# Medium Article Feed Scraper — Tag, Author & Publication (`axery/medium-article-feed-scraper`) Actor

Scrape Medium article metadata from any tag, author or publication feed: title, author, tags, publication date, canonical URL and summary. No login, no API key.

- **URL**: https://apify.com/axery/medium-article-feed-scraper.md
- **Developed by:** [Axery](https://apify.com/axery) (community)
- **Categories:** News, Automation, Integrations
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.34 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## Medium Article Feed Scraper (Tag, Author, Publication)

Scrapes **article metadata** from any public Medium feed — by tag, by author, or by publication. No login, no API key, no cookies.

Useful for content research, competitor tracking, newsletter curation, trend monitoring on a topic, and building a reading index across many tags at once.

### Three feed types

| Type | Target looks like | Returns |
|---|---|---|
| `tag` | `python`, `machine-learning` | Recent articles carrying that tag, across all of Medium |
| `user` | `dhh`, `@dhh` | Recent articles by that author |
| `publication` | the publication slug from its URL | Recent articles in that publication |

### What this returns — and what it doesn't

This Actor returns article **metadata plus the feed's own summary**: title, author, tags, publication date, canonical URL, and a plain-text excerpt. It does **not** return full article bodies — every row carries a canonical `url` if you need to open the article itself.

### What makes this different

**CDATA is unwrapped, HTML is stripped.** Medium's feed wraps nearly every value in `<![CDATA[...]]>` and renders excerpts as HTML. A scraper that passes those straight through puts literal `<![CDATA[` markers in every title and `<p>` tags in every summary. Here titles come out as titles and summaries as plain text.

**Both timestamps are actually comparable.** Medium mixes two date formats inside a single feed item: `published_at` arrives as RFC-822 (`Sun, 23 Aug 2026 03:11:27 GMT`) while `updated_at` arrives as ISO-8601 with fractional seconds. Both are normalized to UTC ISO-8601, so you can sort and diff them without a second cleanup pass.

**Clean URL and tracking URL, kept separate.** Every feed link carries an RSS tracking suffix (`?source=rss------python-5`). `url` is the canonical link — stable, dedupe-able across feeds — and `url_with_tracking` preserves the original if you need it.

**A stable article ID.** Derived from the hex suffix of the article slug, so the same article surfaced by three different tag feeds dedupes to one ID rather than three rows.

**`excerpt_length` tells you what you're looking at.** The summary is capped at 500 characters, and this field reports the excerpt's true length — so a genuinely short teaser is distinguishable from a truncated one, instead of you having to guess.

**Incremental mode.** Remembers article IDs between runs, so a scheduled watch on a tag returns only what's new — and you are charged only for the new rows.

### Input

| Field | Type | Notes |
|---|---|---|
| `feedType` | enum | `tag`, `user`, or `publication`. |
| `targets` | array | Tags / usernames / publication slugs. Each fetched independently. |
| `maxItems` | integer | Per target. `0` = everything the feed returns. |
| `incremental` | boolean | Only articles not seen in previous runs. |
| `proxyConfiguration` | object | Not normally needed. |

#### One limit worth knowing up front

A Medium feed is a **fixed window of the most recent posts** — there is no pagination parameter to page further back. `maxItems` can narrow that window but cannot extend it. To build history on a tag, run this on a schedule with `incremental` enabled and let the dataset accumulate.

### Output

```json
{
  "article_id": "medium.com:214bb62adf43",
  "title": "I Thought I Knew Python Until I Built My First Real Automation System",
  "author": "Muhummad Zaki",
  "published_at": "2026-08-23T04:27:13Z",
  "tags": ["technology", "programming", "coding", "python"],
  "summary": "The moment Python stopped being a programming language and became an employee...",
  "excerpt_length": 123,
  "url": "https://python.plainenglish.io/i-thought-i-knew-python-...-214bb62adf43",
  "feed_type": "tag",
  "feed_target": "python"
}
```

Each run also writes a `RUN_COVERAGE` record to the key-value store with what was requested, what came back, and any per-target failures — so a partial run is visible rather than silent.

### Local development

```bash
pip install -r requirements.txt
python test_local.py python --type tag --out sample_output.json
python test_local.py dhh --type user --max 5
```

`sample_output.json` is real output from a live run of the `python` tag feed.

# Actor input Schema

## `feedType` (type: `string`):

Which kind of Medium feed the targets below name. All three are public feeds that need no key.

## `targets` (type: `array`):

For `tag`: tag slugs such as `python` or `machine-learning`. For `user`: usernames, with or without the leading @. For `publication`: the publication slug from its URL. Each target is fetched independently into the same dataset.

## `maxItems` (type: `integer`):

Cap on articles returned per target. Set `0` for everything the feed returns. Note that a Medium feed is a fixed window of the most recent posts, so this can only narrow that window - it cannot reach further back in time.

## `incremental` (type: `boolean`):

Remember article IDs between runs and return only articles not seen before. Useful for a scheduled run that watches a tag - you are charged only for the new rows.

## `proxyConfiguration` (type: `object`):

Not normally needed - Medium serves these feeds without an anti-bot layer.

## Actor input object example

```json
{
  "feedType": "tag",
  "targets": [
    "python"
  ],
  "maxItems": 0,
  "incremental": false,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `articles` (type: `string`):

One row per article: title, author, tags, publication date, canonical URL and summary.

# 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 = {
    "targets": [
        "python",
        "machine-learning"
    ],
    "maxItems": 0
};

// Run the Actor and wait for it to finish
const run = await client.actor("axery/medium-article-feed-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 = {
    "targets": [
        "python",
        "machine-learning",
    ],
    "maxItems": 0,
}

# Run the Actor and wait for it to finish
run = client.actor("axery/medium-article-feed-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 '{
  "targets": [
    "python",
    "machine-learning"
  ],
  "maxItems": 0
}' |
apify call axery/medium-article-feed-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,axery/medium-article-feed-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/t0kp2sm09PdQtdmm6/builds/u3JQZ0Q56tB30NbA2/openapi.json