# Fliggy Travel Discovery Scraper (`getdataforme/fliggytravel-discovery-scraper`) Actor
The Fliggytravel Discovery Scraper extracts detailed flight data from Fliggytravel, aiding in travel planning, market analysis, and competitive intelligence....
- **URL**: https://apify.com/getdataforme/fliggytravel-discovery-scraper.md
- **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community)
- **Categories:** Automation, Lead generation, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $9.00 / 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
## Fliggytravel Discovery Scraper
The Fliggytravel Discovery Scraper is a powerful tool designed to extract comprehensive flight information from Fliggytravel. This actor enables users to gather detailed data on flight solutions, making it an essential resource for travel planning, market analysis, and competitive intelligence.
### Features
- **Comprehensive Flight Data**: Extracts detailed information about flights, including prices, flight times, and airline details.
- **Customizable Searches**: Input parameters allow for tailored searches based on departure and arrival cities, dates, and more.
- **High Data Quality**: Ensures accurate and up-to-date flight information.
- **Scalable Solutions**: Capable of handling large volumes of data with customizable limits.
- **User-Friendly Interface**: Easy to configure and run with minimal setup required.
- **Automated Data Collection**: Streamlines the process of gathering flight data, saving time and effort.
- **Versatile Output Formats**: Export data in JSON, CSV, or Excel formats for further analysis.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|---------------|---------|----------|---------------------------------------------------------------------|---------------|
| leaving_from | String | Yes | Departure city keyword (e.g. 'ktm', 'New York', 'NYC') | "ktm" |
| going_to | String | Yes | Arrival city keyword (e.g. 'qatar', 'Los Angeles', 'LAX') | "qatar" |
| leaving_date | String | Yes | Departure date in YYYY-MM-DD format | "2026-05-29" |
| back_date | String | Yes | Return date in YYYY-MM-DD format | "2026-06-01" |
| maxItems | Integer | No | Maximum number of flight solutions to collect (0 = unlimited) | 100 |
### Example Usage
#### Example Input
```json
{
"leaving_from": "ktm",
"going_to": "qatar",
"leaving_date": "2026-05-29",
"back_date": "2026-06-01",
"maxItems": 100
}
````
#### Example Output
```json
[
{
"solutionId": "eJxNjEtrw0AMhH%2FMQm42mllpH8eSGlrS0kAT3Fy8NMU5hd5684%2FvZg0hCPT4NDMIDPMcJXucg%2BmckAVni%2FqT6QP9xYpN0u0O793zx0tHYRBj3jy9EmnFw9sdQ1WW%2BmLyjT9aCNqEhm62hoNg0%2BTa%2BD2q8lpr1Jr0aCHMJkI8kv9GjDkFRaTCQOpcH%2BZ89j1YqNKrLozsUaSX5ffvei235updxI1lXIZyct6NR56%2BPneHMhy5H%2Bvimqy1fc5ui3W%2FtPEPD2VOGA%3D%3D_9",
"price": "634",
"currencySymbol": "$",
"remainSeats": "9",
"journeys": [
{
"type": "Depart",
"depCityCode": "KTM",
"depCityName": "Kathmandu",
"arrCityCode": "DOH",
"arrCityName": "Doha",
"depTimeStr": "2026-05-29 14:40:00",
"arrTimeStr": "2026-05-30 00:05:00",
"depArrTimeDesc": "Fri, May 29, 02:40PM - 12:05AM",
"flightTime": "12h 10m",
"segments": [
{
"airlineName": "Air India",
"airlineCode": "AI",
"flightNo": "AI218",
"depAirportName": "Kathmandu Tribhuvan International Airport",
"arrAirportName": "Indira Gandhi Intl Airport",
"depAirportCode": "KTM",
"arrAirportCode": "DEL",
"depTime": "02:40",
"arrTime": "04:20",
"depDate": "May 29",
"arrDate": "May 29",
"flightTime": "1h 55m",
"depTerm": "I",
"arrTerm": "T3",
"equipType": "Narrow Body Aircraft 320"
},
{
"airlineName": "Air India",
"airlineCode": "AI",
"flightNo": "AI2283",
"depAirportName": "Indira Gandhi Intl Airport",
"arrAirportName": "Hamad International Airport",
"depAirportCode": "DEL",
"arrAirportCode": "DOH",
"depTime": "09:25",
"arrTime": "12:05",
"depDate": "May 29",
"arrDate": "May 30",
"flightTime": "5h 10m",
"depTerm": "T3",
"arrTerm": "",
"equipType": "Narrow Body Aircraft 32N"
}
]
}
],
"scrapedAt": "2026-05-27T06:42:54.276Z"
}
]
```
### Use Cases
- **Market Research and Analysis**: Gather data for understanding market trends and consumer preferences.
