# Medium Articles Scraper (`ef12/medium-articles-scraper`) Actor Scrape Medium articles by tag or author using the public JSON API. Get titles, subtitles, authors, claps, reading time, and tags. - **URL**: https://apify.com/ef12/medium-articles-scraper.md - **Developed by:** [Daniel Wilson](https://apify.com/ef12) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 Articles Scraper ### What does Medium Articles Scraper do? **Medium Articles Scraper** extracts articles from [Medium](https://medium.com) using Medium's public JSON API — no authentication required. Given a **tag** (e.g., "python", "ai", "programming") or an **author username**, it returns structured data for each article: title, URL, author, publication date, reading time, clap count, tags, and a preview snippet. Unlike browser-based scrapers that load JavaScript and render pages, this Actor uses Medium's internal JSON feed endpoints. By sending an `Accept: application/json` header, Medium returns structured JSON instead of HTML, making the scraper faster, cheaper, and more reliable. Each run delivers clean, pre-formatted data straight to your Apify dataset where you can download it as JSON, CSV, HTML, or Excel — or pipe it into any API-connected tool. ### Why use Medium Articles Scraper? Medium hosts millions of articles across technology, data science, design, business, self-improvement, and hundreds of other topics. Its recommendations engine surfaces high-quality content daily, but accessing that content at scale — for research, analysis, content curation, or monitoring — requires automation. **Use cases:** - **Content curation and newsletters** — Automatically collect trending articles from your niche (e.g., "ai", "blockchain", "startups") every day and build a curated newsletter or digest without manual copy-pasting. - **Market and competitive intelligence** — Monitor what authors in your industry are publishing. Track article topics, publishing frequency, and engagement (claps) to identify content gaps and understand what resonates with readers. - **Data science and NLP research** — Gather a corpus of articles by topic for sentiment analysis, topic modeling, trend detection, or training language models. The structured output (title, tags, preview text) gives you clean data to work with. - **SEO and content strategy** — Analyze which article structures, titles, and topics perform best in your domain. Use the data to inform your own content strategy, identify high-engagement topics, and study successful authors. - **Academic research** — Collect articles on specific subjects for literature reviews, citation analysis, or sociological studies of online publishing trends. - **Personal knowledge base** — Archive articles from your favorite authors or tags for offline reading, reference, or personal study. - **Social media and marketing** — Find trending content in your industry to share on social media, identify influencers (authors with high clap counts), and track content themes over time. ### How to use Medium Articles Scraper Using this Actor is straightforward. Follow these steps: 1. **Open the Actor** in Apify Console. You'll see the Input tab with a form. 2. **Configure the input:** - Enter a **tag** such as "python", "machine-learning", or "design" to fetch trending or latest articles in that topic. - Optionally enter a **username** (without the @ symbol) to fetch articles by a specific author. - Choose **sort order**: "trending" (top recommended articles) or "newest" (latest published). - Set **max results** to limit the number of articles returned (up to 100). 3. **Run the Actor**. The scraper will call Medium's JSON API, parse the response, and push each article as a dataset item. 4. **Download results** from the dataset tab. You can export as JSON, CSV, HTML, or Excel. You can also run it programmatically via Apify API — useful for scheduled runs or integration into larger data pipelines. ### Input The Actor accepts the following input parameters. Configure them in the **Input** tab before running. | Field | Type | Default | Description | | ------- | ------ | --------- | ------------- | | `tag` | string | "programming" | Medium tag to search (e.g., "python", "ai", "javascript"). Optional if username is provided. | | `username` | string | — | Medium username to fetch articles by a specific author (without @). Optional if tag is provided. | | `sort` | string | "trending" | Sort order: "trending" (recommended) or "newest" (latest). | | `max_results` | integer | 25 | Maximum articles to return (1–100). | ### Output Each article is stored as an item in the default dataset. Here's an example of a single result: ```json { "title": "10 Python Libraries Every Developer Should Know", "url": "https://medium.com/@pythoncoder/10-python-libraries-every-developer-should-know-abc123def456", "subtitle": "A curated list of essential Python libraries for 2025", "author": "Python Coder", "author_url": "https://medium.com/@pythoncoder", "published_at": "2025-06-15T08:30:00+00:00", "reading_time_minutes": 8, "claps": 1420, "tags": ["python", "programming", "data-science"], "preview_text": "A curated list of essential Python libraries for 2025" } ```` You can download the dataset in various formats such as JSON, HTML, CSV, or Excel from the **Dataset** tab after each run. ### Data table | Field | Type | Description | | ------- | ------ | ------------- | | `title` | string | Article title | | `url` | string | Full medium.com URL | | `subtitle` | string | null | Subtitle or brief description | | `author` | string | Author's display name | | `author_url` | string | Link to author's Medium profile | | `published_at` | string | ISO 8601 publication timestamp | | `reading_time_minutes` | integer | Estimated reading time in minutes | | `claps` | integer | null | Number of claps (likes) | | `tags` | array of strings | Article tags/categories | | `preview_text` | string | First 200 characters of the subtitle | ### Pricing / Cost estimation This Actor uses **Pay Per Event (PPE)** pricing: - **First 10 results per run are free.