions for local execution and deployment to Vertex AI Agent Engine.

For detailed setup and execution instructions, please see the RAG with AlloyDB Agent README.

QnA Agent with BigQuery

• Directory: RAG/rag-with-bigquery/
• Description: An agent that implements the RAG pattern using BigQuery for vector search.
• Features:
  • Demonstrates using BigQuery as a vector store for RAG.
  • Includes data ingestion scripts.
  • Provides instructions for local execution and deployment to Vertex AI Agent Engine.

For detailed setup and execution instructions, please see the RAG with BigQuery Agent README.

QnA Agent with Spanner

• Directory: RAG/rag-with-spanner/
• Description: An agent that implements the RAG pattern using Google Cloud Spanner for vector search.
• Features:
  • Demonstrates using Spanner as a vector store for RAG.
  • Includes data ingestion scripts.
  • Provides instructions for local execution and deployment to Vertex AI Agent Engine.

For detailed setup and execution instructions, please see the RAG with Spanner Agent README.