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MinhHoangDono
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google-adk-python

Build AI agent applications with Google ADK Python using tools, multi-agent composition, and workflows.

Updated Mar 7, 2026
Search Tags:#python#multi-agent#google adk#mcp tools#a2a protocol#vertex ai deployment#workflow agents
One-click install
npx skills add https://github.com/MinhHoangDono/antigravity-kit --skill google-adk-python-minhhoangdono
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: google-adk-python
Source: https://github.com/MinhHoangDono/antigravity-kit/tree/main/.agent/skills/google-adk-python
Command: npx skills add https://github.com/MinhHoangDono/antigravity-kit --skill google-adk-python-minhhoangdono

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you build production-ready AI agents in Python using Google’s Agent Development Kit (ADK), so you can integrate tools, workflows, memory/state, and deployments without stitching everything together manually.

Core Features & Use Cases

  • Multi-agent systems & routing: Compose sub-agents and manage dynamic interactions across agents.
  • A2A protocol & remote agents: Connect agents via A2A for agent-to-agent communication.
  • Tool integration including MCP: Expose tools through ADK, including MCP servers as agent tools.
  • Workflow orchestration: Implement sequential, parallel, and loop-style workflow agents for pipelines.
  • State, memory, artifacts, and callbacks: Persist session state, save artifacts, and add callbacks for observability and guardrails.
  • Deployment & evaluation readiness: Support local dev UI/CLI, evaluation runs, and deployment targets like Cloud Run / Vertex AI / GKE.

Quick Start

Ask your AI assistant to scaffold an ADK Python project that defines a root_agent in agent.py, wires in your tools, and adds an interactive CLI run setup for testing.

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 systems with Google ADK in Python?▼

Multi-agent systems with Google ADK are built by composing sub-agents and defining a root_agent in agent.py to manage dynamic interactions and route tasks across the orchestrated pipeline.

How do I integrate MCP tools into an AI agent workflow?▼

MCP tool integration is achieved by exposing MCP servers as agent tools within ADK, enabling your agents to call external functions and execute sequential or parallel workflow pipelines.

Can I deploy ADK agents to Vertex AI?▼

Vertex AI deployment is fully supported alongside local dev UI, CLI testing, and evaluation runs, allowing you to build and test ADK agent applications locally before deploying to cloud targets like Vertex AI.

How does A2A protocol work for remote agent communication?▼

The A2A protocol enables agent-to-agent communication by connecting remote agents, allowing your ADK Python application to distribute tasks and interact with external agent services dynamically.

What's the best way to manage session state and artifacts in AI agents?▼

Session state, memory, and artifacts are managed using ADK's built-in persistence features, allowing you to save session data and attach callback hooks for observability and guardrails during agent execution.

Do I need a specific directory structure for ADK Python projects?▼

An ADK-compatible agents directory is required, containing an agent.py file with a root_agent or App entry point to properly initialize the multi-agent system and wire in your tools.

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