Tokopedia Reviews Scraper

Tokopedia Reviews Scraper extracts product reviews from any shop on Tokopedia, Indonesia's largest e-commerce marketplace — without the official Tokopedia API, login, or cookies. Paste a shop URL and get every review as structured Tokopedia data: star rating, review text, buyer, date, the product reviewed, photos and videos, and the seller's reply.

Runs read Tokopedia's own data endpoint instead of rendering pages in a browser, so thousands of reviews finish in minutes. Export scraped data, run the scraper via API, schedule and monitor runs, or integrate with other tools.

What is Tokopedia Reviews Scraper?

Tokopedia Reviews Scraper is a no-code tool to scrape Tokopedia reviews for a whole shop at once. Give it one or more shop URLs, pick a sort order (newest, most helpful, highest or lowest rating) and an optional filter (star rating, or only reviews with photos), and set how many reviews you need. Results come back as clean JSON, downloadable as CSV or Excel.

• Any shop, any volume: official stores and regular sellers, 10 reviews or 10,000.
• Full review fields: rating, text, buyer name, date, product name and URL, review photos and videos, seller reply and reply date, helpful votes.
• Filters and sorting built in.
• Fast and cheap: no browser, no proxies needed for small runs.

What data can Tokopedia Reviews Scraper extract?

Field	Example
rating	5
reviewText	sangat wangi dan bagus, rekomendasi
reviewerName	Q***b
reviewTime	Hari ini
productName	Makarizo Advisor Hair Recovery Vitamin
productUrl	https://www.tokopedia.com/makarizo-advisor/…
sellerReply	Terima kasih atas ulasannya!
reviewImages	["https://images.tokopedia.net/…"]
reviewVideos	[]
shopName, shopID, totalReviews	Makarizo Advisor, 7494500774502172946, 10000

How much does it cost to scrape Tokopedia reviews?

Pricing is pay per result: you pay only for the review and shop records saved to your dataset, with no subscription. Free monthly usage included with every Apify account covers several hundred reviews. The Actor uses lightweight HTTP requests, so compute cost is negligible even for large shops. See the Pricing tab for the current price per 1,000 results.

How to scrape Tokopedia reviews

1. Open any shop on Tokopedia and copy its URL, for example https://www.tokopedia.com/makarizo-advisor.
2. Paste it into Tokopedia shop URLs. Add several shops if you like.
3. Set Max reviews per shop, choose Sort reviews by, and optionally Filter by rating (or With photo/video only).
4. Click Start, then export from the Storage tab as JSON, CSV, or Excel.

⬇️ Input

{
    "startUrls": ["https://www.tokopedia.com/makarizo-advisor"],
    "maxReviewsPerShop": 500,
    "reviewsSort": "recent",
    "ratingFilter": "all"
}

Field	Required	Description
startUrls	yes	Tokopedia shop URLs (tokopedia.com/{shop-name})
maxReviewsPerShop	no	Default 200
reviewsSort	no	recent, helpful, highest, lowest
ratingFilter	no	all, 5…1, or with_media

Scrape only negative Tokopedia reviews

Set ratingFilter to 1 or 2 to pull complaints only — useful for quality monitoring and competitor research.

⬆️ Output

One review per dataset item, plus one shop record (type: "shop") per URL with the shop's total review count.

{
    "type": "review",
    "shopName": "Makarizo Advisor",
    "rating": 5,
    "reviewText": "sangat baik dan wangi sekali rekomendasi",
    "reviewerName": "Q***b",
    "reviewTime": "Hari ini",
    "productName": "Makarizo Advisor Hair Recovery Vitamin",
    "productUrl": "https://www.tokopedia.com/makarizo-advisor/…",
    "sellerReply": "",
    "reviewImages": ["https://images.tokopedia.net/img/…"],
    "totalLikes": 0
}

Use cases for Tokopedia review data

Seller and brand monitoring

Watch your own shop's reviews and reply times; schedule daily runs and get alerted on 1–2 star reviews.

