# Supplyframe News Spider (`getdataforme/supplyframe-news-spider`) Actor
The Supplyframe News Spider efficiently extracts data from Supplyframe's news room, offering insights into industry trends....
- **URL**: https://apify.com/getdataforme/supplyframe-news-spider.md
- **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community)
- **Categories:** AI, E-commerce, Other
- **Stats:** 2 total users, 1 monthly users, 0.0% 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
## Supplyframe News Spider
### Introduction
The Supplyframe News Spider is designed to efficiently extract data from Supplyframe's news room, providing users with valuable insights into the latest industry trends and developments. This actor is ideal for businesses and researchers looking to stay informed and make data-driven decisions.
### Features
- **Comprehensive Data Extraction**: Scrapes detailed information from specified URLs.
- **High Data Quality**: Ensures accurate and up-to-date information.
- **Efficient Performance**: Optimized for fast and reliable data retrieval.
- **Customizable URL Input**: Allows users to specify multiple URLs for targeted scraping.
- **Structured Output**: Delivers data in a well-organized JSON format.
- **Scalable Solution**: Handles large volumes of data without compromising performance.
- **User-Friendly Interface**: Easy configuration and monitoring through the Apify platform.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|-----------|--------|----------|-----------------------------------------------------|---------------------------------------------------|
| Urls | array | No | The URLs for the spider to scrape. | `["https://intelligence.supplyframe.com/news-room"]` |
### Example Usage
#### Example Input JSON
```json
{
"Urls": [
"https://intelligence.supplyframe.com/news-room"
]
}
````
#### Example Output JSON
```json
[
{
"type": "menu_category",
"section": "ENTERPRISE SOLUTIONS",
"title": "NPI",
"url": "https://supplyframe.com/npi/",
"description": "Infuse new product development with real-time intelligence",
"actor_id": "kC2Qktfw0iZ2rhSM7",
"run_id": "W9sXG90hnbWTWycFJ"
}
]
```
### Use Cases
- **Market Research and Analysis**: Gather industry insights for strategic planning.
- **Competitive Intelligence**: Monitor competitors' activities and market positioning.
- **Price Monitoring**: Track pricing trends and fluctuations.
- **Content Aggregation**: Compile news and updates for content creation.
- **Academic Research**: Access industry data for scholarly studies.
- **Business Automation**: Integrate data into automated workflows for efficiency.
### Installation and Usage
1. Search for "Supplyframe News Spider" 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 data is structured in JSON format, containing key fields such as `type`, `section`, `title`, `url`, `description`, `actor_id`, and `run_id`. This format ensures easy integration and analysis of the extracted data.
### 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
## `Urls` (type: `array`):
The urls for the spider.
## Actor input object example
```json
{
"Urls": [
"https://intelligence.supplyframe.com/news-room"
]
}
```
# 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/supplyframe-news-spider").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/supplyframe-news-spider").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/supplyframe-news-spider --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/supplyframe-news-spider",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Supplyframe News Spider",
"description": "The Supplyframe News Spider efficiently extracts data from Supplyframe's news room, offering insights into industry trends....",
"version": "0.0",
"x-build-id": "iLRznJL9CeN9ymwLC"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~supplyframe-news-spider/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-supplyframe-news-spider",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/getdataforme~supplyframe-news-spider/runs": {
"post": {
"operationId": "runs-sync-getdataforme-supplyframe-news-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/getdataforme~supplyframe-news-spider/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-supplyframe-news-spider",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"Urls": {
"title": "Urls",
"minItems": 1,
"type": "array",
"description": "The urls for the spider.",
"default": [
"https://intelligence.supplyframe.com/news-room"
],
"items": {
"type": "string"
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```