# Capterra 0.9$πŸ’° Reviews Products, Categories & Compare Scraper (`memo23/capterra-scraper`) Actor \[Only 0.9$πŸ’°] Capterra all-in-one scraper: product, reviews, category & compare URLs in one actor. Flat per-review rows (100% field fill: reviewer name, company size, recommendation score, incentivization), Capterra awards & badges, sentiment breakdown, structured integrations. Pure HTTP, no browser - **URL**: https://apify.com/memo23/capterra-scraper.md - **Developed by:** [Muhamed Didovic](https://apify.com/memo23) (community) - **Categories:** Developer tools, Automation, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.90 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Capterra Software Reviews, Products & Categories Scraper ### How it works

How it works

All-in-one Capterra.com scraper β€” paste any mix of URL kinds in the same input list and the actor auto-classifies each one: | Input | Row(s) emitted | |---|---| | **Product URL** β€” `/p/{id}/{slug}/` | 1 product row: name, description, application category, rating, vendor, pricing, features, screenshots, alternatives, review snippets, **awards & badges (Best Value, Shortlist, Ease of Use)**, **structured integrations** (with logos + Capterra URLs), **per-sub-rating averages with reviewer counts**, **Capterra-computed sentiment breakdown** | | **Reviews URL** β€” `/p/{id}/{slug}/reviews/` | **N review rows** β€” one per individual review (`rowType: "review"`). Each row carries a nested `productAggregate` object with the thread-level rating distribution, sub-rating averages, total reviews, etc. No separate summary row. | | **Category URL** β€” `/{slug}-software/` (e.g. `/project-management-software/`) | **N category-product rows** β€” one per product in the category (`rowType: "category-product"`). Each row carries a nested `categoryAggregate` with the category name, description, total count, and related categories. Same flat-with-nested-aggregate pattern as reviews. | | **Compare URL** β€” `/compare/{ids}/{slug-a}-vs-{slug-b}/` | 1 compare row: side-by-side products with ratings, pricing, deployments | Bare numeric product IDs (`137005`) and bare category slugs (`project-management`) also work. JSON + CSV. > **Reviews are flat with the aggregate denormalized into every row.** Every dataset row from a reviews URL is a single review. The thread-level aggregate (rating distribution, sub-rating averages, total reviews) lives under each row's `productAggregate` field β€” duplicated on every review row from the same URL, but that keeps each row self-contained and makes billing trivial: one row = one paid `outputrecord` event. > Pure HTTP. No Puppeteer, no Playwright, no headless Chromium, no third-party Cloudflare-bypass service. --- Capterra is **Cloudflare-gated** with stronger deep-page protection than most B2B-software sites. The actor breaks past it without a browser via a session-aware HTTP pattern: 1. **Warm session.** Open one sticky-IP residential proxy session, hit a known-good product page, retry until 200. Cloudflare sets `__cf_bm` + `_cfuvid` cookies on success β€” captured automatically by the impit cookie jar. 2. **Iterate URLs.** Reuse the same Impit instance + sticky IP for every subsequent URL. Cookie + IP persistence pushes per-URL pass rate from a cold ~25% to a warm ~80%, with per-URL retry bringing cumulative success to ~95%. 3. **Auto-refresh.** On 3 consecutive 403s, drop the session and re-warm with a fresh IP. No human intervention needed. Every page is then parsed via JSON-LD (the rock-solid `SoftwareApplication` schema + `BreadcrumbList`) plus targeted Cheerio walks of the visible HTML for sections (Features, Pricing, Integrations, Support, Alternatives, User reviews). No DOM execution; no DOMContentLoaded wait. --- ### Input Field | Type | Required | Notes --- | --- | --- | --- `startUrls` | `string[]` | yes | Any mix of product / reviews / category / compare URLs (or bare product IDs / category slugs). Auto-classified. `maxItems` | `integer` | no | Safety cap on **total dataset