How to scrape Kickstarter data

Scraping Kickstarter data can be tricky, especially since there's no official public Kickstarter API available for developers. In this guide, I'll show you how to scrape Kickstarter data in a clean and reliable way using a dedicated scraping API. Instead of dealing with fragile HTML parsing or reverse engineering internal endpoints, you'll learn how to request Kickstarter pages, extract structured data, and turn it into something you can actually use.

We'll walk through the full process step by step: setting up your project, making your first request, using AI to extract campaign data, and storing the results for further analysis or integration into your own workflows.

Quick answer (TL;DR)

There's no official public Kickstarter API, but you can still scrape Kickstarter data reliably using ScrapingBee.

Instead of dealing with fragile HTML parsing or internal endpoints, you can:

• request Kickstarter pages through ScrapingBee
• render JavaScript content
• extract campaign data with AI into structured JSON

👉 Find more information about our Kickstarter scraper.

What is the Kickstarter API

Let's clear this up first, because this part trips people up all the time.

There is no official public Kickstarter API you can just sign up for and use. What people usually call the "Kickstarter API" is actually the internal API that the Kickstarter website uses under the hood.

If you open DevTools and watch network requests while browsing Kickstarter, you'll see JSON endpoints powering things like project listings, search results, and campaign details. These endpoints return structured data, which makes them very tempting if you want to scrape Kickstarter.

And yeah, you can use them. The basic idea is simple:

• find the request in the Network tab
• copy the endpoint
• send your own request to it
• get JSON back

Sounds perfect, right? Not quite.

These internal endpoints are:

• undocumented
• subject to change at any time
• protected by rate limits and anti-bot systems

So while they're useful for quick experiments, they're not the most reliable foundation if you're building something real.

This is where a web scraping API like ScrapingBee comes in. Instead of relying on fragile internal endpoints, you can request Kickstarter pages through a stable API, handle blocking automatically, and extract the exact data you need.

Benefits of using Kickstarter API

Using a dedicated Kickstarter scraping API like ScrapingBee gives you a much cleaner and more reliable way to extract data compared to scraping manually.

First, you get structured data. Instead of parsing messy HTML, you're working with clean JSON output that's ready to use. That means less time dealing with selectors and more time building actual features.

Second, it's more efficient. You don't have to worry about handling JavaScript, retries, or edge cases yourself. Everything is handled for you behind the scenes.

And most importantly, it helps you avoid getting blocked.

With ScrapingBee, you get:

• automatic proxy rotation to avoid IP bans
• stealth proxies and realistic headers
• JavaScript rendering for dynamic pages
• stable, consistent requests without constant maintenance

On top of that, you can extract exactly the data you need using built-in tools or AI prompts, without reverse engineering anything. So instead of building and maintaining your own scraping setup, you can focus on getting reliable Kickstarter data from day one.

Data you can extract

Once you start working with Kickstarter data, there's a lot you can pull out and actually use.

At the project level, you can extract:

• project name, description, and category
• funding goal and current pledged amount
• number of backers
• campaign duration and launch date
• creator information

You can also go deeper depending on your use case:

• reward tiers and pricing
• campaign updates
• comments and engagement signals

All of this can be collected in a structured, ready-to-use format, which makes it easy to plug into your workflows.

From there, you can:

• analyze trends across categories
• track campaign performance over time
• compare successful vs failed projects
• build dashboards or internal tools

And if you want to see a similar approach in action, check our scraper for Patreon API.

How to scrape Kickstarter data using ScrapingBee

Alright, now let's get practical and actually scrape Kickstarter data in a reliable way. We're going to use ScrapingBee as our main tool. It handles all the annoying stuff for you like proxies, blocking, and JavaScript rendering, so you can focus on getting the data.

We'll go step by step:

• get your API key
• send your first request
• handle JavaScript-heavy pages
• extract the data you need

Let's start with the setup.

Step 1: Get your API key

First thing you need is a ScrapingBee API key.

Head over to the signup page and create an account. You can register for free and you'll get 1,000 scraping credits without even adding a credit card, which is perfect for testing things out.

👉 Learn more about our pricing.

Once you're in, your API key will be right there in your dashboard.

Just copy it, and you're ready to start making requests.

Step 2: Set up your Python project

Now it's time to create your Python project. I recommend using uv to manage everything.

Run the following commands:

uv init kickstarter-scraper
cd kickstarter-scraper

Now add the dependencies you'll need:

uv add requests python-dotenv

Next, create a .env file in your project root and store your API key there:

SCRAPINGBEE_API_KEY=your_api_key_here

This keeps your key out of your code, which is always a good habit.

