Do I need Selenium or BeautifulSoup to scrape Google Images with Python?

No. With the CrustAPI endpoint you send one HTTP request using the Python requests library and get clean JSON back. There is no browser to drive and no HTML to parse.

What data do I get for each image when scraping Google Images?

Title, source site name, the page link, a direct image URL, a thumbnail URL, and pixel dimensions for both the image and the thumbnail, plus its position in the results. It is enough to build an index or a dataset without a second call.

Is there a free way to scrape Google Images with Python?

CrustAPI includes 3,000 free credits every month with no credit card, and credits never expire. You pay one credit per search, and empty searches are free.

How to scrape Google Images with Python

This guide shows how to scrape Google Images with Python: call one endpoint, get every image result as clean JSON with the source page and direct file URL, then save it to CSV. No browser automation, no parsing HTML.

3,000 free results / month, no card Title, source, direct image URL Clean JSON, no HTML parsing

PULLED LIVE FROM THE API, JULY 20, 2026

One request, real response

The hard part of scraping Google Images yourself is driving a browser, dodging blocks, and parsing a results grid that loads its image data through JavaScript. Instead you send one HTTP request to a search endpoint and get clean JSON back. First a quick test with curl:

# one credit per search; zero results cost nothing curl "https://crustapi.com/v1/search?type=images&q=sunset" -H "x-api-key: YOUR_KEY"

Then the same call in Python with the requests library. Install it with pip install requests, drop in a key, and run:

# pip install requests, then run this file import requests resp = requests.get( "https://crustapi.com/v1/search", params={"type": "images", "q": "sunset"}, headers={"x-api-key": "YOUR_KEY"}, ) data = resp.json() for img in data["images"]: print(img["title"], img["source"], img["imageUrl"])

The results live under the images key. Here are the first two that came back for sunset, shown as CSV so they drop straight into Sheets or a database:

title,source,imageWidth,imageHeight,imageUrl,link "The Science of Sunsets: Nature's Most Stunning Light Show - CMY Cubes","CMY Cubes",1100,733,"https://www.cmycubes.com/cdn/shop/articles/sebastien-gabriel--imlv9jlb24-unsplash-1_2cd354bd-fe04-4a11-b301-5b002e1e9f61.jpg?v=1780874958&width=1100","https://www.cmycubes.com/blogs/cmycubes/the-science-of-sunsets" "Check out this epic sunset I captured in the Phoenix Mountains Preserve - AZ Wonders","Azwonders",576,1024,"https://i0.wp.com/azwonders.com/wp-content/uploads/2023/12/epic-sunset-2.jpg?resize=576%2C1024&ssl=1","https://azwonders.com/2024/01/09/check-out-this-epic-i-sunset-captured-in-the-phoenix-mountains-preserve/"

This one query returned 99 images in a single response. To pull more, add a page number and loop, collecting each page into one list:

# walk pages 1 through 3 into a single list all_images = [] for page in range(1, 4): resp = requests.get( "https://crustapi.com/v1/search", params={"type": "images", "q": "sunset", "page": page}, headers={"x-api-key": "YOUR_KEY"}, ) all_images += resp.json()["images"] print("pulled", len(all_images), "images")

Change q to any search you would type into the Google Images box, like golden retriever puppy or mid century sofa. Add gl and hl to set the country and language.

WHAT EVERY IMAGE INCLUDES

The full record, ready to use

Every image result comes complete, so you build a mood board, a dataset, or a monitoring feed with no second call. Here is one record as CSV, ready for Sheets or a database:

title,source,domain,imageWidth,imageHeight,thumbnailWidth,thumbnailHeight,imageUrl,link,position "The Science of Sunsets: Nature's Most Stunning Light Show - CMY Cubes","CMY Cubes","www.cmycubes.com",1100,733,275,183,"https://www.cmycubes.com/cdn/shop/articles/sebastien-gabriel--imlv9jlb24-unsplash-1_2cd354bd-fe04-4a11-b301-5b002e1e9f61.jpg?v=1780874958&width=1100","https://www.cmycubes.com/blogs/cmycubes/the-science-of-sunsets",1

Or the same record as JSON, exactly as the API returns it:

// one image in full { "title": "The Science of Sunsets: Nature's Most Stunning Light Show - CMY Cubes", "imageUrl": "https://www.cmycubes.com/cdn/shop/articles/sebastien-gabriel--imlv9jlb24-unsplash-1_2cd354bd-fe04-4a11-b301-5b002e1e9f61.jpg?v=1780874958&width=1100", "imageWidth": 1100, "imageHeight": 733, "thumbnailUrl": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTs2Avh6chhSSTBhMM9Z-TfzxTLwC6EnIfWg3RAwTsaXQ&s", "thumbnailWidth": 275, "thumbnailHeight": 183, "source": "CMY Cubes", "domain": "www.cmycubes.com", "link": "https://www.cmycubes.com/blogs/cmycubes/the-science-of-sunsets", "position": 1 }

The imageUrl is the full-size file on the source site, thumbnailUrl is Google's small preview, and link is the page the image sits on. Pixel dimensions come with both.

HOW IT COMPARES

vs writing your own scraper

You can build an image scraper with a headless browser and a parser, and plenty of tutorials show how. The trade is maintenance. Here is the difference in practice.

	Writing it yourself	CrustAPI
Setup	Install a headless browser, handle blocks and page changes	One GET request, no browser
Output	Parse the results grid yourself; fields break when the layout changes	Clean JSON from the API, or the same rows as CSV
Maintenance	Fix the scraper each time the page changes	We keep it working
Free tier	Free, but you pay in time and blocked requests	3,000 results / month, no card
Cost model	Server, proxy, and developer time	One credit per search; empty searches free
Storing the data	Yours to manage	No storage limits. Export to CSV or a database

PRICING

One credit per search

Prepaid packs, and credits never expire: 25k for $49 · 100k for $149 · 500k for $549 · 2.5M for $1,999 · up to 250M, all self-serve, ex-tax. A search bills one credit no matter how many images come back, and a search that returns none costs nothing.

Run your first image search free

3,000 credits a month. No card, no contract.

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THE LAST STEP

Save the results to a CSV file

Because the response is plain JSON, Python's built-in csv module writes it to a file in a few lines. No extra libraries. This reads every image from the search and writes one row each:

# save every image to a CSV file with the built-in csv module import csv, requests data = requests.get( "https://crustapi.com/v1/search", params={"type": "images", "q": "sunset"}, headers={"x-api-key": "YOUR_KEY"}, ).json() with open("images.csv", "w", newline="") as f: writer = csv.writer(f) writer.writerow(["title", "source", "imageUrl", "width", "height", "link"]) for img in data["images"]: writer.writerow([img["title"], img["source"], img["imageUrl"], img.get("imageWidth", ""), img.get("imageHeight", ""), img.get("link", "")])

Open images.csv in Sheets or Excel and you have a finished image index. Swap the query, run it again, and append to build a bigger set. To download the files themselves, loop over the rows and fetch each imageUrl.

FAIR PLAY

When a different tool fits better: licensed stock imagery, rights-cleared photos, or a contract with a formal SLA are a stock-photo license or a Google Cloud product, not scraping. This endpoint returns the public image results anyone sees on the Google Images page, which is what most research, monitoring, and dataset work needs. Check the license on each source page before you reuse a file.

BEFORE YOU ASK

Common questions

WORKS WITH YOUR STACK

LangChain n8n MCP

Clay

Try it on any search term

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