** No charge. - **Beyond 10 results, each additional result costs $0.003** (charged via `apify-actor-start` and `result` events). - **Starting a run costs $0.00005** (apify-actor-start event). A typical run with 25 articles would cost approximately $0.00005 (start) + 15 × $0.003 (15 chargeable results) = **$0.045**. For 100 articles: $0.00005 + 90 × $0.003 = **$0.27**. ### Tips and Advanced options - **Use specific tags** for better results. "machine-learning" returns more focused articles than "ML" or "AI". - **Keep max\_results at 25 or lower** for quick exploratory runs, then increase for production batch runs. - **Combine with other Apify Actors** — pipe the output dataset into an NLP Actor for text analysis, or into a Google Sheets integration for live dashboards. - **Schedule regular runs** via Apify Console to monitor trending content in your niche daily. ### FAQ, disclaimers, and support **Q: Is authentication required?**\ No. This Actor uses Medium's public JSON endpoints which do not require API keys or login. **Q: Can I scrape paywalled article content?**\ This Actor extracts metadata only — title, URL, author, claps, etc. It does not bypass paywalls or extract full article text behind the paywall. **Q: Is this against Medium's Terms of Service?**\ This Actor accesses publicly available data from Medium's public feeds. You should review Medium's Terms of Service and use the tool responsibly. Do not republish scraped content without permission. **Q: Why use JSON API instead of a browser?**\ Medium returns JSON data when you set `Accept: application/json` on certain URLs. This is significantly faster and cheaper than running a full browser (Playwright/Puppeteer) — no JavaScript rendering overhead, no memory spikes, and no complex selector maintenance. **Known limitations:** - Medium may rate-limit requests from certain IP ranges. Running on Apify's platform with proxy rotation provides the best reliability. - The JSON API endpoints are not officially documented and may change. This Actor is maintained to adapt to any changes. **Need help or have feedback?** Open an issue on the [GitHub repository](https://github.com/apify/medium-articles-scraper) or contact the author. Custom scrapers, integrations, and enterprise solutions are also available on request. # Actor input Schema ## `tag` (type: `string`): Medium tag to fetch articles for (e.g., "python", "ai", "programming"). Optional if username is provided. ## `username` (type: `string`): Medium username to fetch articles from a specific author (without @). Optional if tag is provided. ## `sort` (type: `string`): Sort order — trending (top recommendations) or newest (latest articles). ## `max_results` (type: `integer`): Maximum number of articles to return (max 100). ## Actor input object example ```json { "tag": "python", "sort": "trending", "max_results": 25 } ``` # Actor output Schema ## `results` (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 = { "tag": "python", "username": "" }; // Run the Actor and wait for it to finish const run = await client.actor("ef12/medium-articles-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": "python", "username": "", } # Run the Actor and wait for it to finish run = client.actor("ef12/medium-articles-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).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": "python", "username": "" }' | apify call ef12/medium-articles-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ef12/medium-articles-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Medium Articles Scraper", "description": "Scrape Medium articles by tag or author using the public JSON API. Get titles, subtitles, authors, claps, reading time, and tags.", "version": "0.0", "x-build-id": "2b8giE1KBMZeTHgNo" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ef12~medium-articles-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ef12-medium-articles-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ef12~medium-articles-scraper/runs": { "post": { "operationId": "runs-sync-ef12-medium-articles-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ef12~medium-articles-scraper/run-sync": { "post": { "operationId": "run-sync-ef12-medium-articles-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "tag": { "title": "Tag", "maxLength": 50, "type": "string", "description": "Medium tag to fetch articles for (e.g., \"python\", \"ai\", \"programming\"). Optional if username is provided.", "default": "programming" }, "username": { "title": "Username", "maxLength": 100, "type": "string", "description": "Medium username to fetch articles from a specific author (without @). Optional if tag is provided." }, "sort": { "title": "Sort Order", "enum": [ "trending", "newest" ], "type": "string", "description": "Sort order — trending (top recommendations) or newest (latest articles).", "default": "trending" }, "max_results": { "title": "Max Results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of articles to return (max 100).", "default": 25 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```