A short tutorial for pulling Capterra software reviews (rating, pros, cons, sub-ratings, reviewer industry and company size) into Python with the Capterra Reviews Scraper on Apify.
Disclosure: I built this actor; it's a paid tool on Apify ($1.50 per 1,000 reviews). The code in this repo is MIT-licensed.
capterra.com sits behind a Cloudflare challenge that needs a real browser. Capterra, GetApp and Software Advice are all Gartner properties and share one review database, so the actor reads it over plain HTTPS from Software Advice instead. No headless browser, about 25 reviews per request, and every review keeps its original Capterra ID (Capterra___7209621).
pip install apify-clientfrom apify_client import ApifyClient
client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("rel8ble/capterra-reviews-scraper").call(run_input={
"products": ["https://www.capterra.com/p/135003/Slack/", "Asana"],
"maxReviewsPerProduct": 20,
"sortBy": "newest",
})
for review in client.dataset(run["defaultDatasetId"]).iterate_items():
print(review["rating"], review["productName"], review["title"])example.py is the runnable version: it filters to 1-2 star reviews and writes a CSV.
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('rel8ble/capterra-reviews-scraper').call({
products: ['Asana'], maxReviewsPerProduct: 20,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.length, 'reviews');One call runs the actor and returns the results. Use format=csv for a spreadsheet, format=json for code:
curl -X POST "https://api.apify.com/v2/acts/rel8ble~capterra-reviews-scraper/run-sync-get-dataset-items?format=csv" -H "Authorization: Bearer <YOUR_APIFY_TOKEN>" -H "Content-Type: application/json" -d '{"products":["https://www.capterra.com/p/135003/Slack/"],"maxReviewsPerProduct":100,"sortBy":"newest","starRatings":["1","2"]}' -o results.csvThe sync endpoint waits up to 5 minutes; for bigger runs use the clients above (they wait as long as needed). example.js is a runnable Node.js version that writes capterra_reviews.csv (npm install, then APIFY_TOKEN=... node example.js).
- n8n: import
n8n/workflow.json(Workflows > Import from file), replaceYOUR_APIFY_TOKEN, and add a Google Sheets "Append or update row" node after it, matched onreviewIdso weekly runs don't create duplicates. It uses n8n's built-in HTTP Request node, so no community node is needed (the official Apify node,@apify/n8n-nodes-apify, works too). - Make: Apify app > "Run an Actor" (actor
rel8ble/capterra-reviews-scraper, wait for finish on, input JSON as above) > Apify "Get Dataset Items" > Google Sheets "Add a Row" or your CRM module. - Zapier: Apify app > "Run Actor" action (or the "Finished Actor Run" trigger on a scheduled run) > "Fetch Dataset Items" > Google Sheets / HubSpot / Airtable.
- Google Sheets only: run it from the Store page form, download CSV and import. Useful columns: productName, rating, title, pros, cons, date, reviewerIndustry, reviewerCompanySize.
- AI agents: the Apify MCP server (https://mcp.apify.com) lets Claude, ChatGPT or Cursor call
rel8ble/capterra-reviews-scraperas a tool.
| Field | Default | What it does |
|---|---|---|
products |
- | Capterra, GetApp or Software Advice product URLs, or exact product names ("Asana") |
maxReviewsPerProduct |
100 | 0 = every matching review |
sortBy |
newest |
newest or recommended |
starRatings |
all | e.g. ["1", "2"] for negative reviews |
companySizes |
all | Codes A (self-employed) to I (10,001+ employees) |
keyword |
- | Only reviews that mention this word or phrase |
reviewedAfter |
- | YYYY-MM-DD; with newest sorting the run stops early |
{
"productName": "monday.com AI Work Platform",
"reviewId": "Capterra___7216113",
"title": "Great time saver",
"rating": 5,
"pros": "The ability to enhance your automations and process using LLMs.\nYou can also have it setting tasks based on certain criteria which is great",
"cons": "Nothing it was all very easy to use. Like most AI you need to understand how to talk to it but once you have that its very easy to ise",
"easeOfUseRating": 5,
"customerServiceRating": 4,
"featuresRating": 5,
"valueForMoneyRating": 5,
"date": "2026-09-20",
"reviewerName": "Philip D.",
"reviewerIndustry": "Marketing and Advertising",
"reviewerCompanySize": "201-500 employees",
"reviewerVerified": true,
"timeUsed": "More than 2 years",
"usageFrequency": "Daily",
"productOverallRating": 4.57,
"productTotalReviews": 6116
}Every row also carries the product summary (overall rating, star breakdown, average sub-ratings), plus the vendor response and its date when the vendor replied.
- Competitor research: pull every 1-2 star review of a rival and group the
consby theme. - Review monitoring: schedule daily with
sortBy: "newest"andreviewedAfterset to yesterday;reviewIdis stable, so dedupe across runs. - Sales intel: filter by
companySizesto see what enterprise vs. SMB buyers complain about. - LLM datasets: clean pros/cons text with ratings as labels.
$1.50 per 1,000 reviews, pay per result. No start fee, no monthly rental. Duplicates, filtered-out reviews and products that aren't found cost nothing. Apify's free plan includes $5 of monthly credit.
- Coverage is the Software Advice catalog. Most Capterra products are there (Slack, Asana, Jira, monday.com, Notion, QuickBooks Online, Docusign, Canva, Shopify, Microsoft Teams, Google Workspace all worked in testing), but a few big vendors aren't, for example Salesforce Sales Cloud, HubSpot CRM, Zendesk and Mailchimp. Those return "product not found" with close matches, free of charge.
- Product names are matched exactly, never guessed. URLs are the most reliable input.
- Reviewer job titles aren't published by the source; you get industry and company size instead.
- Reviewer names are first name + last initial, as Capterra shows them.
- Actor on the Apify Store: https://apify.com/rel8ble/capterra-reviews-scraper
- Apify Python client docs: https://docs.apify.com/api/client/python
MIT