Product data API for AI shopping agents: 5 options compared

TL;DR

• A product data API returns products as structured fields: price, sale price, variants, stock, images and reviews. AI shopping agents call one so they answer from today's catalog, since a model's training data holds no current prices.
• Five options cover most teams: Bright Data, Apify, Oxylabs, Zyte and Firecrawl. The first four sell per-site scrapers, Actors or AI extraction, priced per 1,000 records, results or responses.
• Firecrawl gives an AI shopping agent product data through one account. Firecrawl Alexandria adds retailer providers such as Shopify, Walmart and Best Buy, and the Firecrawl /scrape product format turns any product page into structured product JSON without an LLM call.

A product data API is a service that returns product listings as structured fields instead of raw web pages. An AI shopping agent calls it to search a catalog, read one product's price and stock, or compare the same item across stores, without writing a parser for every retailer.

Product data reaches an agent in two ways:

• Retailer and marketplace connectors. The agent searches a store's catalog by keyword or ID and gets back clean records with prices, ratings and fulfillment options.
• Product page extraction. The agent passes a product URL and gets back the product on that page, with each variant's price and availability.

Agents usually need both. Connectors cover search across the big retailers, and extraction covers the long tail of brand stores that no connector lists.

AI shopping agents need a product data API because language models answer from training data. A model doesn't know today's price, whether a size is in stock, or whether a sale ended yesterday, so an agent without a live lookup guesses.

Shoppers already see the gap. In a Gartner survey published May 27, 2026, 54% of consumers who used AI for a recent purchase said they had to double-check all the information it gave them.

The traffic is already there. Adobe data reported by Digital Commerce 360 shows AI-referral traffic to US retail sites grew 62% year over year in July 2026, and those visits converted 60% better than non-AI traffic.

A product data API for AI agents should return current data in a consistent shape, cover the stores your users shop, and connect to the AI tools your agent runs in. Price per lookup matters too, because agents make many calls per conversation.

1. Coverage. Check which retailers have ready-made connectors, and how the API handles a store that has none.
2. Freshness. A live call returns today's price. A dataset is a snapshot, which suits analysis better than answering a shopper.
3. Variant detail. Price and stock should sit on each size or color, since that is how people buy.
4. Agent access. Look for an MCP server and plugins for ChatGPT and Claude, plus SDKs for agents you build yourself.
5. Pricing at agent volume. Work out what one product lookup costs, then multiply by the lookups per conversation.

Five product data APIs come up most often for AI shopping agents: Bright Data, Apify, Oxylabs, Zyte and Firecrawl. All five connect to AI agents through an MCP server. They differ in how they get product data and how they charge for it.

Firecrawl gives shopping agents retailer catalogs through Firecrawl Alexandria providers such as Shopify, Walmart and Best Buy, reads any product page with the Firecrawl /scrape product format, and finds product pages with Firecrawl /search. A product page costs 1 Firecrawl credit and most retail Alexandria tools cost 5, and agents connect through the Firecrawl MCP server. The Firecrawl section below covers the details.

Bright Data offers a Web Scraper API with ready-made scrapers for retail sites and a library of product datasets. It bills per 1,000 records, where a record is one extracted item, and its MCP server connects it to AI agents. Our Bright Data alternatives guide compares it with other providers.

Apify runs scrapers called Actors, published by Apify and by community developers in its e-commerce category. Usage is billed in compute units on monthly plans from $19, and the free plan includes $5 of usage. See our Apify alternatives roundup for more options.

Oxylabs sells a Web API with dedicated scrapers, called by target and collection, such as /amazon/product. Its pricing starts at $0.50 per 1,000 results and comes with $5 in free credits. Our Oxylabs alternatives list covers similar tools.

Zyte uses AI extraction in Zyte API to return product fields from a URL. Its pricing depends on how hard a site is to fetch and whether it needs a browser. For other options, see our Zyte alternatives comparison.

Firecrawl is the context API for AI agents. For shopping agents it covers both paths in one account: Firecrawl Alexandria returns catalog data from retailer providers, and the Firecrawl /scrape product format reads any product page. Firecrawl /search finds product pages, and Firecrawl /monitor tracks them for price changes.

Firecrawl Alexandria is Firecrawl's library of data providers and specialized indexes that AI agents call through one Firecrawl account. For shopping, it brings catalog search, prices, store availability and reviews from retailers into the same chat or agent run. Finding a tool is free, and running one costs the Firecrawl credits listed on it. Through the Firecrawl MCP server, an agent sees those prices by calling firecrawl_find_tools with includeCosts: true.

These Firecrawl Alexandria tools cover the jobs a shopping agent runs most:

Some providers ask an admin on your Firecrawl team to accept their terms in the dashboard before their tools run. Shopping is one use, and the same account reaches every other Firecrawl Alexandria category:

The product format of Firecrawl /scrape turns one product page into one structured product. It returns the title, brand, category, description and every variant, and each variant carries its price, original price when on sale, availability and images.

