# smolagents purchase research

- **Agent pattern:** `ToolCallingAgent` shortlist
- **Search surfaces:** Shopping, images, and videos
- **Saved artifact:** `smolagents-purchase-research.md`


# What you'll build

A smolagents purchase researcher that combines live listings, visual references, and setup or maintenance demonstrations before recommending a shortlist. Each evidence surface answers a different part of the buying decision.

**Default brief:** Help a beginner choose a compact espresso machine under USD 700. Recommend no more than three options with tradeoffs, evidence limits, and source URLs.


# How the agent works

1.  **Check live listings.** Shopping search captures current products, prices, sellers, and availability signals.
2.  **Inspect form and controls.** Image search supplies visual references without treating appearance as proof of dimensions.
3.  **Test ownership friction.** Video search finds setup and maintenance demonstrations before the agent ranks the shortlist.


# Core agent setup

This excerpt keeps the smolagents model, tools, and research guardrails together. The full script also validates credentials, supplies the prompt, and saves the purchase shortlist.

``` python
agent = ToolCallingAgent(
    model=model,
    tools=[
        shopping_search(provider="smolagents"),
        images_search(provider="smolagents"),
        videos_search(provider="smolagents"),
    ],
    instructions=(
        "Use all relevant tools, treat listing prices as time-sensitive, "
        "and corroborate dimensions rather than inferring them from images."
    ),
    max_steps=8,
)
report = str(agent.run(PROMPT))
```

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


# Run the recipe

Set `SERPAPI_API_KEY` and `OPENAI_API_KEY` in the repository-root `.env`, then run:

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

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

**Bounded research:** The `ToolCallingAgent` gets eight steps and is instructed to use all relevant surfaces while treating listing prices as time-sensitive.


# Inspect the result

The recipe writes `cookbook-output/smolagents-purchase-research.md`. Expect up to three options with current listing evidence, visual and ownership tradeoffs, evidence limitations, and direct source URLs.

Set `COOKBOOK_PROMPT` to research a different product, budget, or buyer profile.


# SerpApi tools used

[shopping_search](../../reference/shopping_search.md#serpapi_search_tools.shopping_search) returns current listings and prices, [images_search](../../reference/images_search.md#serpapi_search_tools.images_search) helps compare form and controls, and [videos_search](../../reference/videos_search.md#serpapi_search_tools.videos_search) finds setup or maintenance demonstrations. Keeping those signals distinct helps the agent explain price, physical, and ownership tradeoffs in the shortlist.

**Framework pattern:** The `ToolCallingAgent` setup follows smolagents' multiple-tools example and uses SerpApi shopping, image, and video search.

- [Open the runnable recipe](https://github.com/serpapi/serpapi-search-tools-python/tree/main/cookbook/smolagents)
- [View smolagents' source example](https://github.com/huggingface/smolagents/blob/main/examples/multiple_tools.py)