Now in your Python script (for example main.py), load the API key like this:

import csv
import json
import os
from typing import Any

import requests
from dotenv import load_dotenv

load_dotenv()

def get_api_key() -> str:
    api_key = os.getenv("SCRAPINGBEE_API_KEY")
    if not api_key:
        raise ValueError("Missing SCRAPINGBEE_API_KEY in .env")
    return api_key


def main() -> None:
    api_key = get_api_key()


if __name__ == "__main__":
    main()

That's it. Your project is set up, your API key is safely stored, and you're ready to start sending requests.

Step 3: Send a request to Kickstarter API

Now let's actually fetch some data. We'll request the Kickstarter Technology category page through ScrapingBee, render the JavaScript, and use AI extraction to turn the campaign list into structured JSON.

Here's a Python example with comments so you can see what's going on:

import json
import os
from typing import Any

import requests
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

SCRAPINGBEE_API_URL = "https://app.scrapingbee.com/api/v1/"
KICKSTARTER_TECH_URL = "https://www.kickstarter.com/discover/categories/technology"


def get_api_key() -> str:
    # Read API key from environment
    api_key = os.getenv("SCRAPINGBEE_API_KEY")
    if not api_key:
        raise ValueError("Missing SCRAPINGBEE_API_KEY in .env")
    return api_key


def build_ai_rules() -> dict[str, Any]:
    # Define what data we want to extract using AI
    return {
        "campaigns": {
            "type": "list",
            "description": "ongoing Kickstarter technology campaigns visible on the page",
            "output": {
                "title": {
                    "type": "string",
                    "description": "project title",
                },
                "url": {
                    "type": "string",
                    "description": "link to the project",
                },
                "funded_percentage": {
                    "type": "string",
                    "description": "funding percentage like 250%",
                },
                "backers": {
                    "type": "string",
                    "description": "number of backers",
                },
                "days_left": {
                    "type": "string",
                    "description": "remaining time left in the campaign",
                },
            },
        }
    }


def fetch_kickstarter_campaigns(api_key: str) -> list[dict[str, str]]:
    # Build request parameters for ScrapingBee
    params = {
        "api_key": api_key,
        "url": KICKSTARTER_TECH_URL,
        "render_js": "true",        # render JavaScript-heavy page
        "premium_proxy": "true",    # use better proxies to avoid blocks
        "wait_for": ".js-project-group", # wait for project list to load
        "ai_extract_rules": json.dumps(build_ai_rules()),  # convert rules to JSON string
        # Optionally, you can ask the AI to focus on the specific
        # part of the page by adding:
        # "ai_selector": ".js-project-group"
    }

    # Send request to ScrapingBee API
    response = requests.get(SCRAPINGBEE_API_URL, params=params, timeout=60)
    response.raise_for_status()  # raise error for bad responses

    # Parse JSON response
    data: dict[str, Any] = response.json()

    # Extract campaigns list safely
    campaigns = data.get("campaigns", [])

    # Normalize data (make sure all fields exist and are strings)
    normalized_campaigns: list[dict[str, str]] = []
    for campaign in campaigns:
        normalized_campaigns.append(
            {
                "title": str(campaign.get("title", "")).strip(),
                "url": str(campaign.get("url", "")).strip(),
                "funded_percentage": str(campaign.get("funded_percentage", "")).strip(),
                "backers": str(campaign.get("backers", "")).strip(),
                "days_left": str(campaign.get("days_left", "")).strip(),
            }
        )

    return normalized_campaigns


def main() -> None:
    api_key = get_api_key()

    # Fetch campaign data
    campaigns = fetch_kickstarter_campaigns(api_key)

    # Print result as formatted JSON
    print(json.dumps({"campaigns": campaigns}, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()

What's happening here:

• ScrapingBee loads the Kickstarter page in a real browser (render_js=true) so all dynamic content appears correctly
• it waits until the project list is actually visible (wait_for=".js-project-group") instead of guessing timing
• premium proxies help you avoid blocks and rate limits
• AI reads the page and extracts campaign data based on your rules
• you get JSON back, no HTML parsing needed

Note that this example uses premium proxies, JS rendering, and AI extraction, so it's more expensive than a basic request. But believe me, it's worth it as Kickstarter is not too fond of being scraped, plus JavaScript rendering is required to fetch the actual data.

Run it:

uv run python main.py

No field-level selectors, no reverse engineering internal endpoints. Nice!

If you want to see another real-world scraper built the same way, check our Pitchbook scraper.