- **Competitive Intelligence**: Monitor competitor pricing and offerings.
- **Price Monitoring**: Track flight prices for cost-effective travel planning.
- **Content Aggregation**: Compile flight data for travel blogs or websites.
- **Academic Research**: Analyze travel patterns and trends.
- **Business Automation**: Automate the collection of flight data for business processes.
### Installation and Usage
1. Search for "Fliggytravel Discovery Scraper" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters.
4. Click "Start" to begin extraction.
5. Monitor progress in the log.
6. Export results in your preferred format (JSON, CSV, Excel).
### Output Format
The output is a JSON array containing objects with the following fields:
- `solutionId`: Unique identifier for the flight solution.
- `price`: Cost of the flight.
- `currencySymbol`: Currency of the price.
- `remainSeats`: Number of remaining seats.
- `journeys`: Array of journey details including departure and arrival information, flight times, and segments.
- `scrapedAt`: Timestamp of when the data was scraped.
### Support
For custom/simplified outputs or bug reports, please contact:
- Email: support@getdataforme.com
- Subject line: "custom support"
- Contact form:
We're here to help you get the most out of this Actor!
# Actor input Schema
## `leaving_from` (type: `string`):
Departure city keyword (e.g. 'ktm', 'New York', 'NYC')
## `going_to` (type: `string`):
Arrival city keyword (e.g. 'qatar', 'Los Angeles', 'LAX')
## `leaving_date` (type: `string`):
Departure date in YYYY-MM-DD format
## `back_date` (type: `string`):
Return date in YYYY-MM-DD format
## `maxItems` (type: `integer`):
Maximum number of flight solutions to collect (0 = unlimited)
## Actor input object example
```json
{
"leaving_from": "ktm",
"going_to": "qatar",
"leaving_date": "2026-05-29",
"back_date": "2026-06-01",
"maxItems": 100
}
```
# Actor output Schema
## `results` (type: `string`):
No description
# 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 = {};
// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/fliggytravel-discovery-scraper").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("getdataforme/fliggytravel-discovery-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{}' |
apify call getdataforme/fliggytravel-discovery-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/fliggytravel-discovery-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Fliggy Travel Discovery Scraper",
"description": "The Fliggytravel Discovery Scraper extracts detailed flight data from Fliggytravel, aiding in travel planning, market analysis, and competitive intelligence....",
"version": "0.0",
"x-build-id": "CapSffKlrCIl0oaxu"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~fliggytravel-discovery-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-fliggytravel-discovery-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/getdataforme~fliggytravel-discovery-scraper/runs": {
"post": {
"operationId": "runs-sync-getdataforme-fliggytravel-discovery-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/getdataforme~fliggytravel-discovery-scraper/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-fliggytravel-discovery-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": [
"leaving_from",
"going_to",
"leaving_date",
"back_date"
],
"properties": {
"leaving_from": {
"title": "Leaving From",
"type": "string",
"description": "Departure city keyword (e.g. 'ktm', 'New York', 'NYC')",
"default": "ktm"
},
"going_to": {
"title": "Going To",
"type": "string",
"description": "Arrival city keyword (e.g. 'qatar', 'Los Angeles', 'LAX')",
"default": "qatar"
},
"leaving_date": {
"title": "Leaving Date",
"type": "string",
"description": "Departure date in YYYY-MM-DD format",
"default": "2026-05-29"
},
"back_date": {
"title": "Return Date",
"type": "string",
"description": "Return date in YYYY-MM-DD format",
"default": "2026-06-01"
},
"maxItems": {
"title": "Max Items",
"minimum": 0,
"type": "integer",
"description": "Maximum number of flight solutions to collect (0 = unlimited)",
"default": 100
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```