# Medium Article & Engagement Scraper (`fanndev/medium-engagement-scraper`) Actor Scrape Medium articles with their clap counts and response counts: by topic, by author, by publication, or one article at a time with its full text. Includes paywall status, publication, tags and author details. No login. - **URL**: https://apify.com/fanndev/medium-engagement-scraper.md - **Developed by:** [Faisal Ahdan naufal](https://apify.com/fanndev) (community) - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.20 / 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 Article & Engagement Scraper Scrape Medium articles **with their clap counts** — the number that tells you which posts actually landed. Pull a whole topic, an author's back catalogue, a publication's latest articles with their full text, or a single article end to end. ### Why use this actor - **Engagement, not just headlines.** Claps and response counts per article, so you can rank a topic by what readers actually responded to rather than by what was published most recently. - **35 articles per topic in a single request** — the richest slice this source gives out. - **Paywall status on every article**, so you know which ones your readers can open. - **Full article text** where Medium publishes it, in HTML and plain text. - **Author and publication resolved**, not left as internal references: name, handle, and the publication each article ran in. - **No account, no login, no API key.** - **Honest about what each surface gives.** Where a figure is not published, the field is explicitly empty and a flag tells you why — no zeros standing in for missing data. ### How it works 1. You pick a mode and give it a topic, author, publication or article URL. 2. The actor reads Medium's public pages and pulls out each article with its engagement figures. 3. Results stream into your dataset — download as JSON, CSV or Excel, or load into a database. Run it on a schedule against the topics you care about and you have a record of what performs. ### Modes | Mode | What you get | Per request | | --- | --- | --- | | `tag` | Articles for a topic, **with claps** | ~35 articles | | `author` | An author's recent articles, **with claps** | ~10 articles | | `publication` | A publication's latest articles **with the article text** (no claps) | 10 articles | | `post` | One article with its full text | 1 article | | `reference` | Check the source is live and what each surface returns | — | **Which modes give claps, and which give text, is a property of the source, not a choice.** Topic and author pages publish engagement but only a preview; publication feeds publish the article text but no engagement. Every record's `_source` field says which you got. ### Input ```json { "mode": "tag", "tags": ["artificial-intelligence", "programming"], "tagSection": "top", "maxItems": 200, "maxConcurrency": 3, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ``` | Field | Type | Description | | --- | --- | --- | | `mode` | string | `tag` (default), `author`, `publication`, `post` or `reference`. | | `tag` / `tags` | string / array | Topics. A name, slug or `/tag/` URL all work. | | `tagSection` | string | `top` (~35 articles), `archive` (newest, ~20) or `recommended` (~10). | | `author` / `authors` | string / array | Medium handles or profile URLs, e.g. `@medium`. | | `publication` / `publications` | string / array | Publication slugs or URLs, e.g. `better-programming`. | | `postUrl` / `postUrls` | string / array | Full Medium article URLs. | | `maxItems` | integer | Maximum records per run. Default 200. | | `maxConcurrency` | integer | Pages fetched in parallel. Default 3. | | `proxyConfiguration` | object | Residential proxy, on by default. Keep it on for scheduled runs. | ### Output Real output from `mode: "tag"`: ```json { "postId": "adf87621fb4a", "title": "The End of Patterns", "subtitle": "On recovery of a practice", "url": "https://medium.com/design-bootcamp/the-end-of-patterns-adf87621fb4a", "slug": "the-end-of-patterns-adf87621fb4a", "clapCount": 77, "responseCount": 1, "isPaywalled": true, "visibility": "LOCKED", "isSeries": false, "publishedAt": "2026-09-16T07:26:39Z", "updatedAt": "2026-09-16T07:26:39Z", "publishedAtEpochMs": 1789543599243, "authorName": "Joe Alterio", "authorUsername": "joealterio_7106", "authorId": "14f8b20ac0d7", "authorBio": null, "authorFollowers": null, "publicationName": "Bootcamp", "publicationSlug": "design-bootcamp", "publicationId": "48e972f5c24e", "tags": ["ai", "design", "product-design", "creativity", "patterns"], "previewImageAlt": "Antique game of Go Fish, with duos of matching cards.", "previewImageId": "1*JRC2jcK0mhRmAprJfaEimw.jpeg", "hasFullContent": false, "tagQueried": "artificial-intelligence", "tagSection": "top", "recordType": "POST", "_query": "artificial-intelligence", "_input": "artificial-intelligence", "_source": "medium-tag-apollo-top", "_scrapedAt": "2026-09-18T05:32:16Z" } ``` Real output from `mode: "publication"` (content truncated here): ```json { "postId": "3f456a51ff99", "title": "Calling AWS Bedrock from code", "url": "https://medium.com/better-programming/calling-aws-bedrock-from-code-3f456a51ff99?source=rss----d0b105d10f0a---4", "authorName": "Thomas Reid", "publishedAtRaw": "Fri, 10 Nov 2023 17:35:02 GMT", "updatedAtRaw": "2023-11-10T17:35:02.467Z", "tags": ["bedrock", "python", "llm", "boto3", "ai"], "contentHtml": "

' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "tag": "artificial-intelligence", "tags": [ "artificial-intelligence", "programming" ], "author": "@medium", "publication": "better-programming" }; // Run the Actor and wait for it to finish const run = await client.actor("fanndev/medium-engagement-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 = { "tag": "artificial-intelligence", "tags": [ "artificial-intelligence", "programming", ], "author": "@medium", "publication": "better-programming", } # Run the Actor and wait for it to finish run = client.actor("fanndev/medium-engagement-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 '{ "tag": "artificial-intelligence", "tags": [ "artificial-intelligence", "programming" ], "author": "@medium", "publication": "better-programming" }' | apify call fanndev/medium-engagement-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,fanndev/medium-engagement-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/DnjoE1PTgFj9qbnGA/builds/LgynouKN06NpbPRrp/openapi.json