# Medium Scraper - Articles, Full Text & Whole Archives (`clearpath/medium-scraper`) Actor Scrape every Medium article in a tag, author or publication, not just the latest 10. Full text as Markdown, HTML and plain text, plus claps, responses, reading time, tags, images, code blocks and author and publication profiles. Keyword search past 1,000 results, lists, any Medium link. No login. - **URL**: https://apify.com/clearpath/medium-scraper.md - **Developed by:** [ClearPath](https://apify.com/clearpath) (community) - **Categories:** News, Social media, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.49 / 1,000 stories 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? 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 [![1,047 articles. 6,549 comments. In 38.9 seconds.](https://api.apify.com/v2/key-value-stores/rfpznBPgVbyLYqNaQ/records/medium-scraper-readme-hero-refresh-v2-554063ce787b.png)](https://console.apify.com/actors/IRgwoYrBv3LN1x7eD/input) From **Clearpath**, creators of [Reddit Search Scraper](https://apify.com/clearpath/reddit-search-scraper), [Reddit Post Comments Scraper](https://apify.com/clearpath/reddit-post-comments-bulk-scraper) and [Reddit Subreddit Posts Scraper](https://apify.com/clearpath/reddit-subreddit-posts-scraper). **200,000+ combined lifetime runs** across these content research scrapers. ### Why choose this Medium scraper? - **Fast bulk exports.** 1,047 articles and 6,549 comments in 38.9 seconds in a local test. Apify run times vary. - **Text ready for analysis.** Plain text, Markdown and HTML, plus claps, dates and author profiles. Free articles include full text; member-only articles include public previews. - **Deeper collection.** Collect tag, author and publication archives. Keyword searches continue past 1,000 results. Include comments and replies alongside each article. Get Medium articles #### Try it: 20 articles from Better Programming ```json { "publications": [ "better-programming" ], "maxItemsPerSource": 20, "includeContent": true, "includeResponses": true, "maxResponsesPerPost": 20 } ``` [Open the actor input](https://console.apify.com/actors/IRgwoYrBv3LN1x7eD/input) and paste this in JSON mode. Export up to 20 articles with up to 20 comments and replies each as JSON, CSV or Excel. Increase `maxItemsPerSource` for more articles and `maxResponsesPerPost` for more comments. For large archives, set a date window or maximum run cost.
Clearpath Content Research  •  Articles, posts and threads with full text
  Medium Articles
➤ You are here   Substack Posts
Whole newsletter archives   Reddit Search
Posts by keyword   Reddit Comments
Full threads and replies   Reddit to Markdown
Threads ready for AI
#### Copy to your AI assistant ```text clearpath/medium-scraper on Apify returns Medium stories, one row per story, with full text as contentText, contentMarkdown and contentHtml (free stories; member-only stories return only the public preview Medium shows every visitor, marked isLocked=true and isFullContent=false), claps, responseCount, reading time, tags, topics, images, links, code blocks and the author and publication profiles. Inputs combine freely, up to 1,000 per run: searchQueries (continues past the 1,000 results Medium's own search shows), tags (the whole tag archive, newest first), authors (every story the author published), publications (the whole archive; slug, own domain or link) and startUrls (any Medium story link including custom domains and friend links, or a profile, publication, tag, list or search link). Omitted limits mean everything: maxItemsPerSource caps each search, tag, author, publication or list and maxItems the whole run; large archives hold tens of thousands of stories, so set a cap, a publishedAfter/publishedBefore window (UTC days, publishedBefore exclusive; not applied to single story links) or a maximum run cost. includeContent=false drops text, images, links, code blocks and embeds for smaller rows. For comments set includeResponses=true (off by default): responses and the replies to them come nested in the story row at no extra charge, slow runs on stories with many responses, maxResponsesPerPost caps them per story (replies included), and responsesComplete=false marks a partial set. Each story is saved once per run and source