# Foodpanda Reviews Scraper — Review Text, Ratings & Replies (`subimpact/foodpanda-reviews-api`) Actor

Scrape Foodpanda restaurant reviews: review text, per-topic ratings (overall/food/rider), reviewer name, dates, likes, vendor replies. MY/SG/HK/PH/TW/PK/BD/KH/MM/LA. Public review surface, no login. Pay per review delivered.

- **URL**: https://apify.com/subimpact/foodpanda-reviews-api.md
- **Developed by:** [subimpact](https://apify.com/subimpact) (community)
- **Categories:** E-commerce, Lead generation, Automation
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.00 / 1,000 reviews

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Foodpanda Reviews Scraper

Scrape **restaurant reviews** from Foodpanda — review text, per-topic ratings (overall / food / rider), reviewer name, dates, likes, and vendor replies — across Malaysia, Singapore, Hong Kong and more APAC markets.

Unlike the many "Foodpanda restaurant & menu" scrapers on the Store, this Actor is **reviews-first**: it returns the actual review text, not just the aggregate rating number.

### Why use this Actor

- **Reviews-only data**: review text, per-topic scores, reviewer, date, likes, vendor reply — everything a restaurant owner, aggregator, or researcher needs for reputation and sentiment work.
- **Multi-market**: `my`, `sg`, `hk`, `ph`, `tw`, `pk`, `bd`, `kh`, `mm`, `la` (country code in input).
- **Simple input**: paste vendor codes or full restaurant/reviews URLs; pagination is handled automatically (pageKey cursor).
- **Public review surfaces only**: no login, no checkout, no account data.

### How to use

1. Open any Foodpanda restaurant page — the URL looks like `https://www.foodpanda.my/restaurant/zqj6/sakom-village/reviews`.
2. Copy the vendor code (the short alphanumeric segment, e.g. `zqj6`) — or paste the whole URL, the Actor extracts the code.
3. Pick the **country** (`my`, `sg`, `hk`, ...).
4. Set `maxReviewsPerVendor` (0 = all reviews, paginated to the end).
5. Start the run.

#### Input

| Field | Type | Description |
|---|---|---|
| `vendorCodes` | string\[] | Foodpanda vendor codes (e.g. `m3jj`, `zqj6`) |
| `vendorUrls` | string\[] | Full restaurant or `/reviews` URLs — codes auto-extracted |
| `country` | string | Two-letter market: `my`, `sg`, `hk`, `ph`, `tw`, `pk`, `bd`, `kh`, `mm`, `la` (default `my`) |
| `maxReviewsPerVendor` | int | Cap per vendor (0 = all, paginated to the end) |
| `maxVendors` | int | Cap the number of vendors processed (0 = all) |

#### Output (one row per review)

| Field | Description |
|---|---|
| `vendorCode` | Foodpanda vendor code |
| `country` | Market code |
| `reviewId` | Review UUID |
| `reviewText` | Full review text |
| `reviewerName` | Reviewer display name (or "Anonymous") |
| `reviewerId` | Reviewer identifier |
| `isAnonymous` | Anonymous flag |
| `ratingOverall` | Overall star rating (1–5) |
| `ratingFood` | Food score (1–5) when present |
| `ratingRider` | Rider score (1–5) when present |
| `ratings` | Full per-topic ratings array |
| `likes` | Like/helpful count |
| `vendorReply` | Vendor reply text when present |
| `createdAt` | Review timestamp (ISO) |
| `reviewUrl` | Foodpanda review page URL |

### Use cases

- **Reputation & sentiment analysis** for restaurant chains across markets
- **Menu/quality feedback monitoring** (food scores, dish complaints)
- **Competitor review benchmarking** for F\&B market research
- **AI/RAG training data**: natural-language restaurant reviews with structured ratings

### Pricing

Pay per event: **$2.00 per 1,000 reviews** (that's $0.002 per review). You only pay for reviews actually delivered. Platform usage (compute) is included in the event price.

### Notes & limitations

- This Actor collects **publicly visible review data** from Foodpanda's public reviews surface. Use it in accordance with Foodpanda's Terms of Service and local regulations.
- Reviews availability varies by vendor — some restaurants have no reviews yet (empty data is normal for new/small vendors).
- Each page returns up to 30 reviews; deep pagination is automatic via the `pageKey` cursor.
- Not affiliated with Foodpanda or Delivery Hero.

# Actor input Schema

## `vendorCodes` (type: `array`):

One or more Foodpanda vendor codes (e.g. m3jj, zqj6). Find them in restaurant URLs: https://www.foodpanda.my/restaurant/<CODE>/<slug>. You can also paste full restaurant or /reviews URLs — codes are extracted automatically.

## `vendorUrls` (type: `array`):

Alternative to vendorCodes: paste full Foodpanda restaurant or reviews URLs (e.g. https://www.foodpanda.my/restaurant/zqj6/sakom-village/reviews). Codes are extracted automatically.

## `country` (type: `string`):

Two-letter country code for the reviews API. Default 'my'. Supported markets include my, sg, hk, ph, tw, pk, bd, kh, mm, la.

## `maxReviewsPerVendor` (type: `integer`):

Maximum number of reviews to fetch per vendor. 0 = all available (paginate until exhausted).

## `maxVendors` (type: `integer`):

Maximum number of vendors to process. 0 = all provided.

## Actor input object example

```json
{
  "vendorCodes": [
    "m3jj"
  ],
  "country": "my",
  "maxReviewsPerVendor": 0,
  "maxVendors": 0
}
```

# Actor output Schema

## `results` (type: `string`):

One dataset item per review: vendorCode, country, reviewId, reviewText, reviewerName, reviewerId, isAnonymous, ratingOverall, ratingFood, ratingRider, ratings (full per-topic array), likes, vendorReply, createdAt, reviewUrl.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "vendorCodes": [
        "m3jj"
    ],
    "country": "my"
};

// Run the Actor and wait for it to finish
const run = await client.actor("subimpact/foodpanda-reviews-api").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 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);
});

// 📚 Want to learn more 📖? Go to → 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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "vendorCodes": ["m3jj"],
    "country": "my",
}

# Run the Actor and wait for it to finish
run = client.actor("subimpact/foodpanda-reviews-api").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "vendorCodes": [
    "m3jj"
  ],
  "country": "my"
}' |
apify call subimpact/foodpanda-reviews-api --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,subimpact/foodpanda-reviews-api"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ff9I0kaNMTFayfunR/builds/XNnfZAGoWVGGm25Nf/openapi.json