# Medium Followers Scraper (`codingfrontend/medium-followers-scraper`) Actor Extract detailed posts, comments, profiles, engagement metrics, hashtags, and media attachments from Medium Followers. Engineered for social listening, user sentiment tracking, influencer outreach, and viral trend analysis. Features optimized proxy rotation to bypass rate limits. - **URL**: https://apify.com/codingfrontend/medium-followers-scraper.md - **Developed by:** [codingfrontend](https://apify.com/codingfrontend) (community) - **Categories:** Social media, Lead generation, Other - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 Followers Scraper Extract Medium user followers data including profile details, membership status, and bio information. ### Features - **Configurable result limit** — set exactly how many items to collect per run - **Cloud-ready** — runs on Apify with scheduling, proxy rotation, and webhook support ### Use Cases - Real-time news monitoring and media tracking - Content aggregation and curation pipeline automation - Topic and keyword trend tracking for SEO or research - Competitive content strategy and publishing cadence analysis ### Input | Field | Type | Default | Required | Description | |-------|------|---------|----------|-------------| | `usernames` | array | `—` | | List of Medium usernames without @ (e.g. ['karpathy', 'towardsdatascience']) | | `username` | string | `—` | | Single Medium username without @ (alternative to usernames list) | | `maxItems` | integer | `50` | | Maximum number of profiles to scrape | | `proxyConfiguration` | object | Apify Residential | | Proxy settings. Residential proxy is strongly recommended | ### Output Each scraped item includes: | Field | Description | |-------|-------------| | `username` | Social media handle or username | | `displayName` | Display name shown publicly on profile | | `bio` | Profile bio or about text | | `followerCount` | Follower count | | `followingCount` | Following count | | `profileUrl` | Profile url | #### Sample Output ```json { "username": "@handle", "displayName": "displayName_value", "bio": "bio_value", "followerCount": "followerCount_value", "followingCount": "followingCount_value", "profileUrl": "profileUrl_value" } ```` ### Notes - **Proxy**: Residential proxy is highly recommended — set `proxyConfiguration: { useApifyProxy: true, apifyProxyGroups: ["RESIDENTIAL"] }` - **Rate limits**: Keep `maxItems` reasonable per run to avoid IP bans or rate throttling - **Scheduling**: Use Apify Scheduler to run this actor on a recurring basis for a consistently fresh dataset # Actor input Schema ## `usernames` (type: `array`): List of Medium usernames without @ (e.g. \['karpathy', 'towardsdatascience']) ## `username` (type: `string`): Single Medium username without @ (alternative to usernames list) ## `maxItems` (type: `integer`): Maximum number of profiles to scrape ## `proxyConfiguration` (type: `object`): Proxy settings ## Actor input object example ```json { "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("codingfrontend/medium-followers-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 = { "proxyConfiguration": { "useApifyProxy": True } } # Run the Actor and wait for it to finish run = client.actor("codingfrontend/medium-followers-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 '{ "proxyConfiguration": { "useApifyProxy": true } }' | apify call codingfrontend/medium-followers-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=codingfrontend/medium-followers-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Medium Followers Scraper", "description": "Extract detailed posts, comments, profiles, engagement metrics, hashtags, and media attachments from Medium Followers. Engineered for social listening, user sentiment tracking, influencer outreach, and viral trend analysis. Features optimized proxy rotation to bypass rate limits.", "version": "1.0", "x-build-id": "VZLXP3NFnHiDkBdC9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/codingfrontend~medium-followers-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-codingfrontend-medium-followers-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/codingfrontend~medium-followers-scraper/runs": { "post": { "operationId": "runs-sync-codingfrontend-medium-followers-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/codingfrontend~medium-followers-scraper/run-sync": { "post": { "operationId": "run-sync-codingfrontend-medium-followers-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": { "usernames": { "title": "Medium Usernames", "type": "array", "description": "List of Medium usernames without @ (e.g. ['karpathy', 'towardsdatascience'])", "items": { "type": "string" } }, "username": { "title": "Single Medium Username", "type": "string", "description": "Single Medium username without @ (alternative to usernames list)" }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of profiles to scrape", "default": 50 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings" } } }, "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 } } } } } } } } } } ```