Tripadvisor Reviews Scraper
Give it Tripadvisor hotel, restaurant or attraction pages (or place names) and it returns one JSON row per review: rating, title, original-language text with an optional translation, date, sub-ratings, trip type, the owner reply with its date, and the place's overall rating and review count. $4.50 per 1,000 reviews on the free plan, less on paid plans; you pay only for rows you get.
$4.50 per 1,000 reviews on the Free plan, down to $0.80 on higher plans
Why this actor
- No depth limit. A place with 15,000 reviews is read to the end; stars, language, trip type, month and keyword filters are applied by Tripadvisor itself.
- Only new reviews.
onlyNewReviewsdelivers just what earlier runs did not; repeats are not charged. - Real data or a clear error. A free
coveragerow per place (stated vs delivered) and a freeerrorrow with acode, never a silent empty run.
Drop-in input
Easy switch. The input names of the most used Tripadvisor reviews actor are accepted as they are.
As stated in the actor’s README. The full list of accepted input names is on the Store page.
What you get
| Field | What it holds |
|---|---|
reviewId | Tripadvisor___ plus the platform's own review ID. Stable between runs, so you can dedupe weekly exports. |
date | Date the review was written, ISO format. |
title, text | What the reviewer wrote, in the language it was written in. |
extras.translatedTitle, extras.translatedText, extras.translatedLanguage | Machine translation of a review written in another language, next to the original. Only with translateTo. |
extras.originalLanguage | Language the review was written in. |
product.rating, product.reviewCount | rating is the average of all review ratings of the place (computed from its star histogram, not Tripadvisor's rounded bubble rating); reviewCount is the total number of reviews. Repeated on every row. Turn off with includePlaceSummary. |
ratingOverall | Overall bubble rating, 1 to 5. |
ratings | Sub-ratings the reviewer filled in (value, rooms, location, cleanliness, service, sleep quality). Skipped ones are left out. |
vendorResponse, extras.ownerResponseDate | The owner or manager reply and the date it was posted, when there is one. |
helpfulVotes, language, reviewerVerified | Helpful votes, review language, and whether the review is marked as verified. |
productName, productUrl, reviewUrl | The place and where the review lives. |
extras.tripType | Business, couples, family, friends or solo. |
extras.visitDate | Month of the visit (yyyy-mm). |
extras.photoUrls | Photos attached to the review. |
extras.totalCount | How many reviews match your filters at that place. |
scrapedAt | When the row was collected. |
Sample row (from the README example; long text shortened, lists cut to two items):
{
"reviewId": "Tripadvisor___123456789",
"platform": "tripadvisor",
"productName": "Example Grand Hotel",
"date": "2026-09-27",
"title": "Great stay",
"text": "Quiet room, friendly staff.",
"ratingOverall": 5,
"ratings": {
"value": 5,
"cleanliness": 5
},
"language": "en",
"vendorResponse": "Dear [name], thank you for staying with us.",
"extras": {
"tripType": "COUPLES",
"visitDate": "2026-08",
"placeType": "ACCOMMODATION"
}
}
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 1,000 | Free | Bronze | Silver | Gold | Platinum | Diamond |
|---|---|---|---|---|---|---|
| Review (main) | $4.50 | $1.75 | $1.35 | $0.80 | $0.80 | $0.80 |
Use cases
- Competitor tracking: export a rival hotel's newest reviews every week and watch what changes.
- Reputation monitoring: pull only the 1 and 2 star reviews of your own places, in every language.
- Sentiment analysis: feed review text, trip type and sub-ratings into your own model or spreadsheet.
- Market research: compare restaurants or attractions in one city side by side.
Run it from code or an AI agent
Actor ID arcadium/tripadvisor-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~tripadvisor-reviews/run-sync-get-dataset-items" \
-H "Authorization: Bearer $APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"startUrls":[{"url":"https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html"}],"maxReviewsPerPlace":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/tripadvisor-reviews').call({
"startUrls": [
{
"url": "https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html"
}
],
"maxReviewsPerPlace": 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/tripadvisor-reviews").call(run_input={
"startUrls": [{
"url": "https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html",
}],
"maxReviewsPerPlace": 20,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)MCP
{
"mcpServers": {
"apify": {
"url": "https://mcp.apify.com/?tools=arcadium/tripadvisor-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/tripadvisor-reviews")
rows = tool.invoke({"run_input": {
"startUrls": [{
"url": "https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html",
}],
"maxReviewsPerPlace": 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/tripadvisor-reviews",
run_input={
"startUrls": [{
"url": "https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html",
}],
"maxReviewsPerPlace": 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/tripadvisor-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/tripadvisor-reviews",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
0,
0
],
"parameters": {
"method": "POST",
"url": "https://api.apify.com/v2/acts/arcadium~tripadvisor-reviews/run-sync-get-dataset-items",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "{\"startUrls\":[{\"url\":\"https://www.tripadvisor.com/Hotel_Review-g60763-d93450-Reviews-Hyatt_Grand_Central_New_York-New_York_City_New_York.html\"}],\"maxReviewsPerPlace\":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~tripadvisor-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
- Public reviews only. Nothing behind a login.
titleandtextare always the original. A translation exists only when you settranslateTo, and it is Tripadvisor's own machine translation. Tripadvisor does not translate reviews older than about two years, so those rows have noextras.translatedText. If translation stops working during a run, the remaining rows are delivered without it.- A keyword search returns at most 1,000 reviews, a limit set by Tripadvisor.
- Several languages in one run are read as separate passes and merged newest first, so
dateFromandonlyNewReviewskeep their early stop. With another sort the passes are delivered one after another. - Name search picks the best match only. For hotels with common names, use the URL.
- Sub-ratings exist only where the reviewer filled them in, and the set differs between hotels, restaurants and attractions.
- Tripadvisor changes its site from time to time. The actor adapts on its own in most cases; if a run fails for several places at once, try again later or open an issue.
FAQ
How fresh is the data? It is read when you run the actor, so it matches what the site shows at that moment.
Can I get only new reviews since my last run? Yes. Turn on onlyNewReviews and keep the same stateKey between runs. Reviews you already received are skipped and not charged. The state lives in a key-value store in your Apify account. Pick a new stateKey to start over.
Does the actor store anything in my account? Yes, one small named key-value store, tripadvisor-reviews-state. It caches the identifiers of Tripadvisor's own review queries so the next run starts faster, and it holds the onlyNewReviews state. It contains no review text.
A place returned no reviews. Why? Check that the URL opens a hotel, restaurant or attraction page in your browser. Places without reviews return a free error row.
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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