# Medium Publication Scraper Pro (`red.cars/medium-publication-scraper`) Actor

Enterprise-grade Medium data extraction with comprehensive filtering and multi-mode support. Extract publications, authors, articles and trending content without API keys.

- **URL**: https://apify.com/red.cars/medium-publication-scraper.md
- **Developed by:** [AutomateLab](https://apify.com/red.cars) (community)
- **Categories:** Developer tools, Automation, Other
- **Stats:** 10 total users, 0 monthly users, 73.1% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Medium Publication Scraper - Web Scraping, Content Extraction, Blog Analytics

Web scraping tool for extracting Medium publications, author profiles, articles, and trending content. No API key required. Data extraction includes article text, claps, publication dates, tags, and author metadata for content intelligence, thought leadership monitoring, and industry trend research.

### Use Cases

- **Content Competitive Analysis**: Monitor competitor publications, track article performance, and identify high-engagement content patterns in your industry. Analyze what topics and formats resonate with target audiences.

- **Thought Leadership Tracking**: Follow key authors and publications to stay current on emerging ideas, technologies, and industry discussions. Build a personalized feed of important content for your domain.

- **Writer Discovery**: Find influential writers in specific niches (AI, startups, programming) for potential collaboration, guest posting, or influencer outreach. Evaluate reach and engagement before reaching out.

- **SEO Content Research**: Analyze top-performing articles by topic/tag to inform content strategy and keyword targeting. Understand what content ranks well in your industry.

- **Market Intelligence**: Extract industry trends from curated publications like Towards Data Science, Freecodecamp, or The Verge for market research reports and competitive analysis.

### MCP Tools Reference

This actor exposes the following tools via the Apify MCP protocol. AI agents can invoke these tools directly to integrate with workflows:

| Tool | Input | Output |
|------|-------|--------|
| `scrapePublication` | Publication handle (@towards-data-science), `maxArticles`, `includeContent` | Publication metadata + all articles |
| `scrapeAuthor` | Author handle (@username), `maxArticles` | Author profile with follower count, bio, articles |
| `scrapeArticle` | Article URL | Full article content, metadata, engagement metrics |
| `scrapeTrending` | `maxResults` | Trending articles from Medium homepage |
| `scrapeByTag` | Tag name (artificial-intelligence), `maxResults` | Articles filtered by topic tag |

### Input Parameters

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `mode` | string | `publication`, `author`, `articles`, `trending`, `tags`, `bulk` | publication |
| `urls` | array | Medium URLs (@username, @publication, article URLs) | - |
| `publications` | array | Publication handles (e.g., @towards-data-science, @freecodecamp) | - |
| `authors` | array | Author handles (e.g., @tim, @medium) | - |
| `tags` | array | Topic tags (e.g., artificial-intelligence, startup, programming) | - |
| `maxArticles` | integer | Max articles per publication/author (1-1000) | 100 |
| `includeContent` | boolean | Extract full article text (slower) | false |
| `includeMetrics` | boolean | Extract claps, responses, reading time | true |
| `includeAuthorInfo` | boolean | Extract author details and follower counts | true |
| `extractionDepth` | string | `basic`, `standard`, `comprehensive` | standard |
| `proxyType` | string | `DATACENTER` (Standard) or `RESIDENTIAL` (Premium) | DATACENTER |

### Pricing

This actor uses Apify's Pay-Per-Event (PPE) pricing model. You are charged per successful result returned. No charge for queries that return zero results.

- **Standard Proxy**: $0.60–$7 per IP address. Medium has moderate anti-bot protection; standard proxy handles most extraction tasks.

- **Premium Residential Proxy**: $7–$8 per GB. Use for high-volume or scheduled scraping where datacenter IPs may be blocked.

- **Compute Units**: Consumed from your Apify plan based on run time and memory allocation.

