# Google ADK retail location strategy

- **Agent pattern:** Session-backed ADK runner
- **Search surfaces:** Maps, web, and news
- **Saved artifact:** `google-adk-location-strategy.md`


# What you'll build

A Google ADK agent that compares cities for the first location of a specialty coffee roaster. It evaluates place-level competition, durable city context, and recent local developments before recommending one market.

**Default brief:** Choose between Austin, Raleigh, and Denver. Compare each city consistently and return evidence, tradeoffs, a recommendation, and source URLs.


# How the agent works

1.  **Inspect place patterns.** Maps search looks for competitors and neighborhood signals at the local level.
2.  **Add context and recency.** Web search supplies durable city facts while news search finds recent local changes.
3.  **Compare in one session.** An in-memory ADK session keeps the evidence together while the agent builds the final strategy.


# Core agent setup

This excerpt shows the ADK agent's three evidence surfaces. The full script also configures its session service, transient-error retries, credentials, and Markdown output.

``` python
agent = Agent(
    name="serpapi_retail_location_strategist",
    model=MODEL,
    instruction=(
        "Use maps for place-level evidence, web for durable context, "
        "and news for current signals."
    ),
    tools=[
        maps_search(provider="google-adk"),
        web_search(provider="google-adk", allowed_engines=["google_light", "bing"]),
        news_search(provider="google-adk"),
    ],
)
runner = Runner(agent=agent, app_name=app_name, session_service=session_service)

async for event in runner.run_async(
    user_id=user_id,
    session_id=session_id,
    new_message=message,
):
    ...
```

[View the complete `main.py` →](https://github.com/serpapi/serpapi-search-tools-python/blob/main/cookbook/google-adk/main.py)


# Run the recipe

Set `SERPAPI_API_KEY` and `GEMINI_API_KEY` (or `GOOGLE_API_KEY`) in the repository-root `.env`, then run:

``` bash
uv run --isolated --no-project --with 'serpapi-search-tools[google-adk]' --with python-dotenv cookbook/google-adk/main.py
```

Optional controls: `GOOGLE_ADK_MODEL`, `COOKBOOK_PROMPT`, and `COOKBOOK_OUTPUT_DIR`.

**Resilient live run:** The recipe retries transient Gemini availability failures up to three times while preserving the same ADK session and task.


# Inspect the result

The recipe writes `cookbook-output/google-adk-location-strategy.md`. Expect a consistent city comparison, place-level evidence, current local signals, tradeoffs, one recommendation, and direct source URLs.

Use `COOKBOOK_PROMPT` to replace the cities, retail concept, or decision criteria without changing the agent setup.


# SerpApi tools used

[maps_search](../../reference/maps_search.md#serpapi_search_tools.maps_search) reveals competitor and neighborhood patterns, [web_search](../../reference/web_search.md#serpapi_search_tools.web_search) supplies durable city context, and [news_search](../../reference/news_search.md#serpapi_search_tools.news_search) tracks recent local developments. Using all three helps the agent compare each city with the same place-level, contextual, and current evidence.

**Framework pattern:** The city-comparison workflow follows Google's retail AI location strategy sample and uses SerpApi maps, web, and news tools.

- [Open the runnable recipe](https://github.com/serpapi/serpapi-search-tools-python/tree/main/cookbook/google-adk)
- [View Google's source example](https://github.com/google/adk-samples/tree/main/python/agents/retail-ai-location-strategy)