# Instagram Followers and Following Scraper (`danek/instagram-followers-following-scraper`) Actor Scrape followers and following of an Instagram account. - **URL**: https://apify.com/danek/instagram-followers-following-scraper.md - **Developed by:** [Jan Danecki](https://apify.com/danek) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.15 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Instagram Followers and Following Scraper Extract follower and following data from public Instagram accounts at scale. This actor helps you collect and analyze Instagram followers and following for influencer research, audience intelligence, competitor monitoring, lead generation, and social media analytics. Simply provide one or more Instagram usernames and receive structured follower data that can be exported in JSON, CSV, Excel, or integrated directly into your workflows. ### Features * Scrape followers from public Instagram profiles * Process multiple accounts in a single run * Automatic pagination support * Structured output for easy analysis * Export results in multiple formats * API and automation friendly * Suitable for large-scale audience research * Built-in retry and error handling ### Common Use Cases #### Influencer Research Analyze the audiences of creators, influencers, and brands to identify potential partnerships and collaboration opportunities. #### Competitor Analysis Understand who follows competing brands and discover audience overlap across accounts. #### Lead Generation Build targeted prospect lists based on niche communities and industry-specific audiences. #### Market Research Study audience composition, engagement patterns, and growth opportunities within specific markets. #### Audience Discovery Find relevant users connected to industry leaders, creators, and competitors. ### Input Provide one or more Instagram usernames to collect follower information. Example: ```json { "usernames": ["mrbeast"], "maxResultsPerUser": 1000, "dataToScrape": "followers" ```` ### Output Each follower is returned as a separate dataset item. Example: ```json { "sourceUsername": "sample_user", "userId": 123434, "username": "123456789", "fullName": "asd", "profilePicUrl": "https://instagram.com/sample_user/profile_pic.jpg", "isVerified": true, "isPrivate": false, "type": "https://instagram.com/sample_user" } ``` ### Export Options Results can be exported as: - JSON - CSV - Excel - XML Or consumed directly through the Apify API. ### API Usage Run the actor programmatically using the Apify API and retrieve results from the generated dataset. This makes it easy to integrate with: - CRM systems - Data warehouses - Marketing platforms - Internal analytics tools - Automation workflows ### Performance Tips - Start with smaller follower limits when testing. - Use reasonable concurrency settings for large jobs. - Process accounts in batches for maximum reliability. - Schedule recurring runs to monitor audience changes over time. ### Limitations - Only publicly accessible Instagram data can be collected. - Availability depends on Instagram's current public interface. - Results may vary due to platform changes, restrictions, or rate limits. - Users should ensure compliance with applicable laws and platform policies. ### Who Is This For? - Growth marketers - Agencies - Influencer managers - Sales teams - Researchers - Data analysts - Social media professionals ### Support If you encounter issues or need additional features, open an issue. # Actor input Schema ## `usernames` (type: `array`): Instagram usernames ## `maxResultsPerUser` (type: `integer`): Max results to scrape per user ## `dataToScrape` (type: `string`): Choose whether to scrape followers or following ## Actor input object example ```json { "usernames": [ "instagram" ], "maxResultsPerUser": 50, "dataToScrape": "followers" } ``` # 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 = { "usernames": [ "instagram" ], "maxResultsPerUser": 50, "dataToScrape": "followers" }; // Run the Actor and wait for it to finish const run = await client.actor("danek/instagram-followers-following-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 = { "usernames": ["instagram"], "maxResultsPerUser": 50, "dataToScrape": "followers", } # Run the Actor and wait for it to finish run = client.actor("danek/instagram-followers-following-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 '{ "usernames": [ "instagram" ], "maxResultsPerUser": 50, "dataToScrape": "followers" }' | apify call danek/instagram-followers-following-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=danek/instagram-followers-following-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Followers and Following Scraper", "description": "Scrape followers and following of an Instagram account.", "version": "1.0", "x-build-id": "mWN4ycYFU5ee07hwu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/danek~instagram-followers-following-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-danek-instagram-followers-following-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/danek~instagram-followers-following-scraper/runs": { "post": { "operationId": "runs-sync-danek-instagram-followers-following-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/danek~instagram-followers-following-scraper/run-sync": { "post": { "operationId": "run-sync-danek-instagram-followers-following-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "usernames", "maxResultsPerUser", "dataToScrape" ], "properties": { "usernames": { "title": "usernames", "type": "array", "description": "Instagram usernames", "items": { "type": "string" } }, "maxResultsPerUser": { "title": "Max results per user", "type": "integer", "description": "Max results to scrape per user" }, "dataToScrape": { "title": "Followers or following", "enum": [ "followers", "following" ], "type": "string", "description": "Choose whether to scrape followers or following" } } }, "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 } } } } } } } } } } ```