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ck:google-adk-python

Build modular AI agents with Google ADK Python for orchestration and multi-agent coordination.

1|Updated Mar 6, 2026
Search Tags:#deployment#multi-agent#agents#mcp#a2a#google-adk#vertex-ai
One-click install
npx skills add https://github.com/quanganh208/cookmate --skill ck-google-adk-python-quanganh208
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ck:google-adk-python
Source: https://github.com/quanganh208/cookmate/tree/main/.opencode/skills/google-adk-python
Command: npx skills add https://github.com/quanganh208/cookmate --skill ck-google-adk-python-quanganh208

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Building reliable, composable AI agents that integrate tools, maintain session state, coordinate multiple specialists, and run in production is complex and error prone; this Skill consolidates patterns, APIs, and deployment guidance so teams can move from prototype to production faster.

Core Features & Use Cases

  • Multi-agent composition: patterns for coordinators, sub-agents, and hierarchical pipelines to split responsibilities across specialists.
  • A2A and MCP integration: guidance to expose remote agents, use the A2A protocol, and attach MCP servers as toolsets for file, git, or database access.
  • Workflows and orchestration: use Sequential, Parallel, and Loop workflow agents for predictable pipelines or dynamic LLM routing for flexible tasks.
  • State, memory, and artifacts: instructions for ephemeral session state, long-term memory services, and versioned artifact storage for files and binary data.
  • Observability, plugins, and callbacks: examples for adding lifecycle hooks, metrics, tracing, and custom plugins for safety and telemetry.
  • Deployment and evaluation: end-to-end notes for Cloud Run, Vertex AI, GKE deployments and using the adk eval framework for testing and scoring.

Quick Start

Define a root_agent or App in an agent package with an exported agent variable and run adk run my_agent:agent to test the agent locally.

Frequently Asked Questions about ck:google-adk-python

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

FAQPage Schema
How do I build multi-agent pipelines with Google ADK in Python?▼

Build multi-agent pipelines in Google ADK by defining a root agent that coordinates sub-agents. You can use Sequential, Parallel, or Loop workflow agents to create predictable pipelines, or rely on dynamic LLM routing to handle flexible task coordination.

Can I deploy Google ADK agents to Cloud Run or Vertex AI?▼

Yes, Google ADK agents can be deployed to production environments including Cloud Run, Vertex AI, and GKE. The framework provides end-to-end deployment guidance and integrates with adk eval for testing and scoring agents before release.

How does MCP integration work with Google ADK agents?▼

MCP integration in Google ADK lets you attach MCP servers as toolsets for your agents. This allows your agents to access external resources like files, git repositories, or databases directly through the Model Context Protocol.

What is the best way to manage session state and long-term memory in AI agents?▼

Manage session state and long-term memory in AI agents using Google ADK's built-in services. The framework supports ephemeral session state for active conversations, long-term memory services for persistent context, and versioned artifact storage for files.

Do I need Python 3.10 or higher to use the Google ADK library?▼

Yes, Python 3.10 or higher is required to use the Google ADK library. The framework relies on modern Python features to support its modular agent architecture, session management, and plugin callback systems.

How do I evaluate AI agents before production deployment?▼

Evaluate AI agents before production deployment using the adk eval framework included with Google ADK. This tool allows you to test and score your agents, ensuring reliable orchestration of tools and workflows before releasing them.

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