Step 4: Process and store the data

At this point, you already have structured data coming back from ScrapingBee, which is the hardest part done.

Instead of dealing with raw HTML or brittle selectors, you're working with clean JSON that contains exactly the fields you asked for. Now it's just a matter of handling that data and storing it somewhere useful.

import csv
import json
import os
from typing import Any

import requests
from dotenv import load_dotenv

CSV_OUTPUT_PATH = "kickstarter_campaigns.csv"

# ... other constants and existing code ...

def save_campaigns_to_csv(campaigns: list[dict[str, str]], output_path: str) -> None:
    # Define CSV columns (must match keys in campaign dicts)
    fieldnames = ["title", "url", "funded_percentage", "backers", "days_left"]

    # Open file for writing (UTF-8 to support all characters)
    with open(output_path, "w", newline="", encoding="utf-8") as csv_file:
        # Create a DictWriter to map dict keys to CSV columns
        writer = csv.DictWriter(csv_file, fieldnames=fieldnames)

        # Write header row (column names)
        writer.writeheader()

        # Write all campaign rows at once
        writer.writerows(campaigns)


def main() -> None:
    # Get API key from environment
    api_key = get_api_key()

    # Fetch Kickstarter campaign data via ScrapingBee
    campaigns = fetch_kickstarter_campaigns(api_key)

    # Print JSON output for debugging or quick inspection
    print(json.dumps({"campaigns": campaigns}, indent=2, ensure_ascii=False))

    # Save structured data to CSV file
    save_campaigns_to_csv(campaigns, CSV_OUTPUT_PATH)

    # Confirm how many records were saved
    print(f"\nSaved {len(campaigns)} campaigns to {CSV_OUTPUT_PATH}")


if __name__ == "__main__":
    # Entry point of the script
    main()

Here's what's happening in the code:

• we take the list of campaign objects returned by the API
• define a consistent set of fields (title, URL, funding, backers, etc.)
• write everything into a CSV file with proper headers
• save it locally so it can be reused later

And here's the sample result:

title,url,funded_percentage,backers,days_left
Rewindpix: A Non-disposable Digital "Film" Camera,https://www.kickstarter.com/projects/rewindpix/rewindpix-a-non-disposable-digital-film-camera?ref=discovery_category&total_hits=57360&category_id=333,3653%,"3,287",40 days left
"The Supertiny - 100W power, world's smallest size.",https://www.kickstarter.com/projects/rollingsquare/smallest-100w-gan-usb-c-laptop-charger-super-tiny-fast-power?ref=discovery_category&total_hits=57360&category_id=337,2060%,"4,373",32 days left

From here, you can do a lot more with the data:

• analyze funding trends across categories
• track how campaigns evolve over time
• feed the data into dashboards or internal tools
• integrate it into larger data pipelines or automation workflows

This is where scraping Kickstarter actually becomes useful. You're not just collecting data, you're turning it into something you can work with.

Ready to take Kickstarter scraping to the next level?

By now, you've seen how to scrape Kickstarter data without fighting HTML, reverse engineering endpoints, or getting blocked every five minutes.

Instead of relying on fragile setups, you can use ScrapingBee to handle the heavy lifting:

• proxy rotation and anti-bot protection
• JavaScript rendering for dynamic pages
• AI-powered data extraction
• clean, structured output ready to use

Whether you're building a side project, doing market research, or plugging this into a larger data pipeline, this approach scales way better than manual scraping.

Try it yourself! Get started for free with 1,000 credits and no credit card required.

Set it up, run your first request, and you're already ahead of most scraping setups.

Frequently asked questions (FAQs)

Can I scrape Kickstarter without an API?

Yeah, you can scrape Kickstarter manually by parsing HTML or calling internal endpoints, but it's fragile and easy to break. Using an API like ScrapingBee helps you avoid blocks, handle rate limits, and get consistent results without babysitting your scraper.

Is it legal to scrape Kickstarter data?

Scraping publicly available data is generally fine, but you should always respect Kickstarter's terms of service and avoid abusive behavior. Use APIs responsibly, keep request rates reasonable, and don't try to bypass protections in a harmful way.

What types of Kickstarter data can I access?

You can access a wide range of data, including project titles, descriptions, funding goals, pledged amounts, number of backers, campaign duration, and updates. With the right setup, most publicly visible campaign data can be extracted in a structured format.

Can I automate regular data collection?

Yes, you can schedule requests to run at intervals and keep your dataset up to date. Combined with a tool like ScrapingBee, this lets you build automated pipelines that track campaign performance, monitor trends, or feed data into dashboards and internal tools.