google-adk-python

Q: How do I install google-adk-python?

Run npx skills add https://github.com/samhvw8/dot-claude --skill google-adk-python in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/google-adk-python/ (personal) or .claude/skills/google-adk-python/ (project).

by samhvw8AI & Agents

Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.

Installs: 83
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/samhvw8/dot-claude --skill google-adk-python

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads google-adk-python automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Google ADK Python Skill

You are an expert guide for Google's Agent Development Kit (ADK) Python - an open-source, code-first toolkit for building, evaluating, and deploying AI agents.

When to Use This Skill

Use this skill when users need to:

Build AI agents with tool integration and orchestration capabilities
Create multi-agent systems with hierarchical coordination
Implement workflow agents (sequential, parallel, loop) for predictable pipelines
Integrate LLM-powered agents with Google Search, Code Execution, or custom tools
Deploy agents to Vertex AI Agent Engine, Cloud Run, or custom infrastructure
Evaluate and test agent performance systematically
Implement human-in-the-loop approval flows for tool execution

Core Concepts

Agent Types

LlmAgent: LLM-powered agents capable of dynamic routing and adaptive behavior

Define with name, model, instruction, description, and tools
Supports sub-agents for delegation and coordination
Intelligent decision-making based on context

Workflow Agents: Structured, predictable orchestration patterns

SequentialAgent: Execute agents in defined order
ParallelAgent: Run multiple agents concurrently
LoopAgent: Repeat execution with iteration logic

BaseAgent: Foundation for custom agent implementations

Key Components

Tools Ecosystem:

Pre-built tools (google_search, code_execution)
Custom Python functions as tools
OpenAPI specification integration
Tool confirmation flows for human approval

Multi-Agent Architecture:

Hierarchical agent composition
Specialized agents for specific domains
Coordinator agents for delegation

Installation

# Stable release (recommended)
pip install google-adk

# Development version (latest features)
pip install git+https://github.com/google/adk-python.git@main

Implementation Patterns

Single Agent with Tools

from google.adk.agents import LlmAgent
from google.adk.tools import google_search

agent = LlmAgent(
    name="search_assistant",
    model="gemini-2.5-flash",
    instruction="You are a helpful assistant that searches the web for information.",
    description="Search assistant for web queries",
    tools=[google_search]
)

Multi-Agent System

from google.adk.agents import LlmAgent

# Specialized agents
researcher = LlmAgent(
    name="Researcher",
    model="gemini-2.5-flash",
    instruction="Research topics thoroughly using web search.",
    tools=[google_search]
)

writer = LlmAgent(
    name="Writer",
    model="gemini-2.5-flash",
    instruction="Write clear, engaging content based on research.",
)

# Coordinator agent
coordinator = LlmAgent(
    name="Coordinator",
    model="gemini-2.5-flash",
    instruction="Delegate tasks to researcher and writer agents.",
    sub_agents=[researcher, writer]
)

Custom Tool Creation

from google.adk.tools import Tool

def calculate_sum(a: int, b: int) -> int:
    """Calculate the sum of two numbers."""
    return a + b

# Convert function to tool
sum_tool = Tool.from_function(calculate_sum)

agent = LlmAgent(
    name="calculator",
    model="gemini-2.5-flash",
    tools=[sum_tool]
)

Sequential Workflow

from google.adk.agents import SequentialAgent

workflow = SequentialAgent(
    name="research_workflow",
    agents=[researcher, summarizer, writer]
)

Parallel Workflow

from google.adk.agents import ParallelAgent

parallel_research = ParallelAgent(
    name="parallel_research",
    agents=[web_researcher, paper_researcher, expert_researcher]
)

Human-in-the-Loop

from google.adk.tools import google_search

# Tool with confirmation required
agent = LlmAgent(
    name="careful_searcher",
    model="gemini-2.5-flash",
    tools=[google_search],
    tool_confirmation=True  # Requires approval before execution
)

Deployment Options

Cloud Run Deployment

# Containerize agent
docker build -t my-agent .

# Deploy to Cloud Run
gcloud run deploy my-agent --image my-agent

Vertex AI Agent Engine

# Deploy to Vertex AI for scalable agent hosting
# Integrates with Google Cloud's managed infrastructure

Custom Infrastructure

# Run agents locally or on custom servers
# Full control over deployment environment

Model Support

Optimized for Gemini:

gemini-2.5-flash
gemini-2.5-pro
gemini-1.5-flash
gemini-1.5-pro

Model Agnostic: While optimized for Gemini, ADK supports other LLM providers through standard APIs.

Best Practices

Code-First Philosophy: Define agents in Python for version control, testing, and flexibility
Modular Design: Create specialized agents for specific domains, compose into systems
Tool Integration: Leverage pre-built tools, extend with custom functions
Evaluation: Test agents systematically against test cases
Safety: Implement confirmation flows for sensitive operations
Hierarchical Structure: Use coordinator agents for complex multi-agent workflows
Workflow Selection: Choose workflow agents for predictable pipelines, LLM agents for dynamic routing

Common Use Cases

Research Assistants: Web search + summarization + report generation
Code Assistants: Code execution + documentation + debugging
Customer Support: Query routing + knowledge base + escalation
Content Creation: Research + writing + editing pipelines
Data Analysis: Data fetching + processing + visualization
Task Automation: Multi-step workflows with conditional logic

Development UI

ADK includes built-in interface for:

Testing agent behavior interactively
Debugging tool calls and responses
Evaluating agent performance
Iterating on agent design

Resources

GitHub: https://github.com/google/adk-python
Documentation: https://google.github.io/adk-docs/
llms.txt: https://raw.githubusercontent.com/google/adk-python/refs/heads/main/llms.txt

Implementation Workflow

When implementing ADK-based agents:

Define Requirements: Identify agent capabilities and tools needed
Choose Architecture: Single agent, multi-agent, or workflow-based
Select Tools: Pre-built, custom functions, or OpenAPI integrations
Implement Agents: Create agent definitions with instructions and tools
Test Locally: Use development UI for iteration
Add Evaluation: Create test cases for systematic validation
Deploy: Choose Cloud Run, Vertex AI, or custom infrastructure
Monitor: Track agent performance and iterate

Remember: ADK treats agent development like traditional software engineering - use version control, write tests, and follow engineering best practices.

FAQ

What does the google-adk-python skill do?

How do I install google-adk-python?

Run npx skills add https://github.com/samhvw8/dot-claude --skill google-adk-python in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/google-adk-python/ (personal) or .claude/skills/google-adk-python/ (project).

Which AI agents can use google-adk-python?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is google-adk-python safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains google-adk-python?

It is published by samhvw8 in samhvw8/dot-claude. It has 83 recorded installs.

More from samhvw8/dot-claude

View all →

ui-design-system

samhvw8/dot-claude

React UI component systems with TailwindCSS + Radix + shadcn/ui. Stack: TailwindCSS (styling), Radix UI (primitives), shadcn/ui (components), React/Next.js. Capabilities: design system architecture, accessible components, responsive layouts, theming, dark mode, component composition. Actions: review, design, build, improve, refactor UI components. Keywords: TailwindCSS, Radix UI, shadcn/ui, design system, component library, accessibility, ARIA, responsive, dark mode, theming, CSS variables, component architecture, atomic design, design tokens, variant, slot, composition. Use when: building component libraries, implementing shadcn/ui, creating accessible UIs, setting up design systems, adding dark mode/theming, reviewing UI component architecture.

Frontend & Design3.2K installs