google-adk-python

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SKILL.md (first lines)

# 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

```bash
# 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

```python
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

```python
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

```python
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

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