rows**, not URLs. A reviews URL with 100 reviews emits 100 review rows; a category URL with 50 products emits 50 category-product rows. Default `1000`. Free-tier users are capped at `100`. `maxReviewsPerProduct` | `integer` | no | Max individual review rows emitted per reviews URL. Default `100`, `0` = no limit. `maxProductsPerCategory` | `integer` | no | Max individual category-product rows emitted per category URL. Default `50`, `0` = no limit. `maxRequestRetries` | `integer` | no | Per-URL retry budget inside the warm session. Default `8`. `proxy` | object | no | Apify residential proxy recommended (US country). Required because Capterra rejects datacenter IPs. #### Example input ```json { "startUrls": [ "https://www.capterra.com/p/137005/SmartPM/", "https://www.capterra.com/p/137005/SmartPM/reviews/", "https://www.capterra.com/project-management-software/", "https://www.capterra.com/compare/137005-229614/SmartPM-vs-Notion/" ], "maxItems": 50, "maxReviewsPerProduct": 100, "maxProductsPerCategory": 50, "proxy": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US" } } ```` That yields **90 rows** on SmartPM: 1 product + 46 individual review rows (each with `productAggregate` nested) + 42 category-product rows from `/project-management-software/` (each with `categoryAggregate` nested) + 1 compare. Use `maxProductsPerCategory` / `maxReviewsPerProduct` to cap the per-URL fan-out. *** ### Output schema Four row shapes, distinguished by the `rowType` field: `product`, `review`, `category-product`, `compare`. #### `rowType: "product"` ```jsonc { "rowType": "product", "productUrl": "https://www.capterra.com/p/137005/SmartPM/", "productId": "137005", "name": "SmartPM", "description": "SmartPM is an AI-powered Automated Project Controlsβ„’ platform built by construction for construction…", "applicationCategory": "BusinessApplication", "categories": ["Construction Software", "Project Management Software"], "rating": { "average": 4.9, "count": 46, "bestRating": 5 }, "vendor": { "name": "SmartPM", "websiteUrl": null }, "pricingStarts": "$49", "logoUrl": "https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/7c88b5d2-…", "screenshotUrls": [ /* up to ~22 imgix URLs */ ], "features": [ /* visible feature list */ ], "deployments": [ "Cloud", "Web-Based" ], "trainingOptions": [ "Live Online", "Documentation", "Webinars" ], "supportOptions": [ "Email/Help Desk", "Phone Support", "Chat" ], "customerTypes": [ "Small Business", "Mid-size", "Enterprise" ], "reviewSnippets": [ { "title": "SmartPM, an amazing tool for schedule analysis", "pros": "I had a 2 year experience using SmartPM…", "cons": "Some advanced features have a learning curve…", "overallRating": null, "reviewer": { "name": null, "jobTitle": null, "industry": null, "companySize": null }, "source": "capterra" } /* up to 10 surfaced on the product page */ ], "alternatives": [ { "productId": "183213", "productUrl": "https://www.capterra.com/p/183213/Contractor-Foreman/", "name": "Contractor Foreman", "rating": { "average": null, "count": null, "bestRating": null }, "vendor": null, "logoUrl": null, "pricingStarts": null, "sponsored": null } /* + ~4 more */ ], "breadcrumbs": [ { "name": "Capterra", "url": "https://www.capterra.com/" }, { "name": "Construction Software", "url": "https://www.capterra.com/construction-software/" }, { "name": "SmartPM", "url": "https://www.capterra.com/p/137005/SmartPM/" } ], // ── RSC-derived rich data (from Capterra's embedded product JSON) ── "awards": [ { "title": "2025 Capterra Best Value for Money", "badgeUrl": "/public-bx-capterra-v0/badge-best-value.png", "badgeAlt": "Best Value Badge", "additionalInfo": "and in", "linkText": "+4 categories", "linkAction": "best-value" } ], "valueBadges": [ { "name": "Business Intelligence", "year": "2025", "categorySlug": "business-intelligence-software" }, { "name": "Construction Management", "year": "2025", "categorySlug": "construction-management-software" }, { "name": "Data Analysis", "year": "2025", "categorySlug": "data-analysis-software" }, { "name": "Project