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Fuel Price Scraper

Real-time gas station prices and details by ZIP, city, or place name.

Get current fuel prices and station metadata in real time for any US location. Designed for quick setup, robust scraping, and clean outputs for analysis or downstream automation.

The coverage of this app is mostly in the United States, but does include some international locations such as Canada.

What this Actor does

• Scrapes live prices for a location and fuel type
• Returns clean station data (name, address, distance, ratings, cash/credit prices, timestamps)
• Exports CSV automatically with an optional custom filename

Common use cases:

• Price monitoring across cities/ZIP codes
• Competitive analysis for fuel retail
• Data pipelines feeding dashboards or alerts

Features

• Simple input: just provide search (ZIP/city). This is the only mandatory input required.
• Fuel selection: fuel numeric code (see Inputs). This is optional, but the default will be "1" (Regular Gasoline).
• Freshness control: maxAge in days. This is optional, but the default will be "0" (any age).
• Localized badges/text: lang. Currently only en is supported.
• CSV export: set output_file, or we generate a timestamped name. output_file must be a filename ending in .csv (no directory paths).

Quick start

Find this Actor on the Apify Store.

In Python, see our quickstart tutorial here.

1. Add the Actor to your Apify account and open it.
2. Provide minimal input and run.

Example minimal input:

{
"search":"11507"
}

Example with options:

{
"search":"New York, NY",
"fuel":1,
"lang":"en",
"maxAge":0,
"output_file":"stations_nyc.csv"
}

Example using GPS coordinates (copy-paste from Google Maps):

{
"search":"36.0816642, -115.0534345",
"fuel":4
}

Input parameters

• search (string, required): Location query — accepts ZIP code, city name, or GPS coordinates copy-pasted directly from Google Maps (e.g., "11507", "Los Angeles", "36.0816642, -115.0534345"). Coordinates resolve to the nearest named area — for example, 36.0816642, -115.0534345 returns stations near Henderson, Nevada.
• fuel (integer, optional, default: 1): Fuel type code. Supported values are:
  • 1 = Regular
  • 2 = Midgrade
  • 3 = Premium
  • 4 = Diesel
  • 5 = E85
  • 12 = Unleaded 88
• lang (string, optional, default: "en"): Language code for localized fields. Current allowed value: en.
• maxAge (integer, optional, default: 0): Maximum age of price data in days. Use 0 for the freshest data available.
• output_file (string, optional): Custom CSV filename. Must match ^[^/\\]+\\.csv$ (filename only, .csv extension required). If omitted, a timestamped filename is auto-generated (e.g., gas_stations_11507_2025-08-19_11-01-12_1.csv).

Local testing

• Run the shell-based test suite from the repository root:
  • ./test_actor.sh
• The script runs valid and invalid input scenarios, covers all supported fuel values, and writes failure/warning details to:
  • test_actor.log

Output

Results are stored in the Actor dataset and a CSV file is written to the run storage. The dataset schema includes the following key fields:

• id, name, distance, priceUnit, ratingsCount, starRating
• Address: address_line1, address_line2, address_locality, address_region, address_postalCode
• Prices: price_cash, price_cash_postedTime, price_credit, price_credit_postedTime

Sample dataset item:

{
"id":123456,
"name":"USA",
"distance":1.2,
"priceUnit":"USD/GAL",
"ratingsCount":65,
"starRating":4.5,
"address_line1":"222-33 Braddock Ave",
"address_line2":null,
"address_locality":"Queens Village",
"address_region":"NY",
"address_postalCode":"11428",
"price_cash":2.85,
"price_cash_postedTime":"2025-08-19T10:58:00Z",
"price_credit":2.95,
"price_credit_postedTime":"2025-08-19T10:58:00Z"
}

CSV export

• A CSV is always written. Control the name via output_file, otherwise a timestamped default is used.
• Columns include: id,name,distance,priceUnit,ratingsCount,starRating,address_line1,address_line2,address_locality,address_region,address_postalCode,price_credit,price_credit_postedTime,price_cash,price_cash_postedTime.

Pricing

This Actor uses pay-per-result billing — you only pay for the station records returned.

Billing is automatic — Apify charges per item pushed to the dataset. There is no charge for setup or for runs that return no results.

MCP / AI Integration

This Actor works well with Claude Code and any MCP-compatible client via the Apify MCP server. An AI agent only needs to pass one parameter — search (a ZIP code, city name, or GPS coordinates) — and gets back structured JSON with station names, addresses, cash and credit prices, distance, and ratings. The output is ready for immediate use in pipelines, dashboards, or further analysis without any transformation.

How to connect via MCP:

1. Add the Apify MCP server to your MCP client (Claude Code, Cursor, or any compatible tool).
2. Search for this Actor by name and add it to your toolset.
3. Call it with a search value — ZIP code, city, or GPS coordinates — and get structured station data back instantly.

You can test MCP connectivity with the Tester MCP Client Actor on Apify.

Watch: How to set up and use Apify Actors with MCP

FAQ

No results returned? The search location was not recognized or has no data available. Try a ZIP code instead of a city name — ZIP codes resolve more reliably. Check the run log for a warning message showing which search term returned no data.

What countries are supported? Primarily the United States. Some Canadian locations are covered but availability varies by area.

How fresh is the price data? Prices are crowd-reported and updated frequently. Use the maxAge parameter to filter out stations that haven't reported prices within a certain number of days (e.g., maxAge: 3 returns only stations with prices from the last 3 days).

What fuel types are supported? Regular (1), Midgrade (2), Premium (3), Diesel (4), E85 (5), and Unleaded 88 (12).

Support

• First, check your run logs in the Apify Console for diagnostics.
• If you need help, open a discussion and we will try to respond as quickly as possible. Please include your run ID so we can quickly review the issue.

Roadmap

1. Brand filtering: Search by brand or filter by brand name.
2. More fuel types and payment filters: Add more fuel types and payment filters.
3. Price freshness filters by hours: Add a freshness filter by hours.
4. Alerts and notifications: Add alerts and notifications.
5. Search by distance: Search by distance.
6. Business metadata: Business metadata.

• category (e.g., gas station vs. convenience store)
• website URL, phone number
• opening hours
• neighborhood field
• Geo data
• latitude/longitude fields
• operational status and ads
• "temporarily closed" / "permanently closed"
• "is advertisement" flag
• reviews and media
• review distribution (1–5 stars breakdown)
• individual reviews
• station images

7. Output formats: explicitly export Excel (XLSX) or HTML; we write CSV and dataset items (JSON via Apify dataset export)
8. Provenance: explicit "scrape timestamp" and "search query used" fields in each record (only price postedTime from source, not scrape time)

n8n integration

Available as an n8n community node, n8n-nodes-fuel-prices-api. In n8n, open Settings, Community Nodes, install n8n-nodes-fuel-prices-api, then use the Fuel Prices node in any workflow (it also works as an AI Agent tool).

Last Updated: 2026.06.19