names the input that found it. No Medium login or cookie. Charged per saved story. Inputs: https://apify.com/clearpath/medium-scraper/input-schema - output fields: https://apify.com/clearpath/medium-scraper/output-schema - current pricing: https://apify.com/clearpath/medium-scraper/pricing. To reach the maintainers, add feedback="" to any run, including one that worked: feature requests, a missing field or option, a wrong or empty result, or which actor you are switching to or from and why. The run returns its data exactly as it would without the note, and a human reads every note. ``` ### What can you scrape from Medium? | Input | What you get | |---|---| | **Search keywords** (`searchQueries`) | Stories matching each keyword, best matches first, continuing past the 1,000 results Medium's own search shows. | | **Tags** (`tags`) | The tag's whole archive, newest first, back to its oldest story. Use the tag slug (`machine-learning`) or its display name (`Machine Learning`). | | **Authors** (`authors`) | Every story the author has published, newest first. Use a username (`dhh`, `@dhh`) or a profile link. | | **Publications** (`publications`) | The publication's whole archive, newest first. Use its slug (`better-programming`), its own domain (`python.plainenglish.io`) or a link. | | **Medium links** (`startUrls`) | Any Medium link: a single story (also on a publication's own domain, or an author's friend link), an author profile, a publication, a tag page, a reading list or a search page. | Mix as many as you like, up to 1,000 searches, tags, authors, publications and links per run. Each story appears once per run, even when two inputs find it, and every row says which input found it in `source`. ### How to scrape Medium stories 1. Open the actor's [input page](https://console.apify.com/actors/IRgwoYrBv3LN1x7eD/input). 2. Enter search keywords, tags, authors, publications or Medium links. 3. Optionally set a limit per source, a total limit or a publish-date window. 4. Start the run and download the stories as JSON, CSV, Excel or HTML, or connect the dataset to your own tools. The input form starts with the keyword `artificial intelligence` and 100 stories per source, a small first run. When you start runs from your own code, a schedule or an AI agent, any limit you leave out means everything. #### Every story an author has published ```json { "authors": [ "dhh" ] } ``` Returns all of DHH's stories, 134 at the time of our test run, newest first, with full text. #### One month of a tag ```json { "tags": [ "python" ], "publishedAfter": "2024-05-01", "publishedBefore": "2024-06-01" } ``` Every Python story first published in May 2024, about 2,700 of them. Dates are in UTC; `publishedAfter` includes its day and `publishedBefore` excludes its day. The window works for searches, tags, authors, publications and lists. #### A whole publication, capped ```json { "publications": [ "better-programming" ], "maxItemsPerSource": 1000 } ``` The 1,000 newest stories of Better Programming, which took about 80 seconds in our test run. Remove the cap for the whole archive, more than 15,000 stories. #### A keyword search past 1,000 results ```json { "searchQueries": [ "python" ], "maxItemsPerSource": 2500 } ``` 2,500 unique stories for one keyword. Medium's own search stops at 1,000; this actor keeps going. #### Specific stories, profiles, lists and tag pages ```json { "startUrls": [ { "url": "https://medium.com/p/3bd3267cc151" }, { "url": "https://dhh.medium.com/" }, { "url": "https://medium.com/tag/rust" }, { "url": "https://medium.com/search?q=htmx" } ], "maxItemsPerSource": 20 } ``` A story link returns that story. A profile, publication, tag, list or search link returns its stories, here capped at 20 each. A reading list returns the stories still live on Medium; removed stories in a list are skipped. #### Stories with their responses ```json { "startUrls": [ { "url": "https://medium.com/p/3bd3267cc151" } ], "includeResponses": true, "maxResponsesPerPost": 5 } ``` Adds the first 5 responses (Medium's comments) inside the story's row, replies included, in the order Medium shows readers: each response followed by its replies. Responses cost nothing extra; they make runs slower on stories with many responses. #### Metadata only ```json { "tags": [ "python" ], "maxItemsPerSource": 1000, "includeContent": false } ``` Leaves out the story text, images, links, code blocks and embeds. Rows are smaller; everything else stays. #### Input options | Parameter | Type | Default | Meaning | |---|---|---|---| | `searchQueries` | list of text | none | Keywords to search stories for, one search each. | | `tags` | list of text | none | Tag slugs or display names; each returns the whole tag archive. | | `authors` | list of text | none | Usernames or profile links; each returns every story by that author. | | `publications` | list of text | none | Publication slugs, own domains or links; each returns the whole archive. | | `startUrls` | list of links | none | Any Medium story, profile, publication, tag, list or search link. | | `maxItemsPerSource` | integer | none (everything) | Stop each search, tag, author, publication or list after this many stories. | | `maxItems` | integer | none (no total limit) | Stop the whole run after this many stories. | | `publishedAfter` | date | none | Only stories first published on or after this day (UTC). | | `publishedBefore` | date | none | Only stories first published before this day (UTC). | | `includeContent` | true/false | `true` | Story text as plain text, Markdown and HTML, plus images, links, code blocks and embeds. | | `includeResponses` | true/false | `false` | Each story's responses inside its row. | | `maxResponsesPerPost` | integer | none (all) | With responses on, keep at most this many per story, replies included. | The [Input tab](https://apify.com/clearpath/medium-scraper/input-schema) has the full description of every option. ### What data do you get from Medium? One row per story, with up to 59 fields: | Group | Fields | |---|---| | **Story** | `postId`, `title`, `excerpt`, `url`, `shortUrl`, `canonicalUrl`, `uniqueSlug`, `language`, `license`, `type`, `visibility`, `isLocked`, `isFullContent`, `memberOnlySource` | | **Text** | `contentText`, `contentMarkdown`, `contentHtml`, `images` (url, size, alt text, caption, link), `links` (including mentioned members and jumps within the story), `codeBlocks` (language, code), `embeds` (with size or thumbnail), `topHighlight` (the passage readers highlighted most), `highlights` (every top highlight) | | **Engagement** | `clapCount`, `voterCount` (readers who clapped), `responseCount`, `readingTimeMinutes`, `wordCount`, optional `responses` | | **Dates** | `publishedAt`, `latestPublishedAt`, `updatedAt`, `createdAt`, `pinnedAt`, `pinnedByAuthorAt` (pinned to the author's profile), `boostedAt` (when Medium boosted the story), `scrapedAt` | | **Author** | `authorName`, `authorUsername`, `authorUrl`, `author` (bio, avatar, followers, following, Medium membership and tier, member since, book author, X username, newsletter) | | **Publication** | `publicationName`, `publication` (slug, link, own domain, description, tagline, short description, followers, avatar, logo, X and Facebook pages), `featuredInPublications`, `isFeaturedInPublication` | | **Topics** | `tags`, `tagDetails` (each tag's story and follower count), `topics`, `series` | | **Images and SEO** | `imageUrl`, `image` (size and alt text), `seo` (SEO title and description, meta description, social title and description) | | **Flags** | `isPublished`, `isSeries`, `isShortform`, `isNewsletter`, `allowsResponses`, `responsesLocked`, `inResponseToPostId`, `platform` | | **Source** | `source`: which search, tag, author, publication, list or link found the story | A real row from the Python tag, a free story in a publication with charts and code: ```json { "postId": "7e6e37e25355", "title": "The Vehicles Killing More American Pedestrians Are SUVs and Pickups", "url": "https://medium.com/python-gis-workflows/the-vehicles-killing-more-american-pedestrians-are-suvs-and-pickups-7e6e37e25355", "excerpt": "US pedestrian deaths were 1,292 higher in 2023 than in 2017. Federal crash records show which vehicles account for most of the rise.", "authorName": "nunacode", "authorUsername": "nunacode", "authorUrl": "https://medium.com/@nunacode", "publicationName": "Python GIS Workflows", "publishedAt": "2026-10-10T15:37:29.194Z", "latestPublishedAt": "2026-10-10T15:37:29.194Z", "updatedAt": "2026-10-10T16:37:03.288Z", "clapCount": 