Medium Scraper: Articles by Tag, Author & Search

Scrape Medium stories by tag, search, author or publication. For every story you get the title, subtitle, author and their follower count, publication, publish date, claps, responses, reading time, word count, tags and member-only flag, and optionally the full text as Markdown and plain text.

• Four ways in: tags (artificial-intelligence, python), Medium search, authors (@username) and publications (medium.com/better-programming), plus single story addresses
• Six tag views: newest, trending, top this week, month, year or of all time
• Past Medium's 100-story limit per tag: when you ask for more, the scraper also reads the tag's other views and Medium search for it, without duplicates
• Full text on request, with headings, lists, code blocks, links and images kept as Markdown
• Top posts, top authors and related tags for every tag, search, author and publication
• No login, no API key, no browser

What does this Medium scraper do?

@zulie (author, 300 stories, 2020-2026)
 Median claps 2,483 · top story "If You Want to Be a Creator, Delete All..." 69,400 claps
tag python (top this year, at least 10 claps)
 403 stories in 76 seconds
search "rag pipeline"
 300 stories · top "Building a RAG Pipeline for 10M+ Documents" 1,977 claps

1. Add tags, searches, authors, publications or story addresses.
2. The scraper reads Medium's own story lists, the same ones its website shows, page by page.
3. You get one row per story, a summary per source and a CSV.

flowchart LR
    A[Tags · searches ·<br/>authors · publications] --> B[Medium story lists]
    B --> C[Claps, responses,<br/>reading time, tags]
    C --> D[Full text as<br/>Markdown, optional]
    C --> E[One row per story<br/>+ top posts per source]

Who is it for?

• Content marketers and writers finding what gets claps in a topic before writing about it
• Publications and editors scouting authors by followers and claps
• SEO and trend researchers tracking what a topic publishes every day
• AI and data teams collecting technical articles as Markdown for RAG and training sets
• Agencies reporting on a brand's or a competitor's Medium publication

What data do you get?

Stories (dataset, one row per story)

Field	What it tells you
title, subtitle, url, image	The story
authorName, authorUsername, authorFollowers, authorUrl	Who wrote it and how big their audience is
publication, publicationSlug, publicationFollowers, publicationUrl	Where it was published, when it was
publishedAt, updatedAt, daysSincePublished	When
claps, responses	How readers reacted
readingTimeMinutes, wordCount, language	Length and language
tags, isMemberOnly	Medium's tags; whether it is behind the membership paywall
markdown, text, fullTextIsPreview	Full text, when Full text is on
sourceType, source	Which tag, search, author or publication found it

SUMMARIES holds one summary per source: median claps, responses and reading time, member-only share, newest and oldest story, top 10 stories, top 10 authors by claps and 20 related tags. STORIES_CSV has every story as flat columns.

Example: 8 October 2026

1,105 stories with full text in 2 minutes 58 seconds from two tags, one search, two authors, one publication and one story address.

Source	Stories	Median claps	Top story
tag artificial-intelligence, newest	100	0	173 claps, all published that day
tag python, newest	97	0	500 claps
search "rag pipeline"	300	1	"Building a RAG Pipeline for 10M+ Documents", 1,977
author @zulie	300	2,483	"If You Want to Be a Creator, Delete All...", 69,400
author karpathy	8 (the whole profile)	1,801	"Software 2.0", 61,029
publication better-programming	300	193	2,977

• Newest stories in a big tag are hours old and have almost no claps yet. For what works, use Top this month or Top this year, or set Minimum claps.
• With Top this year and at least 10 claps, the python tag gave 403 stories in 76 seconds: Medium shows 100 per view, the rest came from the other views and search.

Pricing

$0.001 per story, plus $0.001 per story for the full text when you switch it on.

• 1,000 stories: $1 (with full text $2)
• The run above: 1,105 stories with full text, $2.21

Good to know

• Member-only stories: their metadata is complete; the full text is Medium's public preview only, flagged with fullTextIsPreview.
• 100 stories per tag view: Medium itself shows at most 100 stories for each view of a tag. Ask for more and the scraper adds the other views and a search for the tag, so several hundred unique stories per tag are normal.
• Authors and publications are read newest first, all the way back.
• Personal data: only the public author of each story (name, username, follower count). Commenters, readers and followers are never collected.
• Same story twice (found by a tag and by a search) is saved and charged once.
• No login, no API key, no proxies needed.

Input

Field	Meaning
Tags	Medium tags, one per line
Tag stories	Newest, trending, top this week, month, year or of all time
Search	Searches, one per line
Authors	@username, username or profile address
Publications	better-programming or the publication's address
Story addresses	Single stories
Maximum stories per source	1 to 5,000 (default 50)
Full text	Off by default
Images in the full text	On by default
Minimum claps	0 by default
Only stories from the last (days)	0 = any date

Integrations

Export to JSON, CSV, Excel or Google Sheets, or connect through the Apify API, webhooks, Make, Zapier and n8n. Schedule it daily with Only stories from the last 1 day to follow a topic, or send the Markdown straight into a vector database.