Competitor research

Compare what buyers praise and complain about across competing shops in your category.

Indonesian-language sentiment analysis

Feed structured Bahasa Indonesia reviews into sentiment models and dashboards.

Product-quality reports for agencies

Build reputation reports for e-commerce clients without manual copy-paste.

Integrations and Tokopedia reviews API

Run the Actor from code with the Apify API and the JavaScript or Python client, or without code via Google Sheets, Slack, n8n, Make, Zapier, and webhooks. It also works with LangChain, LlamaIndex, and the Apify MCP server for AI agents.

from apify_client import ApifyClient

client = ApifyClient("<YOUR_API_TOKEN>")
run = client.actor("lergassy/tokopedia-reviews-scraper").call(run_input={
    "startUrls": ["https://www.tokopedia.com/makarizo-advisor"],
    "maxReviewsPerShop": 200,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["rating"], item["reviewText"])

🤖 For AI Agents & LLM Apps

Compact reference for agents calling this Actor through the Apify MCP server or the Apify API (lergassy/tokopedia-reviews-scraper).

Purpose: returns the product reviews of any Tokopedia store — Indonesia's largest marketplace — as one row per review, with the seller's replies attached. Use it to answer "what do buyers complain about in this shop", "which products get the worst ratings" and "does this seller answer complaints".

Minimal input:

{ "startUrls": ["https://www.tokopedia.com/makarizo-advisor"], "maxReviewsPerShop": 100 }

Output: one row per review — type, shopID, shopName, shopUrl, reviewId, rating, reviewText, reviewTime, reviewerName, reviewerId, isAnonymous, totalLikes, sellerReply, sellerReplyTime, productName, productUrl, productId, productImage, reviewImages, reviewVideos. The dataset also carries one shop row per store with totalReviews and reviewsScraped, which is how you tell a store with few reviews from a run that hit its cap.

Behaviors an agent should know:

• Input is store URLs, not product URLs or search queries. The Actor resolves the store id from the URL and walks the store's whole review feed.
• maxReviewsPerShop defaults to 200 and is the only real cost control. A store with 10,000 reviews will happily return all of them if you raise it.
• ratingFilter narrows to a single star rating or to with_media (reviews that carry photos or video); reviewsSort chooses recent, helpful, highest or lowest. Filter through these rather than post-filtering rows, so the cap is spent on rows you want.
• Review text is written by Indonesian shoppers and is mostly in Indonesian, often with slang and abbreviations. Translate before sentiment scoring.
• Buyer names arrive masked (R***a) when the reviewer chose to stay anonymous; isAnonymous marks those rows.
• No login and no API key: the Actor reads the store's own review endpoint.

❓ FAQ

Is it legal to scrape Tokopedia reviews?

The Actor extracts only publicly visible review data; Tokopedia's robots.txt explicitly allows crawling review pages, and no private buyer data is collected. Review the site's terms and consult a lawyer for your specific use case.

Can I use Tokopedia Reviews Scraper with the Apify API?

Yes — see the API tab for ready snippets in JavaScript, Python, and cURL.

Can I use it through an MCP server?

Yes. Connect the Apify MCP server and your AI agent can call this Actor as a tool.

Can I integrate it with other apps?

Yes — Google Sheets, Slack, n8n, Make, Zapier, webhooks, or any HTTP client.

Which shops work?

Any Tokopedia shop URL of the form tokopedia.com/{shop-name} — official stores and regular sellers.

The reviews are in Indonesian. Can I translate them?

The Actor returns original text; add a translation step or an LLM in your workflow.

Do I need proxies?

Small runs work without them. For large volumes keep Apify Proxy enabled (on by default).

Can I scrape product listings and prices too?

Not with this Actor — it is focused on reviews. A Tokopedia products and prices scraper is planned; request it in the Issues tab.

Your feedback

Missing a field or hitting an error? Open an issue in the Issues tab — requests are welcome and shipped fast. If the Actor saved you time, a review helps other sellers find it.

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