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Software Reviews Scraper

Type a product name like "Slack" or paste a mix of links and get its reviews from Capterra (and optionally Software Advice), Google Play and the App Store in one dataset, one row shape: rating, title, text, pros, cons, date, the reviewer's role and company size where the site has them. $1.79 per 1,000 reviews on the free plan, and you pay only for the rows you get.

$1.79 per 1,000 reviews on the Free plan, down to $1.16 on higher plans

Why this actor

Drop-in input

Drop-in input. Input written for zen-studio/software-review-scraper (query, maxResults, platforms, sort) works as it is, at $1.79 instead of $4.99 per 1,000 reviews. 8.0% of that actor's runs failed (Apify Store, 30 days to 2026-10-31). Its G2, TrustRadius, Gartner and Trustpilot platforms are not sources here (TrustRadius has its own actor).

As stated in the actor’s README. The full list of accepted input names is on the Store page.

What you get

FieldWhat it holds
platformcapterra, softwareadvice, googleplay or appstore.
reviewIdThe platform's own ID with a platform prefix. Stable between runs, so weekly exports can be deduped.
dateReview date, ISO format.
title, text, pros, consWhat the reviewer wrote. Pros and cons exist on Capterra and Software Advice only.
ratingOverallOverall rating (1 to 5).
ratingsSub-ratings (ease of use, support, value and so on) where the platform has them.
reviewerRole, reviewerIndustry, reviewerCompanySizeBusiness context from Capterra and Software Advice. Empty for app stores.
vendorResponseThe vendor's public reply, when there is one.
productName, rating, review count, category, vendor and website of the product on that platform.
extras.inputProductThe name you searched for, so you can group rows when you pass several products.

Company size always uses one set of labels, whatever the source: Self-employed, 1-10 employees, 11-50 employees, 51-200 employees, 201-500 employees, 501-1000 employees, 1001-5000 employees, 5001-10000 employees, 10001+ employees.

Sample summary row (free):

Sample row (from the README example; long text shortened, lists cut to two items):

{
  "rowType": "review",
  "platform": "googleplay",
  "reviewId": "GooglePlay___a1b2c3d4-0000-0000-0000-000000000000",
  "productName": "Slack",
  "date": "2026-10-14",
  "title": null,
  "text": "Notifications are finally reliable on my phone.",
  "ratingOverall": 4,
  "reviewerCompanySize": null,
  "extras": {
    "inputProduct": "Slack"
  }
}

Price

Live Apify price on 2026-10-31, per 1,000 rows, by Apify plan. You pay only for rows delivered; error and summary rows marked free in the README cost nothing. Plus $0.001 per GB of memory when a run starts.

Per 1,000FreeBronzeSilverGoldPlatinumDiamond
Review (main)$1.79$1.34$1.25$1.16$1.16$1.16

Use cases

Run it from code or an AI agent

Actor ID arcadium/software-reviews. Set APIFY_TOKEN to your Apify API token. The input below is the actor’s prefilled example; see the guide for every field.

curl
curl -X POST "https://api.apify.com/v2/acts/arcadium~software-reviews/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"products":["Slack"],"sources":["capterra","googleplay","appstore"],"maxReviewsPerSource":20}'
JavaScript
// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/software-reviews').call({
  "products": [
    "Slack"
  ],
  "sources": [
    "capterra",
    "googleplay",
    "appstore"
  ],
  "maxReviewsPerSource": 20
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
Python
# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/software-reviews").call(run_input={
    "products": ["Slack"],
    "sources": ["capterra", "googleplay", "appstore"],
    "maxReviewsPerSource": 20,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)
MCP
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/software-reviews",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}
LangChain
# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/software-reviews")
rows = tool.invoke({"run_input": {
    "products": ["Slack"],
    "sources": ["capterra", "googleplay", "appstore"],
    "maxReviewsPerSource": 20,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])
LlamaIndex
# pip install llama-index-readers-apify
import os
from llama_index.core import Document, VectorStoreIndex
from llama_index.readers.apify import ApifyActor

reader = ApifyActor(os.environ["APIFY_TOKEN"])
documents = reader.load_data(
    actor_id="arcadium/software-reviews",
    run_input={
        "products": ["Slack"],
        "sources": ["capterra", "googleplay", "appstore"],
        "maxReviewsPerSource": 20,
    },
    dataset_mapping_function=lambda item: Document(text=str(item.get("text") or ""), metadata={"url": item.get("url")}),
)
index = VectorStoreIndex.from_documents(documents)
CrewAI
# pip install crewai crewai-tools langchain-apify   (reads APIFY_API_TOKEN from the environment)
from crewai import Agent
from crewai_tools import ApifyActorsTool

tool = ApifyActorsTool(actor_name="arcadium/software-reviews")
researcher = Agent(
    role="Research analyst",
    goal="Answer with data collected by the tool",
    backstory="Uses public web data.",
    tools=[tool],
)
n8n
{
  "nodes": [
    {
      "name": "Run arcadium/software-reviews",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~software-reviews/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"products\":[\"Slack\"],\"sources\":[\"capterra\",\"googleplay\",\"appstore\"],\"maxReviewsPerSource\":20}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}
Google Sheets
// Extensions > Apps Script. Store your token once: Project Settings > Script properties > APIFY_TOKEN.
function importLastRun() {
  const token = PropertiesService.getScriptProperties().getProperty('APIFY_TOKEN');
  const url = 'https://api.apify.com/v2/acts/arcadium~software-reviews/runs/last/dataset/items?status=SUCCEEDED&clean=true&format=json';
  const res = UrlFetchApp.fetch(url, { headers: { Authorization: 'Bearer ' + token } });
  const rows = JSON.parse(res.getContentText());
  if (!rows.length) return;
  const keys = Object.keys(rows[0]);
  const values = rows.map((r) => keys.map((k) => (typeof r[k] === 'object' ? JSON.stringify(r[k]) : r[k])));
  const sheet = SpreadsheetApp.getActiveSheet();
  sheet.clearContents();
  sheet.getRange(1, 1, 1, keys.length).setValues([keys]);
  sheet.getRange(2, 1, values.length, keys.length).setValues(values);
}

Limitations

FAQ

How fresh is the data? It is read when you run the actor, so it matches what the sites show at that moment.

Can I get only new reviews since my last run? Yes. Turn on onlyNewReviews and keep the same stateKey. Change the key to start over.

A source returned nothing. Why? Look at the error rows and at STATS.sources in the key-value store. no_match means no confident match for that name, which costs nothing.

Something looks wrong in the output. Who do I tell? Open an issue on the actor page with the run link. We read them all.

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