Planning", "year": "2025", "categorySlug": "project-planning-software" } ], "shortlistBadges": [], // populated when product is in Capterra Shortlist reports "easeOfUseBadges": [], // populated when product holds "Best Ease of Use" awards "integrations": [ { "name": "Microsoft Power BI", "id": "e3df9c2d-1567-427c-acb2-a6d200b52931", "logo": "https://gdm-catalog-fmapi-prod.imgix.net/ProductLogo/e48bb429-…png", "href": "https://www.capterra.com/p/176586/Power-BI/", "reviewCount": 1878 } /* + 7 more */ ], "usedFor": [ { "name": "Construction Scheduling", "slug": "construction-scheduling-software" }, { "name": "Construction Management", "slug": "construction-management-software" } /* + 6 more */ ], "pricing": { "startingPrice": null, // raw value β€” may be blank when not disclosed "startingPriceCurrency": "$$", "startingPriceUnit": "", "pricingModel": "Other", "paymentFrequency": "Per Month", "hasFreeTrial": true, "hasFreeVersion": false }, "subRatingAverages": { // Capterra's authoritative per-sub-rating means + counts "easeOfUse": { "rating": 4.7, "reviews": 46 }, "customerService": { "rating": 5, "reviews": 45 }, "valueForMoney": { "rating": 4.9, "reviews": 42 } }, "reviewSentiment": { // Capterra-computed sentiment breakdown "totalReviews": 46, "overallRating": 4.87, "positive": { "count": 96, "percentage": 96 }, "neutral": { "count": 4, "percentage": 4 }, "negative": { "count": 0, "percentage": 0 } }, "scrapedAt": "2026-05-12T13:35:00.412Z" } ``` #### `rowType: "review"` (one per individual review) Every dataset row from a reviews URL is a single review. Product info (`productId`, `productName`, `reviewsUrl`, `productUrl`, `reviewUrl`) and the **thread-level aggregate** (`productAggregate`) are denormalized onto every row, so each row is fully self-contained β€” no joining required for CSV consumers. Per-review fields come directly from Capterra's embedded RSC JSON (one structured object per review, decoded brace-by-brace), giving **100% fill on every field the source provides**: reviewer name / job title / industry / company size / profile pic / LinkedIn verification, all four sub-ratings, recommendation score (0-10), incentivization status, plus structured `alternativeProducts[]` and `switchedProducts[]` arrays for the competitor products each reviewer mentioned, plus the vendor's reply when one exists. The actor **auto-paginates** through every review page (following each page's `?page=N` link). `productAggregate.totalReviews` and `productAggregate.pagesScraped` reflect the full thread up to `maxReviewsPerProduct`. On SmartPM that's 46 reviews across 2 pages; on bigger products it can be hundreds across 10+ pages. ```jsonc { "rowType": "review", "reviewsUrl": "https://www.capterra.com/p/137005/SmartPM/reviews/", "productId": "137005", "productName": "SmartPM", // ── Per-review fields ──────────────────────────────────────────── "reviewId": "Capterra___6325867", // stable primary key "reviewUrl": "https://www.capterra.com/p/137005/x/#Capterra___6325867", "productUrl": "https://www.capterra.com/p/137005/x/", "title": "SmartPM Elevates Company Schedule Performance & Health", "overallRating": 5, "recommendationScore": 10, // 0–10 NPS, when provided "subRatings": { "easeOfUse": 5, "customerService": 5, "features": 5, "valueForMoney": 5 }, "text": "Our SmartPM experience has been fantastic - integration, support, implementation - everything!", "pros": "SmartPM has already proved itself to be invaluable to our company…", "cons": "Some advanced features have a learning curve…", "adviceToOthers": null, // Optional "Advice to others" text "reasonsForChoosing": null, // "Reasons for Choosing" (~20% fill) "reasonsForSwitching": null, // "Reasons for Switching" (~11% fill) "switchedFrom": "Deltek Acumen", // Name of previous tool (= switchedProducts[0].productName) "switchedProducts": [ // Full structured list (rare β€” 4% fill on SmartPM) { "productId": "10001903", "productSlug": "Deltek-Acumen", "productName": "Deltek Acumen" } ], "alternativeProducts": [ // Competitors the reviewer ALSO evaluated (9% fill on SmartPM) { "productId": "10012584", "productSlug": "Schedule-Validator", "productName": "Schedule Validator" } ], "publishedAt": "May 31, 2024", "timeUsed": "1-2 years", // "2+ years" / "6-12 months" / "Less than 6 months" / "1-2 years" "frequencyOfUse": null, "reviewer": { "name": "John M.", // null when reviewer is anonymous ("Verified User") "jobTitle": "Scheduler", "industry": "Construction", "companySize": "201-500 employees", // "Self-employed" / "1-10 employees" / … / "10,001+ employees" "profilePicUrl": "https://reviews.capterra.com/cdn/profile-images/linkedin/e7e3268d2b...jpeg", "verifiedLinkedIn": false, "validationsPassed": ["ProofOfLink"] // Methods Capterra used to validate the reviewer }, "verified": true, // Capterra "isValidated" flag "incentivization": "vendor-referred-incentive", // "none" | "vendor-referred" | "nominal-gift" | "vendor-referred-incentive" | "unknown" "source": "capterra:incentivized", // back-compat string form of `incentivization` "reviewSourceTooltip": "Vendor Referred - Incentive Offered: This reviewer was invited by the software vendor…", "vendorResponse": { // null when the vendor didn't reply (typical) "date": "July 11, 2022", "text": "Thanks Joe! It's been great working with you!" }, // ── Thread-level aggregate (same on every review row from the same URL) ── "productAggregate": { "rating": { "average": 4.9, "count": 46, "bestRating": 5 }, "subRatingAverages": { "easeOfUse": 4.7, "customerService": 5, "features": 4.8, "valueForMoney": 4.9 }, "ratingDistribution": [ { "stars": 5, "count": 21, "percentage": 84 }, { "stars": 4, "count": 2, "percentage": 8 }, { "stars": 3, "count": 2, "percentage": 8 }, { "stars": 2, "count": 0, "percentage": 0 }, { "stars": 1, "count": 0, "percentage": 0 } ], "commonPros": [], "commonCons": [], "totalReviews": 46, "pagesScraped": 2 }, "scrapedAt": "2026-05-12T10:55:14.123Z" } ``` #### `rowType: "category-product"` (one per product listed in the category) Same flat-with-nested-aggregate pattern as reviews. Every dataset row from a category URL is a single product listed in that category. The category-level aggregate (name, slug, description, total count, related categories) is denormalized under `categoryAggregate` on every row. ```jsonc { "rowType": "category-product", "categoryUrl": "https://www.capterra.com/project-management-software/", "categorySlug": "project-management", // ── Per-product fields (different on every row) ────────────────── "productId": "120390", "productUrl": "https://www.capterra.com/p/120390/Teamwork-Projects/", "name": "Teamwork Projects", "description": null, "rating": { "average": 4.5, "count": 1234, "bestRating": 5 }, "vendor": null, "logoUrl": "https://gdm-catalog-fmapi-prod.imgix.net/…", "pricingStarts": null, "sponsored": false, // ── Category-level aggregate (same on every category-product row from the same URL) ── "categoryAggregate": { "categoryUrl": "https://www.capterra.com/project-management-software/", "categorySlug": "project-management", "categoryName": "Project Management Software", "description": "Project management software helps teams plan, track, and deliver work …", "totalProductsOnPage": 42, "relatedCategories": [ { "name": "Task Management Software", "url": "https://www.capterra.com/task-management-software/" } /* up to 30 */ ] }, "scrapedAt": "2026-05-12T..." } ``` #### `rowType: "compare"` ```jsonc { "rowType": "compare", "compareUrl": "https://www.capterra.com/compare/137005-229614/SmartPM-vs-Notion/", "products": [ { "productId": "137005", "name": "SmartPM", "rating": null, "vendor": null, ... }, { "productId": "229614", "name": "Notion", "rating": null, "vendor": null, ... } ], "summary": { "ratings": [ { "average": 4.9, "count": 46, "bestRating": 5 }, { "average": 4.7, "count": 5000, "bestRating": 5 } ], "pricingStarts": [ "$49", "Free" ], "vendors": [ "SmartPM", "Notion Labs" ], "deployments": [ ["Cloud"], ["Cloud", "Mobile"] ] } } ``` > **Compare rows are populated by per-product fetch.