feat: add Firecrawl shopping scraper example

examples/firecrawl_shopping_scraper/.env.example

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+# Firecrawl API key — sign up for free at https://firecrawl.dev
+FIRECRAWL_API_KEY=fc-your-key-here
+
+# LLM provider key (example uses Anthropic Claude)
+ANTHROPIC_API_KEY=your-anthropic-key-here

examples/firecrawl_shopping_scraper/README.md

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+# Firecrawl Shopping Scraper
+
+A product extraction agent built with the **Upsonic AI Agent Framework** and **FirecrawlTools**. Point it at any shopping website and it scrapes the page, extracts product names, prices, and descriptions, and returns the results as a clean, sorted table.
+
+The example targets [books.toscrape.com](http://books.toscrape.com), a publicly available scraping-safe demo bookstore, but the same pattern works for any publicly accessible e-commerce site.
+
+## Features
+
+- **Single-page scraping**: Fetches a shop page and converts it to clean Markdown via Firecrawl
+- **LLM-powered extraction**: The agent reads the Markdown and pulls out structured product data without custom parsers or CSS selectors
+- **Minimal tool surface**: Only `scrape_url` is enabled so the agent cannot accidentally crawl, search, or batch-scrape
+- **Sorted output**: Products are returned as a Markdown table ordered by price descending, with a summary line showing total count and price range
+- **Extensible**: Switch to `crawl_website` for multi-page crawling or `extract_data` for schema-driven JSON extraction
+
+## Prerequisites
+
+- Python 3.10+
+- Firecrawl API key (sign up for free at [firecrawl.dev](https://firecrawl.dev))
+- Anthropic API key (or swap the model for any Upsonic-supported provider)
+
+## Installation
+
+1. Navigate to this directory:
+
+   ```bash
+   cd examples/firecrawl_shopping_scraper
+   ```
+
+2. Create and activate a virtual environment:
+
+   ```bash
+   # With uv (recommended)
+   uv venv && source .venv/bin/activate
+
+   # With pip
+   python3 -m venv .venv && source .venv/bin/activate
+   ```
+
+3. Install dependencies:
+
+   ```bash
+   # With uv
+   uv pip install -r requirements.txt
+
+   # With pip
+   pip install -r requirements.txt
+   ```
+
+4. Set up your environment variables:
+
+   ```bash
+   cp .env.example .env
+   ```
+
+   Then open `.env` and fill in your keys:
+
+   ```bash
+   FIRECRAWL_API_KEY=fc-your-key-here
+   ANTHROPIC_API_KEY=your-anthropic-key-here
+   ```
+
+## Usage
+
+Run the agent:
+
+```bash
+python main.py
+# or
+uv run main.py
+```
+
+Example output:
+
+```
+Found 20 products · Price range: £10.00 - £59.69
+
+| #  | Book Title                                   | Price  | Rating |
+|----|----------------------------------------------|--------|--------|
+| 1  | Libertarianism for Beginners                 | £59.69 | Two    |
+| 2  | It's Only the Himalayas                      | £52.29 | Two    |
+| 3  | The Black Maria                              | £52.15 | One    |
+| 4  | Starving Hearts (Triangular Trade Trilogy...) | £13.99 | Two    |
+...
+```
+
+To target a different shop, change the URL in the task description inside `main.py`:
+
+```python
+task = Task(
+    description="""
+    Scrape https://your-target-shop.com and extract all visible products.
+    For each product return name, price, and a short description (1-2 sentences).
+    Format as a Markdown table sorted by price descending.
+    """
+)
+```
+
+## Project Structure
+
+```
+firecrawl_shopping_scraper/
+├── main.py          # Agent setup and task definition
+├── requirements.txt # Python dependencies
+├── .env.example     # Environment variable template
+└── README.md        # This file
+```
+
+## How It Works
+
+1. **FirecrawlTools is configured** with only `scrape_url` enabled. This keeps the agent focused and prevents it from issuing unnecessary crawl or search calls.
+
+2. **The task description** tells the agent what page to scrape and exactly what to extract. No custom parser is needed; the LLM reads the Markdown Firecrawl returns and identifies product blocks by structure and context.
+
+3. **Firecrawl fetches the page** and returns it as clean Markdown, stripping navigation, ads, and boilerplate so the LLM gets a compact, structured representation of the content.
+
+4. **The agent extracts and formats** each product row into a Markdown table, sorts by price descending, and prepends a summary line.
+
+### Extending the example
+
+To crawl multiple pages instead of just the homepage, enable `crawl_website`:
+
+```python
+firecrawl = FirecrawlTools(
+    enable_scrape=False,
+    enable_crawl=True,
+    enable_crawl_management=True,
+)
+
+task = Task(
+    description="""
+    Crawl http://books.toscrape.com up to 5 pages and extract every product:
+    name, price, and rating. Return a single Markdown table sorted by price descending.
+    """
+)
+```
+
+To get structured JSON output directly from Firecrawl's LLM extraction layer, enable `extract_data`:
+
+```python
+firecrawl = FirecrawlTools(
+    enable_scrape=False,
+    enable_extract=True,
+)
+
+task = Task(
+    description="""
+    Use extract_data on http://books.toscrape.com/* with this schema:
+    {"products": [{"name": "string", "price": "string", "rating": "string"}]}
+    Return the raw result.
+    """
+)
+```

examples/firecrawl_shopping_scraper/main.py

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+import os
+from dotenv import load_dotenv
+from upsonic import Agent, Task
+from upsonic.tools.custom_tools.firecrawl import FirecrawlTools
+
+load_dotenv()
+
+# Only enable scrape_url — the agent does not need crawling or search for this task
+firecrawl = FirecrawlTools(
+    enable_scrape=True,
+    enable_crawl=False,
+    enable_map=False,
+    enable_search=False,
+    enable_batch_scrape=False,
+    enable_extract=False,
+    enable_crawl_management=False,
+    enable_batch_management=False,
+    enable_extract_management=False,
+)
+
+task = Task(
+    description="""
+    Scrape the homepage of http://books.toscrape.com and extract ALL
+    products visible on the page.
+
+    For each product return:
+      - Name  (full book title)
+      - Price (as shown, e.g. '£51.77')
+      - Rating (word form, e.g. 'Three')
+
+    Format the output as a Markdown table:
+
+    | # | Book Title | Price | Rating |
+    |---|-----------|-------|--------|
+
+    Sort by price descending. Add a one-line summary at the top
+    with the total number of products found and the price range.
+    """
+)
+
+agent = Agent(
+    model="anthropic/claude-sonnet-4-6",
+    tools=[firecrawl],
+)
+
+result = agent.do(task)
+print(result)

examples/firecrawl_shopping_scraper/requirements.txt

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+upsonic
+firecrawl-py
+python-dotenv
+anthropic