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. Restaurant Finder Agent

• Directory: restaurant-finder/
• Description: A conversational AI agent that helps users find restaurants based on a specific dish or ingredient, leveraging the Google Maps Platform API.
• Features:
  • Demonstrates how an agent can use external tools (Google Maps API) to answer user queries.
  • Provides real-time restaurant information.

For detailed setup and execution instructions, please see the Restaurant Finder Agent README.

4. Shopper's Concierge Agent

• Directory: shopper-concierge-demo/
• Description: An advanced shopping assistant that uses a sub-agent for research to provide more relevant product recommendations.
• Features:
  • Showcases a multi-agent architecture where a primary agent delegates tasks to a specialized sub-agent.
  • Includes a Gradio web interface for a complete user experience.

For detailed setup and execution instructions, please see the Shopper's Concierge Agent README.

5. 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.

More RAG Agents

This repository contains additional RAG agent implementations that demonstrate integration with various Google Cloud services. Below is a list of other available RAG agents:

• RAG Engine with Managed DB:

  • Directory: RAG/rag-engine-with-managed-db/
  • Description: An agent that leverages the managed Vertex AI RAG Engine with its own fully managed database, eliminating the need to manage a separate Vector Search index.
  • README: RAG Engine with Managed DB README

• RAG Engine with Vector Search:

  • Directory: RAG/rag-engine-with-vectorsearch/
  • Description: An agent that uses the managed Vertex AI RAG Engine with a Vertex AI Vector Search index as its backend for efficient, scalable document retrieval.
  • README: RAG Engine with Vector Search README

• RAG with Vector Search and Datastore:

  • Directory: RAG/rag-with-vectorsearch-ds/
  • Description: An agent that uses Vertex AI Vector Search as the vector store and Firestore in Datastore mode as the document store.
  • README: RAG with Vector Search and Datastore README

• RAG with Vector Search and GCS:

  • Directory: RAG/rag-with-vectorsearch-gcs/
  • Description: An agent that uses Vertex AI Vector Search as the vector store and Google Cloud Storage (GCS) as the document store.
  • README: RAG with Vector Search and GCS README

References

• ADK Official Docs
  • ADK Python Repository
• ADK Samples Repo
• Agentic Design Patterns
• ADK Web Book by Amulya Bhatia