adk-agent-patterns

Guide Google ADK 2.0 agent architecture selection and implementation.

2|1|Updated Apr 4, 2026
One-click install
npx skills add https://github.com/Folken2/nuvel --skill adk-agent-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: adk-agent-patterns
Source: https://github.com/Folken2/nuvel/tree/main/nuvel/backends/adk/skills/adk-agent-patterns
Command: npx skills add https://github.com/Folken2/nuvel --skill adk-agent-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill resolves the ambiguity in choosing the right agent architecture for Google ADK 2.0, preventing over-engineered or inefficient agent designs.

Core Features & Use Cases

  • Architecture Decision Tree: Provides a clear framework to choose between single LlmAgents, Workflow graphs, or shortcut classes like LoopAgent and ParallelAgent.
  • Workflow Graph Guidance: Offers best practices for implementing complex multi-step logic, branching, and fan-out/fan-in patterns.
  • Use Case: When building a multi-step research agent, use this skill to determine whether to use a simple SequentialAgent or a more robust Workflow graph to handle revision loops and conditional routing.

Quick Start

Load the adk-agent-patterns skill and ask it to recommend an architecture for a multi-step data analysis task involving branching and human-in-the-loop verification.

Frequently Asked Questions about adk-agent-patterns

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

FAQPage Schema
What's the best way to architect multi-agent systems in Google ADK 2.0?▼

The best way to architect multi-agent systems in Google ADK 2.0 is using an architecture decision tree to select between single LlmAgents, Workflow graphs, or shortcut classes like LoopAgent and ParallelAgent. This prevents over-engineered or inefficient agent designs.

How do I implement conditional routing and branching for LLM agents?▼

To implement conditional routing and branching for LLM agents, use Workflow graph guidance within Google ADK 2.0. It offers best practices for complex multi-step logic, fan-out/fan-in patterns, and cycle management to handle revision loops.

When do I need a Workflow graph instead of a SequentialAgent for multi-step orchestration?▼

You need a Workflow graph instead of a SequentialAgent for multi-step orchestration when your logic requires complex branching, conditional routing, or revision loops. SequentialAgent suits simple linear flows, while Workflow graphs handle robust multi-step logic.

Does Google ADK 2.0 support fan-out and fan-in patterns for agent architecture?▼

Yes, Google ADK 2.0 supports fan-out and fan-in patterns for agent architecture through Workflow graphs and convenience shortcut classes like ParallelAgent. These facilitate idiomatic multi-step orchestration and complex parallel execution.

Why does my multi-agent architecture feel over-engineered in Google ADK?▼

Your multi-agent architecture likely feels over-engineered because of ambiguity in choosing the right agent hierarchy. Applying an architecture decision tree ensures you select idiomatic ADK 2.0 classes, preventing inefficient designs.

Can I use shortcut classes for human-in-the-loop verification in multi-agent systems?▼

Yes, you can use shortcut classes like LoopAgent for human-in-the-loop verification in multi-agent systems. Google ADK 2.0 provides these convenience classes to simplify multi-step orchestration involving branching and cycles.