google-adk-python

Name: google-adk-python
Availability: InStock
Author: stonelukas

Design, orchestrate, and deploy AI agents with Google ADK Python.

Updated Jun 16, 2025

Search Tags:#workflow #ai #tooling #multi-agent #agent-development #vertex-ai #adk-python

Ready to run

npx skills add https://github.com/Stonelukas/Mouse-on-Numpad --skill google-adk-python-stonelukas

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: google-adk-python
Source: https://github.com/Stonelukas/Mouse-on-Numpad/tree/main/.opencode/skill/google-adk-python
Command: npx skills add https://github.com/Stonelukas/Mouse-on-Numpad --skill google-adk-python-stonelukas

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a code-first toolkit to design, orchestrate, and deploy AI agents using Google's ADK Python, enabling teams to build multi-agent systems with predictable workflows.

Core Features & Use Cases

Tools Ecosystem: pre-built tools, OpenAPI integration, and custom Python functions as tools to extend agent capabilities.
Multi-Agent Architecture: hierarchical coordination with sub-agents and coordinator patterns.
Workflows & Deployment: SequentialAgent, ParallelAgent, and LoopAgent for structured pipelines; deploy to Vertex AI, Cloud Run, or custom infra.
Best Practices & Use Cases: model how to build research assistants, code assistants, or automation pipelines with eval and safety considerations.

Quick Start

Install the google-adk-python package and initialize a simple LlmAgent workflow to start building multi-agent pipelines.

Frequently Asked Questions about google-adk-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build multi-agent workflows with Google ADK Python?▼

Build multi-agent workflows in Google ADK Python by using SequentialAgent, ParallelAgent, and LoopAgent patterns to orchestrate structured pipelines. This toolkit enables hierarchical coordination with sub-agents and coordinator patterns for predictable AI agent execution.

How do I deploy AI agents to Vertex AI or Cloud Run?▼

Deploy AI agents to Vertex AI or Cloud Run using this toolkit's code-first deployment capabilities. It supports scalable deployment across Google Cloud infrastructure, allowing you to move from local multi-agent orchestration directly to production environments.

Can I use custom Python functions as tools for my AI agents?▼

Yes, you can use custom Python functions as tools for your AI agents. The toolkit supports a tools ecosystem that includes pre-built tools, OpenAPI integration, and custom Python functions to extend agent capabilities within your workflows.

Does Google ADK Python support human-in-the-loop approval processes?▼

Yes, Google ADK Python supports human-in-the-loop approval processes. It provides a code-first workflow that integrates tooling and safety considerations, allowing teams to model automation pipelines with manual approval steps before execution.

What is the best way to coordinate multiple sub-agents in an AI pipeline?▼

The best way to coordinate multiple sub-agents in an AI pipeline is using hierarchical coordination patterns. Google ADK Python provides coordinator patterns and structured workflow agents to manage complex multi-agent systems with predictable execution.

When should I use LoopAgent instead of ParallelAgent for workflow orchestration?▼

Use LoopAgent for workflow orchestration when your process requires iterative execution, and ParallelAgent for concurrent task execution. Google ADK Python provides these distinct workflow patterns to structure AI agent pipelines based on your specific execution requirements.