ADK Sample Creator

This skill helps you create new samples for the ADK Python repository. You should search for subdirectories under contributing (such as new_workflow_samples, workflow_samples, etc.) and confirm with the user which folder they want to use before creating the sample.

[!TIP]

Before creating samples, you can use the adk-style skill to learn about ADK 2.0 architecture knowledge and best practices.

A sample consists of:

1. A directory per sample.
2. An agent.py file defining the agent or workflow logic.
3. A README.md file explaining the sample.

Guidelines

1. Folder Name

Use snake_case for the folder name (e.g., dynamic_nodes, fan_out_fan_in).

2. agent.py Content

The agent.py should focus on demonstrating a specific feature or agent pattern. Use absolute imports for testing convenience.

[!IMPORTANT] Model Selection: Do not set the model parameter explicitly (e.g., model="gemini-2.5-flash") on Agent instances in sample agents. Instead, let them default to the system-configured model, unless a specific model is explicitly requested by the user.

Choose one of the following patterns:

Pattern A: Workflows (for complex graphs)

Use this when you need multiple nodes, routing, or parallel execution.

Imports:

from google.adk import Agent
from google.adk import Context
from google.adk.workflow import node
from google.adk.workflow import JoinNode
from google.adk.workflow._workflow_class import Workflow

Anatomy:

my_agent = Agent(name="my_agent", ...)

@node()
async def my_node(node_input: str):
    return "result"

root_agent = Workflow(
    name="root_wf",
    edges=[("START", my_node)],
)

Pattern B: Standalone Agents (for single-agent or simple tool use)

Use this when you don't need a graph and the agent handles the loop.

Imports:

from google.adk import Agent
from google.adk.tools import google_search  # example

Anatomy:

root_agent = Agent(
    name="standalone_assistant",
    instruction="You are a helpful assistant.",
    description="An assistant that can help with queries.",
    tools=[google_search],
)

3. README.md Content

Each sample should have a README.md with the following structure:

• Overview: What the sample does.
• Sample Inputs: Examples of inputs to test with. Each prompt must be wrapped in backticks. If a prompt has an explanation, always add a blank line between the prompt and the explanation, and indent the explanation by two spaces.
• Graph: Visualization of the graph flow (Mermaid recommended). For Workflow root agents, visualize the graph flow of nodes. For agents that orchestrate tools or sub-agents (e.g., LlmAgent, ManagedAgent), visualize the topology of the agent and its tools/sub-agents instead of internal workflow nodes. Keep it a simple topology diagram (a few nodes and edges). Do not draw a request/response data-flow sequence (e.g., user -> agent -> API -> tool -> ... -> user); those are noisy and add little value over the topology.
• How To: Explanation of key techniques used (e.g., ctx.run_node).
• Related Guides: Links to relevant developer guides in docs/guides/ that explain the concepts or classes used.

README Example Template:

# ADK Sample Name

## Overview

Brief description.

## Sample Inputs

- `Prompt example 1`

- `Prompt example 2`

  *Explanation or expected behavior*

## Graph

For Workflow root agents:
```mermaid
graph TD
    START --> MyNode
```

For agents that orchestrate tools or sub-agents (`LlmAgent`, `ManagedAgent`, ...):
```mermaid
graph TD
    MyAgent[my_agent] -->|calls| MyTool(my_tool)
```

## How To

Explain the details.

## Related Guides

- [Guide Title](../../docs/guides/path/to/guide.md) - Brief description of what the guide covers.

Examples

Dynamic Nodes

Snippet from dynamic_nodes/agent.py:

@node(rerun_on_resume=True)
async def orchestrate(ctx: Context, node_input: str) -> str:
    while True:
        headline = await ctx.run_node(generate_headline)
        # ...

Fan Out Fan In

Snippet from fan_out_fan_in/agent.py:

root_agent = Workflow(
    name="root_agent",
    edges=[("START", (node_a, node_b), join_node, aggregate)],
)