0, "voterCount": 0, "responseCount": 0, "readingTimeMinutes": 4.9, "wordCount": 1189, "language": "en", "isLocked": false, "isFullContent": true, "tags": [ "road-safety", "transportation", "data-journalism", "cities", "python" ], "imageUrl": "https://miro.medium.com/v2/1*JOpjs7Jx437HDJK1ql1brA.png", "topHighlight": null, "highlights": [], "type": "story", "platform": "medium", "shortUrl": "https://medium.com/p/7e6e37e25355", "canonicalUrl": "https://medium.com/python-gis-workflows/the-vehicles-killing-more-american-pedestrians-are-suvs-and-pickups-7e6e37e25355", "uniqueSlug": "the-vehicles-killing-more-american-pedestrians-are-suvs-and-pickups-7e6e37e25355", "visibility": "public", "memberOnlySource": null, "isPublished": true, "isSeries": false, "isShortform": false, "isNewsletter": false, "allowsResponses": true, "responsesLocked": false, "isFeaturedInPublication": false, "inResponseToPostId": null, "createdAt": "2026-10-10T15:27:27.899Z", "pinnedAt": null, "pinnedByAuthorAt": null, "boostedAt": null, "license": "All rights reserved", "author": { "id": "f4c89ee3e0f0", "name": "nunacode", "username": "nunacode", "url": "https://medium.com/@nunacode", "customDomain": null, "bio": null, "imageUrl": "https://miro.medium.com/v2/resize:fill:176:176/1*XKaj1cvRuyzKrwkacrXOmA.jpeg", "followerCount": 16, "followingCount": 20, "isMediumMember": false, "memberSince": null, "membershipTier": null, "isBookAuthor": false, "twitterUsername": null, "newsletterId": "e7f9a3187f6b" }, "publication": { "id": "d9726c783628", "name": "Python GIS Workflows", "slug": "python-gis-workflows", "url": "https://medium.com/python-gis-workflows", "domain": null, "description": "Practical guides for working with spatial data in Python.", "tagline": "Practical guides for working with spatial data in Python.", "shortDescription": "Practical guides for working with spatial data in Python.", "followerCount": 6, "avatarUrl": "https://miro.medium.com/v2/1*RXXKOwSPFEzcU-LxFklq2A.png", "logoUrl": null, "twitterUsername": null, "facebookPageName": null, "createdAt": null }, "featuredInPublications": [], "tagDetails": [ { "slug": "road-safety", "name": "Road Safety", "storyCount": 1319, "followerCount": 28 }, { "slug": "transportation", "name": "Transportation", "storyCount": 21841, "followerCount": 448128 }, { "slug": "data-journalism", "name": "Data Journalism", "storyCount": 2040, "followerCount": 855 }, { "slug": "cities", "name": "Cities", "storyCount": 16841, "followerCount": 205737 }, { "slug": "python", "name": "Python", "storyCount": 260251, "followerCount": 5041216 } ], "topics": [ { "name": "Data Science", "slug": "data-science" } ], "image": { "id": "1*JOpjs7Jx437HDJK1ql1brA.png", "url": "https://miro.medium.com/v2/1*JOpjs7Jx437HDJK1ql1brA.png", "width": 1735, "height": 900, "alt": "Line chart of US pedestrian deaths by striking vehicle, 2017–2023: cars around 2,500–2,950 a year, SUVs rising from 1,223 to 1,686, pickups from 1,017 to 1,270, buses and heavy trucks around 400–550, vans around 300." }, "series": null, "seo": { "title": "SUVs, Pickups and the Rise in US Pedestrian Deaths", "description": "US pedestrian deaths were 1,292 higher in 2023 than in 2017, and 716 of the extra deaths involved SUVs or pickups. What federal FARS crash data shows.", "metaDescription": null, "socialTitle": null, "socialDescription": null }, "source": { "type": "tag", "value": "python", "title": "Python", "url": "https://medium.com/tag/python" }, "scrapedAt": "2026-10-10T21:15:24.854Z", "contentText": "The Vehicles Killing More American Pedestrians Are SUVs and Pickups\n\nUS pedestrian deaths were 1,292 higher in 2023 than in 2017. Federal crash records show which vehicles account for most of the rise.\n\nPedestrians killed on US roads each year, by the type of vehicle that struck them.", "contentMarkdown": "## The Vehicles Killing More American Pedestrians Are SUVs and Pickups\n\n### US pedestrian deaths were 1,292 higher in 2023 than in 2017. Federal crash records show which vehicles account for most of the rise.", "contentHtml": "

The Vehicles Killing More American Pedestrians Are SUVs and Pickups

\n

US pedestrian deaths were 1,292 higher in 2023 than in 2017. Federal crash records show which vehicles account for most of the rise.

