Example Agents

This repository provides a set of sample agents using Google GenAI SDK and ADK. It demonstrates how to create, run, and deploy intelligent AI agents.

Structure

All agent projects are located in the agents/ directory:

• bq_custom_tools_agent/ - Example BigQuery assistant using manual custom Python tools
• bq_adk_tools_agent/ - Complex BigQuery Data Agent powered by Google native ADK tools
• parent_and_subagents/ - Multi-agent setup with Parent and Sub-agents
• state_agent/ - Agent managing conversation state
• workflow_agents/ - Complex workflow using Sequential, Loop, and Parallel Agents

Setup

Before proceeding, ensure you have the Google Cloud CLI (gcloud) installed and configured with your project.

# Login to Google Cloud
gcloud auth login

# Set your application default credentials (required for local runs)
gcloud auth application-default login

# Set your active project
gcloud config set project YOUR_PROJECT_ID

We use uv for dependency management. To set up the Python project:

make setup

Configure your environment variables by copying .env.example to .env and setting GOOGLE_CLOUD_PROJECT, GOOGLE_CLOUD_LOCATION, and MODEL.

Running Agents Locally

You can run the agents locally using the Google ADK CLI via our provided Makefile which safely suppresses annoying C++ gRPC logging spam.

To run a specific agent in your terminal:

make run-agent AGENT=bq_adk_tools_agent

To run the default agent (workflow_agents):

make run

To run the agents in the web UI mode:

make run-web

Deployment to Vertex AI Agent Engine

Agents are wrapped in reasoning_engines.AdkApp to be seamlessly deployed to Vertex AI Agent Engine.

We provide a streamlined deployment script that automatically generates requirements.txt from the pyproject.toml and copies it (and your .env) into the agent's package before executing the adk deploy command.

To deploy an agent:

./scripts/agent_engine_deployment/deploy.sh bq_adk_tools_agent

Code Quality

Run linting, typechecking, and formatting:

make check