ADK Sample Creator

Creates samples under contributing/samples/. These are deliberately minimal agents that each exercise one or two features — distinct from the adk-samples repository, which hosts full end-to-end applications.

Read the adk-style skill first for ADK 2.0 conventions if you have not already.

1. Pick the category directory

Almost every sample lives at contributing/samples/{category}/{sample_name}/. List the categories and confirm with the user which one the sample belongs in before creating anything — a workflow sample landing outside workflows/ is the usual mistake.

ls contributing/samples/

Categories include workflows, patterns, core, multi_agent, tools, models, live, mcp, a2a, evaluation, and plugins. A handful of samples nest one level further when a single feature needs several variants, as plugins/plugin_reflect_tool_retry/basic/ does.

Name the sample directory in snake_case after the feature it demonstrates: dynamic_nodes, fan_out_fan_in, streaming_tool_events.

Do not add an _agent suffix, and do not repeat the category as a prefix — every sample is an agent, and the category is already in the path. Many existing directories still carry both; do not copy them.

2. Write agent.py

Contents of a sample directory:

Use absolute imports so the file can be run and imported directly.

Do not set model= on Agent instances. Samples inherit the system-configured model, which keeps them working when the default model changes; hardcoding model="gemini-2.5-flash" pins the sample to a model that will be retired. Set it only when the user explicitly asks for a specific model.

Then pick one of the two shapes.

Pattern A — Workflow, for multi-step graphs

Use when the sample needs multiple nodes, routing, or parallel execution.

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

Import Workflow from google.adk, not from a private google.adk.workflow._* module.

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


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


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

A plain function can be used as a node directly in edges; reach for the @node(...) decorator when you need one of its options, such as rerun_on_resume=True for a node that calls ctx.run_node.

Pattern B — Standalone agent, for single-agent or simple tool use

Use when there is no graph and the agent drives its own loop.

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

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

3. Write README.md

Follow readme-template.md — section order, prompt formatting, the Mermaid topology rules, and the relative link depth for docs/guides/.

Worked examples

Read these two before writing a new Pattern A sample — one dynamic graph, one static one.

• contributing/samples/workflows/dynamic_nodes/agent.py — a Python node driving a while loop with ctx.run_node, so the number of agent calls is decided at runtime rather than by the edges.

  @node(rerun_on_resume=True)
  async def orchestrate(ctx: Context, node_input: str) -> str:
    yield Event(state={"topic": node_input})
  
    while True:
      headline = await ctx.run_node(generate_headline)
      # ...
  

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• contributing/samples/workflows/fan_out_fan_in/agent.py — three functions run in parallel from START, collected by a JoinNode, then aggregated.

  join_node = JoinNode(name="join_for_results")
  
  root_agent = Workflow(
      name="root_agent",
      edges=[(
          "START",
          (make_uppercase, count_characters, reverse_string),
          join_node,
          aggregate,
      )],
  )
  

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