** Capterra's `/compare/...` pages are 100% client-rendered SPAs β€” their HTML carries no product data. Instead we read the product IDs from the URL itself and fetch each compared product's own page through the warm session, giving full ratings + vendor + pricing + deployments per side-by-side product. Cost: N extra HTTP calls per compare URL (where N = number of products compared, usually 2–3). *** ### Pricing Pay-per-event. **Both reviews and category products are billed flat** β€” every review row is one `outputrecord`, every category-product row is one `outputrecord`. Cost = `rows Γ— outputrecord-price`. No nested-array arithmetic. Event | When | Suggested rate \--- | --- | --- `outputrecord` | Once per dataset row pushed β€” `product`, `review`, `category-product`, `compare`. The thread/category aggregate is nested INSIDE each flat row, not charged separately. | configured on the actor page `additional-data` | Once per nested item inside a `product` or `compare` row (features, screenshots, alternatives, breadcrumbs, compared products). Reviews and category-products are NEVER billed here β€” each is its own paid `outputrecord` row. | **$0.75 per 1,000 items** ($0.00075 each) #### What counts as `additional-data` Only the URL kinds that still keep nested arrays bill here. Reviews and category products never do. Row type | Items billed as `additional-data` \--- | --- **Product row** | every feature + screenshot + review snippet + alternative + breadcrumb category **Compare row** | every compared product *** ### What makes this richer than the competition We surveyed all 16 Capterra actors on the Apify marketplace (May 2026). Most cover one URL surface only: Capability | Competitor actors | **This actor** \--- | --- | --- Product + reviews + category + compare in **one actor** | ❌ (split across 4+ SKUs) | βœ… URL auto-classification (paste any mix) | ❌ | βœ… Pure HTTP, no headless browser | mixed (some use Playwright) | βœ… No paid third-party CF-bypass service | mixed | βœ… Cookie-warmed sticky session pattern | ❌ | βœ… Per-product alternatives + screenshots in product row | ❌ (separate calls) | βœ… Flat-per-review billing (1 review = 1 paid row, no nested-array math) | mixed | βœ… Thread-level aggregate (rating distribution + sub-rating averages) **on every review row** | ❌ (none surface this at all) | βœ… RSC-stream embedded review JSON extraction β€” 100% fill on `reviewId`, `companySize`, `recommendationScore`, `incentivization`, etc. | partial (some fields 0%) | βœ… Capterra **awards & badges** (Best Value, Shortlist, Ease-of-Use) per product | ❌ | βœ… **Structured integrations** per product (name + logo + Capterra URL + review count, not just feature strings) | ❌ | βœ… Capterra-computed **review sentiment breakdown** (positive / neutral / negative %) | ❌ | βœ… Per-sub-rating averages **with reviewer counts** (more precise than mean alone) | partial | βœ… The top marketplace actor (`focused_vanguard/multi-platform-reviews-scraper`, 220 users) covers multiple sources (Capterra + G2 + Trustpilot + Gartner + Software Advice + Reddit) but only emits review data. We do everything Capterra exposes in one actor β€” products, reviews, categories, comparisons β€” without crossing into other platforms. *** ### Notes & limitations - **Capterra routes by numeric product ID** β€” the slug in the URL is decorative. `/p/137005/Slack/` redirects to whatever product 137005 actually is (currently SmartPM). - **Cloudflare gating.** We rely on residential-proxy IP rotation + cookie reuse to slip past CF's WAF. With Apify residential pool (US country), expect ~95% per-URL success on a normal-cost run. - **Reviews pagination** is automatic β€” the actor follows `?page=N` links from each page's own HTML (not constructed URLs, since `/x/` placeholder slugs lose query-strings on redirect). Stops when no next-page link found or `maxReviewsPerProduct` is hit. Each individual review is then emitted as its own flat dataset row with the thread aggregate nested in `productAggregate`. - **Category flattening.