Google ADK Development Environment

What is Google ADK Development Environment?

A development container optimized for building Google Agent Development Kit (ADK) applications using Python 3.13. It bundles VS Code extensions, Google Cloud SDK, and a pre-configured set of Python libraries for cloud, ML, web, and data development.

How to use Google ADK Development Environment?

Open the project folder in VS Code, click “Reopen in Container” when prompted, and wait for the container to build. After setup, run adk-setup to authenticate with Google Cloud, then start developing. Custom aliases like adk-test, adk-format, and adk-lint are available for common tasks.

Key features of Google ADK Development Environment

• Python 3.13 with VS Code extensions (Python, Jupyter, Google Cloud Code)
• Pre-installed Google Cloud SDK and Python client libraries
• Custom aliases for authentication, testing, formatting, and linting
• Automatic code formatting on save with black and ruff
• Port forwarding for common development ports (3000, 5000, 8000, 8080, 9000)
• Jupyter notebook support for interactive development

Use cases of Google ADK Development Environment

• Building and testing Google Agent Development Kit applications
• Developing Python services that use Google Cloud (Storage, BigQuery, AI Platform)
• Machine learning projects with TensorFlow, PyTorch, Transformers, and LangChain
• Web API development with FastAPI or Flask with async support
• Data analysis and visualization using pandas, numpy, matplotlib, and plotly

FAQ from Google ADK Development Environment

What prerequisites are needed to use this container?

You need VS Code with the Dev Containers extension installed. The container builds and installs all dependencies automatically.

How do I authenticate with Google Cloud inside the container?

Copy .env.example to .env, run adk-setup to authenticate, then set your project ID with gcloud config set project YOUR_PROJECT_ID.

What Python packages come pre-installed?

Google Cloud libraries (Storage, BigQuery, AI Platform, Speech, Vision), ML libraries (TensorFlow, PyTorch, Transformers, LangChain), web frameworks (FastAPI, Flask), dev tools (pytest, ruff, black, mypy), and data processing (pandas, numpy, matplotlib, plotly).

How do I run tests or check code quality?

Place tests in a tests/ directory and run adk-test. For linting use adk-lint, for formatting use adk-format, and for a full check (ruff + mypy + pytest) use adk-check.

Where is the project structure defined?

The .devcontainer/ folder contains the devcontainer.json, Dockerfile, and bashrc configuration. A root-level requirements.txt lists Python dependencies and .env.example provides environment variable templates.