", "images": [ { "url": "https://miro.medium.com/v2/1*JOpjs7Jx437HDJK1ql1brA.png", "width": 1735, "height": 900, "alt": "Line chart of US pedestrian deaths by striking vehicle, 2017–2023: cars around 2,500–2,950 a year, SUVs rising from 1,223 to 1,686, pickups from 1,017 to 1,270, buses and heavy trucks around 400–550, vans around 300.", "caption": "Pedestrians killed on US roads each year, by the type of vehicle that struck them.", "link": null }, { "url": "https://miro.medium.com/v2/1*AnjT_oJslZ9AyD2lknTjNQ.png", "width": 1757, "height": 830, "alt": "Stacked area chart of the share of pedestrian deaths by striking vehicle, where its type is known: SUVs rising from 22.1% to 25.9%, pickups around 18–19%, cars falling from 46.3% to 41.4%; SUVs and pickups together from 40.6% to 45.3%.", "caption": "Share of pedestrian deaths by striking vehicle type, among those where the type is known. Buses, trucks, vans and motorcycles are not shown.", "link": null } ], "links": [ { "url": "https://www.spatialworkflow.io/fars-fatal-crashes-python/", "text": "usual quirks of a long-running federal dataset" }, { "url": "https://www.spatialworkflow.io/fars-code-breaks-other-years/", "text": "change from year to year" } ], "codeBlocks": [ { "language": "python", "code": "import zipfile\nfrom pathlib import Path\n\nimport pandas as pd\nimport requests" } ], "embeds": [] } ``` To keep this example short, `contentText`, `contentMarkdown` and `contentHtml` are cut to their opening lines, `codeBlocks` shows the opening lines of the first of its 10 blocks, and `links` shows 2 of its 4 links. Every key in the row is present, and the dataset holds the full values: this story's `contentText` is 8,875 characters. `responses` and `responsesComplete` appear only when responses are turned on. `series` holds the series' id, slug, title, subtitle and description when the story is a Medium series. #### Member-only stories Medium shows every logged-out visitor a preview of a member-only story. This actor returns that preview and marks the row, so you always know whether you have the whole story: ```json { "postId": "a134ec2448ab", "visibility": "member-only", "memberOnlySource": "author", "isLocked": true, "isFullContent": false, "wordCount": 3368, "readingTimeMinutes": 13.1 } ``` This row's `contentText` holds the first 160 words of the story's 3,368. All other fields (title, author, claps, tags, publication and the rest) are complete for member-only stories too. #### Responses With `includeResponses` on, each story's responses come inside its row, each response followed by the replies to it: ```json { "postId": "3bd3267cc151", "title": "The AI apocalypse is already here", "responseCount": 12, "responses": [ { "postId": "cdb8fba36f54", "url": "https://medium.com/@amalrikmaia/you-are-free-to-not-use-ai-based-services-your-entire-life-if-you-want-cdb8fba36f54", "authorName": "Amalrik Maia", "authorUsername": "amalrikmaia", "text": "You are free to not use AI based services your entire life if you want.\nYou are already doing a good job exposing those companies that are making bad use of AI.", "clapCount": 11, "responseCount": 1, "publishedAt": "2018-11-28T12:52:37.267Z", "isLocked": false, "inResponseToPostId": "3bd3267cc151" }, { "postId": "e1d04b581a4a", "url": "https://medium.com/@tallulahdirac/the-person-being-protected-is-not-the-parent-but-the-babysitter-e1d04b581a4a", "authorName": "Tallulah Dirac", "authorUsername": "tallulahdirac", "text": "The person being protected is not the parent, but the babysitter. Being snooped on against your will (or under pressure from the job market) is not at all analogous to voluntarily taking a (non-addictive) drug.", "clapCount": 0, "responseCount": 1, "publishedAt": "2018-12-27T11:02:28.160Z", "isLocked": false, "inResponseToPostId": "cdb8fba36f54" } ], "responsesComplete": true } ``` Two of this story's 15 responses are shown, and the first response's text is cut to its opening lines: a response, then a reply to it. `inResponseToPostId` names the story for a response and the response for a reply, so the whole thread can be rebuilt; `responseCount` on the story counts its direct responses (12), not the replies. `maxResponsesPerPost` counts replies too. `responsesComplete` is `false` when a story's responses could only be partly collected; the responses already collected are kept. #### Other fields that appear when a story has them ```json [ { "postId": "3596e36fe7f9", "isFeaturedInPublication": true, "featuredInPublications": [ { "id": "2ddc5c035323", "name": "Codexa", "slug": "codexa" } ] }, { "postId": "f1d04f4f194b", "embeds": [ { "type": "link_card", "url": "https://basecamp.com/svn", "title": "Try Basecamp free for 30 days\nTrusted by millions, Basecamp puts everything you need to get work done in one streamlined, organized place.basecamp.com", "caption": null, "thumbnailUrl": "https://miro.medium.com/v2/0*hHlzu-IeaP4lBQHM." } ] } ] ``` `featuredInPublications` comes from a Python story featured by the Codexa publication; `embeds` from one of DHH's stories, which ends with a link card. `embeds` also holds embedded media such as code snippets and videos. The dataset's **Stories**, **Engagement** and **Text and media** views show the most useful columns. JSON keeps the nested author, publication and text fields intact; CSV and Excel flatten them into columns. ### What can you do with Medium data? - **Archive export and backup.