** Same pattern as reviews β€” every product listed in a category becomes its own dataset row with `categoryAggregate` nested. A category URL with 50 products fills 50 rows of the `maxItems` budget (capped by `maxProductsPerCategory`). - **Compare extraction** uses per-product fetch β€” Capterra's compare pages are SPAs, so we read product IDs from the URL and fetch each compared product's full page through the warm session. Result: rich compare rows with real ratings, vendor info, pricing, and deployments per product. - **No mobile app.** Capterra is web-only; no API to bypass the WAF via. *** ### Support Issues, feature requests, or custom output shapes? Open a ticket on the actor's Apify page or message the maintainer. # Actor input Schema ## `startUrls` (type: `array`): Paste any mix of: β€’ Product URLs β€” `https://www.capterra.com/p/{id}/{slug}/` β€’ Reviews URLs β€” `https://www.capterra.com/p/{id}/{slug}/reviews/` β€’ Category URLs β€” `https://www.capterra.com/{slug}-software/` β€’ Compare URLs β€” `https://www.capterra.com/compare/{ids}/{slug-a}-vs-{slug-b}/` Bare numeric product IDs (e.g. `137005`) and bare category slugs (e.g. `project-management`) also work. Each entry is auto-classified and emits the matching row type β€” one row per URL. ## `maxItems` (type: `integer`): Safety cap on total dataset rows. Product / compare URLs each emit 1 row. A reviews URL emits one row per individual review (each carrying the thread-level aggregate under `productAggregate`); a category URL emits one row per product listed (each carrying the category-level aggregate under `categoryAggregate`). So a reviews URL with 100 reviews fills 100 rows, a category URL with 50 products fills 50 rows. Each emitted row = one paid output-record event. ## `maxReviewsPerProduct` (type: `integer`): Maximum number of individual reviews to include in each reviews-URL row. Reviews pages can list hundreds; capping keeps row size manageable for CSV consumers. Set 0 for no limit. ## `maxProductsPerCategory` (type: `integer`): Maximum number of category-product rows emitted per category URL. Category index pages list up to ~100 products. Set 0 for no limit. ## `maxConcurrency` (type: `integer`): Reserved for future use. Capterra requires a session-aware sticky-IP pattern, so the actor currently processes URLs sequentially through one warm session for highest pass rate. ## `minConcurrency` (type: `integer`): Reserved for future use. ## `maxRequestRetries` (type: `integer`): Maximum number of retries inside the warm session before giving up on a URL. ## `proxy` (type: `object`): Apify residential proxy works well. The actor uses a sticky session (same IP for the whole run) plus cookie-jar reuse to ride past Cloudflare's deep-page WAF β€” see the README's 'How it works' section. ## Actor input object example ```json { "startUrls": [ "https://www.capterra.com/p/137005/SmartPM/", "https://www.capterra.com/p/137005/SmartPM/reviews/", "https://www.capterra.com/project-management-software/", "https://www.capterra.com/compare/137005-229614/SmartPM-vs-Notion/" ], "maxItems": 1000, "maxReviewsPerProduct": 100, "maxProductsPerCategory": 50, "maxConcurrency": 1, "minConcurrency": 1, "maxRequestRetries": 8, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ "https://www.capterra.com/p/137005/SmartPM/", "https://www.capterra.com/p/137005/SmartPM/reviews/", "https://www.capterra.com/project-management-software/", "https://www.capterra.com/compare/137005-229614/SmartPM-vs-Notion/" ], "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("memo23/capterra-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [ "https://www.capterra.com/p/137005/SmartPM/", "https://www.capterra.com/p/137005/SmartPM/reviews/", "https://www.capterra.com/project-management-software/", "https://www.capterra.com/compare/137005-229614/SmartPM-vs-Notion/", ], "proxy": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US", }, } # Run the Actor and wait for it to finish run = client.actor("memo23/capterra-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ "https://www.capterra.com/p/137005/SmartPM/", "https://www.capterra.com/p/137005/SmartPM/reviews/", "https://www.capterra.com/project-management-software/", "https://www.capterra.com/compare/137005-229614/SmartPM-vs-Notion/" ], "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }' | apify call memo23/capterra-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=memo23/capterra-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Capterra 0.9$πŸ’° Reviews Products, Categories & Compare Scraper", "description": "[Only 0.9$πŸ’°] Capterra all-in-one scraper: product, reviews, category & compare URLs in one actor. Flat per-review rows (100% field fill: reviewer name, company size, recommendation score, incentivization), Capterra awards & badges, sentiment breakdown, structured integrations. Pure HTTP, no browser", "version": "0.1", "x-build-id": "mmRKl5zSihQJnwkpA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/memo23~capterra-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-memo23-capterra-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/memo23~capterra-scraper/runs": { "post": { "operationId": "runs-sync-memo23-capterra-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/memo23~capterra-scraper/run-sync": { "post": { "operationId": "run-sync-memo23-capterra-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "Capterra URLs", "type": "array", "description": "Paste any mix of:\nβ€’ Product URLs β€” `https://www.capterra.com/p/{id}/{slug}/`\nβ€’ Reviews URLs β€” `https://www.capterra.com/p/{id}/{slug}/reviews/`\nβ€’ Category URLs β€” `https://www.capterra.com/{slug}-software/`\nβ€’ Compare URLs β€” `https://www.capterra.com/compare/{ids}/{slug-a}-vs-{slug-b}/`\n\nBare numeric product IDs (e.g. `137005`) and bare category slugs (e.g. `project-management`) also work. Each entry is auto-classified and emits the matching row type β€” one row per URL.", "items": { "type": "string" } }, "maxItems": { "title": "Max dataset rows to emit", "minimum": 1, "type": "integer", "description": "Safety cap on total dataset rows. Product / compare URLs each emit 1 row. A reviews URL emits one row per individual review (each carrying the thread-level aggregate under `productAggregate`); a category URL emits one row per product listed (each carrying the category-level aggregate under `categoryAggregate`). So a reviews URL with 100 reviews fills 100 rows, a category URL with 50 products fills 50 rows. Each emitted row = one paid output-record event.", "default": 1000 }, "maxReviewsPerProduct": { "title": "Max reviews per product", "minimum": 0, "type": "integer", "description": "Maximum number of individual reviews to include in each reviews-URL row. Reviews pages can list hundreds; capping keeps row size manageable for CSV consumers. Set 0 for no limit.", "default": 100 }, "maxProductsPerCategory": { "title": "Max products per category", "minimum": 0, "type": "integer", "description": "Maximum number of category-product rows emitted per category URL. Category index pages list up to ~100 products. Set 0 for no limit.", "default": 50 }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "type": "integer", "description": "Reserved for future use. Capterra requires a session-aware sticky-IP pattern, so the actor currently processes URLs sequentially through one warm session for highest pass rate.", "default": 1 }, "minConcurrency": { "title": "Min concurrency", "minimum": 1, "type": "integer", "description": "Reserved for future use.", "default": 1 }, "maxRequestRetries": { "title": "Max request retries", "minimum": 0, "type": "integer", "description": "Maximum number of retries inside the warm session before giving up on a URL.", "default": 8 }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Apify residential proxy works well. The actor uses a sticky session (same IP for the whole run) plus cookie-jar reuse to ride past Cloudflare's deep-page WAF β€” see the README's 'How it works' section.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```