### Example Output

```json
{
  "results": [
    {
      "type": "article",
      "title": "The Future of Retrieval-Augmented Generation",
      "url": "https://medium.com/towards-data-science/the-future-of-rag-xxxx",
      "author": "akshay_pachaar",
      "authorFollowers": 45000,
      "publishedAt": "2026-04-10T14:30:00.000Z",
      "claps": 8400,
      "readingTime": 12,
      "tags": ["artificial-intelligence", "machine-learning", "nlp"],
      "excerpt": "Retrieval-augmented generation combines the power of...",
      "wordCount": 3200,
      "content": "## Introduction\n\nRetrieval-augmented generation (RAG) represents...",
      "cleanMarkdown": "## Introduction\n\nRetrieval-augmented generation (RAG) represents..."
    }
  ]
}
```

### Quick Start

```bash
## Run via Apify CLI
apify run -a red.cars/medium-publication-scraper

## Or configure input and run at:
## https://apify.com/red.cars/medium-publication-scraper
```

### How It Works

The actor uses intelligent extraction with Apify's proxy infrastructure to gather public data from Medium. It supports multiple extraction strategies to maximize data completeness while minimizing load time:

- **Homepage Only**: Extract from main page (faster but limited)
- **Archive Pages**: Use archive URLs for complete historical data
- **Smart Discovery**: Automatic best method selection based on content availability

The actor handles pagination through publication archives and handles both logged-in and logged-out views of Medium content.

### Data Fields Returned

**Article Results:**

- `type` — Always "article" for article scrapes
- `title`, `url` — Article identifiers and link
- `author`, `authorFollowers` — Author information and reach
- `publishedAt` — Publication timestamp
- `claps` — Engagement metric (1-50 range mapped from Medium's 1-50 clap system)
- `readingTime` — Estimated minutes to read
- `tags` — Topic categories array
- `excerpt` — First 160 characters for preview
- `wordCount` — Total words in article
- `content` — Full HTML or plain text content
- `cleanMarkdown` — Markdown-formatted content

### Known Limitations

- Medium may block high-volume scraping from datacenter IPs; use residential proxy for bulk extraction
- Member-only (premium) content requires a Medium account with subscription; free tier gets public content only
- Some older articles may have limited metadata or missing engagement metrics
- External images in articles may be blocked or require additional requests
- Publication archives may have pagination limits

### Legal

Data source is Medium.com. This actor must be used in compliance with Medium's Terms of Service. Do not use for unauthorized data collection, content republishing, or privacy-violating activities.

# Actor input Schema

## `mode` (type: `string`):

Choose what type of Medium data to extract

## `urls` (type: `array`):

List of Medium URLs to scrape. Supports @username, @publication, and article URLs. For best results, use publication URLs (e.g., medium.com/@towards-data-science)

## `publications` (type: `array`):

List of Medium publication handles (e.g., @towards-data-science, @freecodecamp). Used in publication or bulk mode.

## `authors` (type: `array`):

List of Medium author handles (e.g., @tim, @medium). Used in author or bulk mode.

## `tags` (type: `array`):

List of Medium topic tags to analyze (e.g., artificial-intelligence, startup, programming). Used in tags mode.

## `maxArticles` (type: `integer`):

Maximum number of articles to extract per publication/author

## `includeContent` (type: `boolean`):

Extract complete article text content (may slow down scraper significantly)

## `includeMetrics` (type: `boolean`):

Extract claps, responses, and reading time data

## `includeAuthorInfo` (type: `boolean`):

Extract detailed author information and follower counts

## `includeMemberOnly` (type: `boolean`):

Include Medium member-only/premium posts in results

## `dateRange` (type: `object`):

Filter articles by publication date

## `sortBy` (type: `string`):

How to sort the extracted articles

## `extractionDepth` (type: `string`):

How deep to extract data from each source

## `archiveStrategy` (type: `string`):

Method for extracting historical content

## `maxResults` (type: `integer`):

Maximum total number of items to extract across all sources

## `proxyType` (type: `string`):

Choose your preferred balance of cost vs reliability. You control proxy costs from your Apify plan.

## Actor input object example

```json
{
  "mode": "publication",
  "publications": [
    "@towards-data-science",
    "@freecodecamp"
  ],
  "authors": [
    "@tim",
    "@medium"
  ],
  "tags": [
    "artificial-intelligence",
    "startup",
    "programming"
  ],
  "maxArticles": 20,
  "includeContent": false,
  "includeMetrics": true,
  "includeAuthorInfo": true,
  "includeMemberOnly": true,
  "sortBy": "newest",
  "extractionDepth": "standard",
  "archiveStrategy": "smart",
  "maxResults": 50,
  "proxyType": "DATACENTER"
}
```

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("red.cars/medium-publication-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("red.cars/medium-publication-scraper").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 '{}' |
apify call red.cars/medium-publication-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,red.cars/medium-publication-scraper"
        }
    }
}
```

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/wdbycSeSHVGXrewX8/builds/bE6Uu7ErZRJsdmOL4/openapi.json