** Download every story of a tag, author or publication, or your own stories as Markdown for a move to another blog. - **AI and RAG datasets.** Clean Markdown and plain text with language, word count, topics and license on every story. - **Content and trend research.** Follow a topic month by month with a date window, or compare claps and responses across tags and publications. - **Competitor and publication monitoring.** Schedule a run on the publications and authors you watch with `publishedAfter` set to your last run. - **Author discovery.** Find writers by topic with their followers, bio, membership and engagement per story. - **SEO and content gaps.** Titles, SEO descriptions, tags with their story and follower counts, and every outbound link. ### How much does it cost to scrape Medium? You pay per story saved to your dataset. The Free plan pays $4.99 per 1,000 stories; Bronze $4.59, Silver $4.19, Gold and above $3.49. Full text, profiles and responses are included in that price; responses never cost extra. Story charges for common runs on the Free plan: - 100 newest stories of a tag: about $0.50 - Every story by one author with 134 stories: about $0.67 - 1,000 stories: $4.99 Set a maximum cost per run in the run options: the run stops at that amount, keeps every story it saved and says so in the log. A run that finds no stories has no story charges. The [Pricing tab](https://apify.com/clearpath/medium-scraper/pricing) shows every charge. Free Apify plan users can run this actor; there are no extra limits beyond your plan's usage. ### FAQ **Why do Medium's RSS feeds show only 10 stories?** Medium's feeds list the latest 10 stories of a tag, author or publication, and scrapers that read them inherit that limit. This actor collects the whole archive instead: every story of an author, all of a publication, and every story in a tag back to its oldest month. **Can I download all articles from a Medium author?** Yes. Enter the username in `authors` and leave `maxItemsPerSource` empty. Every story the author has published comes back, newest first, with full text for free stories. **Can I export Medium articles to Markdown?** Yes. Every free story comes as `contentMarkdown` with headings, lists, links, images and fenced code blocks, plus `contentText` and `contentHtml`. **Can I get the full text of member-only stories?** No. Member-only stories come with the preview Medium shows every logged-out visitor, marked `isFullContent: false` and `isLocked: true`. All other fields are complete. The actor does not get around Medium's paywall. **Do I need a Medium account, login or cookie?** No. Nothing to set up beyond your Apify account. **Can I get claps and comments for Medium stories?** Yes. Every row has `clapCount`, `voterCount` and `responseCount`. Turn on `includeResponses` to add the responses themselves (text, author, claps, date) inside each story's row at no extra charge. **How do I get every story in a tag or publication?** Enter the tag or publication and leave `maxItemsPerSource` empty. Big archives hold tens of thousands of stories, so use a date window or a maximum cost per run to collect only what you need. A run stops at its timeout, one hour by default, which covers roughly 40,000 stories; for a bigger archive raise the timeout in the run options or split it into date windows. **Does keyword search stop at 1,000 results?** Medium's own search shows at most 1,000 stories per keyword. This actor continues past that; one search for "python" returned 2,500 unique stories. **Can I build a Medium dataset for analysis or AI training?** Yes: full text, language, word count, tags, topics and license come with every story. Stories remain their authors' work; check each story's `license` and Medium's terms before republishing or training on them. **Can I scrape a story on a custom domain or from a friend link?** Yes. Paste the link in `startUrls`: publication domains such as `python.plainenglish.io`, author domains such as `dhh.medium.com` and friend links all work. **Can I download Medium articles as PDF?** There is no PDF output. `contentHtml` and `contentMarkdown` convert to PDF with any document tool. **Why did my run return fewer stories than I expected?