It works without an LLM call or a schema. Firecrawl reads the structured data the store already publishes, such as JSON-LD, schema.org markup and embedded app state, and merges it so fields from different products never mix. When a page is ambiguous, it returns no product and a warning instead of a guess. The product format docs list every field.

A product page read this way costs 1 Firecrawl credit, the same as a standard scrape. That makes it the default choice for brand stores and independent shops that no connector covers. Our free product data extractor shows the output on any URL.

Firecrawl /search finds product pages across the web when the agent only has a product name, and it can scrape each result in the same call. Firecrawl /monitor runs scheduled checks on pages you choose and sends a webhook or email when one changes, which suits price-drop and back-in-stock alerts. Our guide to automated competitor price monitoring walks through a full tracker.

Minimal, an AI customer support platform for ecommerce brands, runs nearly six million storefront pages a month through Firecrawl so its AI agents answer from the live store. Its customer story covers the setup.

Storefronts come to us in every shape imaginable, and no matter the complexity, Firecrawl turns each one into clean, structured data our AI support agents can answer from.

Niek Hogenboom, Co-Founder, Minimal

You connect an AI shopping agent to Firecrawl through the Firecrawl plugin for ChatGPT, the Firecrawl connector for Claude, or the Firecrawl MCP server for any other agent. Each one signs in to your Firecrawl account, so there is no API key to paste.

1. Install Firecrawl. In ChatGPT, open Plugins, search for Firecrawl and select Install. In Claude, add the Firecrawl connector from the connector directory, then turn it on from the + menu under Connectors.
2. Sign in and approve the connection. If you don't have an account yet, you can create a free one at this step.
3. Start a new chat and mention Firecrawl in your prompt (type @Firecrawl in ChatGPT).

Then paste a prompt like these, with your own products in the brackets:

@Firecrawl find [Sony WH-1000XM5 headphones] at Best Buy, Walmart and Newegg.
For each store, give me the current price, whether it's in stock, and the link.
Then tell me which one is cheapest today.

@Firecrawl read this product page with the product format: [product URL].
List every variant with its price, the original price if it's on sale,
and whether it's in stock.

@Firecrawl read the Walmart reviews for [product name]. Summarize the top
three complaints and the top three things buyers like, with a count for each.

You don't have to name a provider. The assistant picks the Firecrawl Alexandria tool or Firecrawl /scrape call that fits the request.

Any AI agent that supports MCP can use the same tools through the Firecrawl MCP server.

• Claude Code: run claude mcp add --transport http firecrawl https://mcp.firecrawl.dev/v2/mcp-oauth, then sign in from /mcp.
• Codex CLI: run codex mcp add firecrawl --url https://mcp.firecrawl.dev/v2/mcp-oauth, then codex mcp login firecrawl.
• Cursor and other clients with a JSON config: add the entry below to your MCP settings.

{
  "mcpServers": {
    "firecrawl": {
      "url": "https://mcp.firecrawl.dev/v2/mcp-oauth"
    }
  }
}

Developers building their own agent can call the same tools from the Python and Node SDKs, the REST API or the Firecrawl CLI. This Python example reads a product page with the product format, then searches products across Shopify merchants through Firecrawl Alexandria:

from firecrawl import Firecrawl
 
firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")
 
# Firecrawl /scrape: one product page in, one structured product out
page = firecrawl.scrape(
    "https://www.allbirds.com/products/mens-tree-runners",
    formats=["product"],
)
for variant in page.product.variants[:3]:
    print(page.product.title, variant.values, variant.price.formatted, variant.availability.in_stock)
 
# Firecrawl Alexandria: search products across Shopify merchants
results = firecrawl.scrape(
    alexandria={
        "provider": "shopify",
        "capability": "catalog/search_catalog",
        "options": {"query": "trail running shoes under $150", "country": "US", "limit": 3},
    },
)
for item in results.alexandria[0].data["products"]:
    print(item["title"])

Men's Tree Runner - Jet Black (White Sole) {'size': '8'} $100.00 True
Men's Tree Runner - Jet Black (White Sole) {'size': '9'} $100.00 False
Men's Tree Runner - Jet Black (White Sole) {'size': '10'} $100.00 False
Men's ZigWild Trail 6 Shoes
Saucony Excursion TR15 Trail Running Shoe - Men's

Firecrawl Alexandria isn't part of Firecrawl's keyless tier, so provider calls need a signed-in Firecrawl account (the ChatGPT plugin, Claude connector and MCP sign-in) or an API key. The free plan includes 1,000 Firecrawl credits a month, and the Hobby plan is $19 a month billed monthly for 5,000. Teams running shopping agents at scale can review the enterprise page and security docs.

Pick the product data API that matches how your agent shops. If it answers shoppers in a chat, it needs live lookups across the retailers your users buy from plus a way to read any other store's page, through an MCP server it can call. Firecrawl covers both in one account, so the agent can search Walmart and Best Buy, read a brand store's product page, and set a price alert in the same run.