adk-sample-creator
Creates a new sample agent in the ADK Python repository — the sample directory, its `agent.py`, and its `README.md` — following the conventions the existing samples already use. Use when the user wants to add a sample or example demonstrating a feature or agent pattern (dynamic nodes, fan-out/fan-in, a standalone tool-using agent), asks where a new sample belongs under `contributing/samples/`, or wants an existing sample's README brought up to the standard structure. Don't use for building a real working agent for the user's own project (use `adk-agent-builder`), or for checking whether the Python blocks in a Markdown file run (use `adk-verify-snippets`).
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- 2026년 8월 14일 00:10
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- adk-sample-creator
- description
- Creates a new sample agent in the ADK Python repository — the sample directory, its `agent.py`, and its `README.md` — following the conventions the existing samples already use. Use when the user wants to add a sample or example demonstrating a feature or agent pattern (dynamic nodes, fan-out/fan-in, a standalone tool-using agent), asks where a new sample belongs under `contributing/samples/`, or wants an existing sample's README brought up to the standard structure. Don't use for building a real working agent for the user's own project (use `adk-agent-builder`), or for checking whether the Python blocks in a Markdown file run (use `adk-verify-snippets`).
# 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.
```bash
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:
| File | Required | Purpose |
| --- | --- | --- |
| `agent.py` | yes | The agent or workflow. Must expose `root_agent`. |
| `README.md` | yes | See [readme-template.md](references/readme-template.md). |
| `__init__.py` | sometimes | Present when the sample is imported as a package. |
| `tests/*.json` | no | Recorded sessions used as eval sets. |
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.
```python
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.
```python
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.
```python
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](references/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.
```python
@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)
# ...
```
- `contributing/samples/workflows/fan_out_fan_in/agent.py` — three functions
run in parallel from `START`, collected by a `JoinNode`, then aggregated.
```python
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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