** Check the log. It names any tag, author, publication or link Medium did not recognize, and says when the run stopped at your maximum cost. Date windows and limits also narrow the results, a reading list skips stories that were removed from Medium, and a story found by two inputs is saved once. **How fresh is the data?** Every run collects stories as Medium shows them at that moment, including claps and responses. Schedule the actor to keep a dataset current. **Is it legal to scrape Medium?** The actor collects stories Medium shows to any logged-out visitor. How you use them is your responsibility: respect authors' copyright and each story's license, Medium's terms, and data protection law for personal details such as author profiles. ### Support Questions, a missing field or a run that did not return what you expected: open an issue in the **Issues** tab or email **max@mapa.slmail.me**. Reports with the run ID get answered fastest. Medium is a trademark of A Medium Corporation. This actor is independent and is not affiliated with or endorsed by Medium. # Actor input Schema ## `searchQueries` (type: `array`): Words to search Medium stories for, one search per line, e.g. `rust async` or `startup fundraising`. Best matches come first; each search continues by publish date well past the 1,000 stories Medium's own search shows. ## `tags` (type: `array`): Medium tags such as `python` or `machine-learning` (a display name like `Machine Learning` works too). Collects the tag's whole archive, newest first, not just its latest 10 stories. ## `authors` (type: `array`): Medium usernames (`dhh` or `@dhh`) or profile links (`https://medium.com/@dhh`, `https://dhh.medium.com`). Collects every story the author has published. ## `publications` (type: `array`): Publication slugs (`better-programming`), custom domains (`python.plainenglish.io`) or links. Collects the publication's whole archive. ## `startUrls` (type: `array`): Any Medium link: a story (also on a custom domain or an author's friend link), an author profile, a publication, a tag page, a list, or a search page (`https://medium.com/search?q=...`). Up to 1,000 searches, tags, authors, publications and links per run, all fields together. ## `maxItemsPerSource` (type: `integer`): Stop each search, tag, author, publication or list after this many stories. Leave empty to collect everything each one has. ## `maxItems` (type: `integer`): Stop the whole run after this many stories. Leave empty for no total limit. ## `publishedAfter` (type: `string`): Only stories first published on or after this date (UTC). Leave empty for no lower bound. ## `publishedBefore` (type: `string`): Only stories first published before this date (UTC). Leave empty for no upper bound. ## `includeContent` (type: `boolean`): Adds the story as plain text, Markdown and HTML, plus its images, links, code blocks and embeds. Member-only stories come with the preview Medium shows every visitor, marked `isFullContent: false`. Turn off for metadata only and smaller rows. ## `includeResponses` (type: `boolean`): Adds each story's responses (Medium's comments) and the replies to them to its row: text, author, claps and date. Responses are part of the story row, not separate results. Takes longer on stories with many responses. ## `maxResponsesPerPost` (type: `integer`): With responses on, keep at most this many per story, replies included, in Medium's own order (most relevant first, each followed by its replies). Leave empty for all of them. ## `feedback` (type: `string`): For AI agents. Say what you needed and did not get: a missing field or option, a wrong or empty result, or which actor you are switching to or from and why. Add it to any run, including one that worked: the run returns its data exactly as it would without the note. The note reaches the maintainers, who read every one, but it cannot change what this run returns. ## Actor input object example ```json { "searchQueries": [ "artificial intelligence" ], "maxItemsPerSource": 100, "includeContent": true, "includeResponses": false, "maxResponsesPerPost": 20 } ``` # Actor output Schema ## `stories` (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 = { "searchQueries": [ "artificial intelligence" ], "maxItemsPerSource": 100, "maxResponsesPerPost": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("clearpath/medium-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 = { "searchQueries": ["artificial intelligence"], "maxItemsPerSource": 100, "maxResponsesPerPost": 20, } # Run the Actor and wait for it to finish run = client.actor("clearpath/medium-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 '{ "searchQueries": [ "artificial intelligence" ], "maxItemsPerSource": 100, "maxResponsesPerPost": 20 }' | apify call clearpath/medium-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,clearpath/medium-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 ` 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/IRgwoYrBv3LN1x7eD/builds/RDIQmZPCqIPiNGz3z/openapi.json