# Instagram Engagers(Likes and Comments) βœ… No cookies βœ… (`scraping_solutions/instagram-engagers-likes-and-comments-no-cookies`) Actor Instagram Engagers is a fast, flexible, and developer-friendly API designed to extract high-quality data from Instagram engagers based on posts. - **URL**: https://apify.com/scraping\_solutions/instagram-engagers-likes-and-comments-no-cookies.md - **Developed by:** [Scraping Solutions](https://apify.com/scraping_solutions) (community) - **Categories:** Social media, Lead generation - **Stats:** 392 total users, 10 monthly users, 99.4% runs succeeded, 16 bookmarks - **User rating**: 2.01 out of 5 stars ## Pricing from $0.50 / 1,000 engagers 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/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 ### πŸš€ What is Instagram Engagers Scraper? **Instagram Engagers** is a fast, flexible, and developer-friendly API designed to extract high-quality data from Instagram engagers based on posts. Whether you're tracking trends, analyzing brand engagement, or researching influencer activity, our API gives you structured and relevant data in seconds. Unlike other tools, we focus on **precision and customization**: get only the fields you need, with no noise β€” perfect for data pipelines, dashboards, or direct integration into your apps. --- ### βš™οΈ How It Works 1. Send a POST request with your desired posts and result limits. 2. Instantly get structured data in JSON format. 3. Integrate it seamlessly into your workflows or tools. --- ### πŸ§ͺ Input Example ```json { "Posts": [ "https://www.instagram.com/p/DJHhRQzMLWD/" ], "dataToScrape": "comments", "resultsLimit": 50 } ```` *** ### πŸ“¦ Output Example ```json [ { "type": "comments", "link_post": "https://www.instagram.com/p/DJHhRQzMLWD", "comment_like_count": 0, "created_at": 1746135474, "hashtags": [], "like_count": 0, "mentions": [], "text": "πŸ”₯❀️πŸ”₯", "link_user": "https://www.instagram.com/loewensweets", "user.full_name": "LΓΆwensweets Mathias Malsch YoganaschschwerkΒ© .", "user.id": "21275061902", "user.is_private": false, "user.is_verified": true, "user.profile_pic_url": "https://scontent-atl3-1.cdninstagram.com/v/t51.2885-19/483470307_3147281325409963_1945960373892621567_n.jpg?stp=dst-jpg_e0_s150x150_tt6&_nc_ht=scontent-atl3-1.cdninstagram.com&_nc_cat=107&_nc_oc=Q6cZ2QFoHo7e0HArwEbqNAI9dkolis-JmC_r99SyZP-p25f4RIxHULbVbkpL24e61dvdgiA&_nc_ohc=JkYYffsKvjsQ7kNvwEvglyn&_nc_gid=qp9bHu1uO6yHwvwykTEnjA&edm=AD93TDoBAAAA&ccb=7-5&oh=00_AfEwWwu7yXKNddBzVDwvd1rotNqrpEJyURUuDfvTZFzvmg&oe=681A0F06&_nc_sid=87e5dd", "user.username": "loewensweets" } ] ``` > πŸ”Ž *Only the most relevant fields are returned by default β€” keeping your payloads clean and focused.* *** ### 🎯 Key Features - πŸ“Œ Filtered fields: Only receive attributes you care about (e.g. URL, caption, likes). - ⚑ Fast & lightweight: Optimized for speed and low latency. - 🧩 Easy to integrate: Works with Python, JavaScript, or any language that supports HTTP. - πŸ’Ύ Multiple formats: Data available in JSON (CSV and Excel coming soon). - πŸ” Privacy-respecting: Only scrapes publicly available content. *** ### πŸ› οΈ Ideal For - Social media analytics - Brand monitoring - Influencer research - Trend discovery - Marketing intelligence *** ### βœ… Why Choose This API? - No complex setup β€” start scraping in seconds. - Designed for automation and scale. - Transparent pricing with generous free tier. - Focused output for serious data use cases β€” no clutter, no fluff. *** **Get started now** and turn hashtags into insights. # Actor input Schema ## `Posts` (type: `array`): Add a list of Posts. You can add them one per line. ## `resultsLimit` (type: `integer`): number of engagers for scraping, 1-200 are the allowed values ## `dataToScrape` (type: `string`): choose between comments and likes ## Actor input object example ```json { "Posts": [ "https://www.instagram.com/p/DJHhRQzMLWD/" ], "resultsLimit": 20, "dataToScrape": "comments" } ``` # 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 = { "Posts": [ "https://www.instagram.com/p/DJHhRQzMLWD/" ], "resultsLimit": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("scraping_solutions/instagram-engagers-likes-and-comments-no-cookies").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "Posts": ["https://www.instagram.com/p/DJHhRQzMLWD/"], "resultsLimit": 20, } # Run the Actor and wait for it to finish run = client.actor("scraping_solutions/instagram-engagers-likes-and-comments-no-cookies").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "Posts": [ "https://www.instagram.com/p/DJHhRQzMLWD/" ], "resultsLimit": 20 }' | apify call scraping_solutions/instagram-engagers-likes-and-comments-no-cookies --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraping_solutions/instagram-engagers-likes-and-comments-no-cookies", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Engagers(Likes and Comments) βœ… No cookies βœ…", "description": "Instagram Engagers is a fast, flexible, and developer-friendly API designed to extract high-quality data from Instagram engagers based on posts.", "version": "0.0", "x-build-id": "nyYYPr2L4R6fiFuSO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraping_solutions~instagram-engagers-likes-and-comments-no-cookies/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraping_solutions-instagram-engagers-likes-and-comments-no-cookies", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraping_solutions~instagram-engagers-likes-and-comments-no-cookies/runs": { "post": { "operationId": "runs-sync-scraping_solutions-instagram-engagers-likes-and-comments-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraping_solutions~instagram-engagers-likes-and-comments-no-cookies/run-sync": { "post": { "operationId": "run-sync-scraping_solutions-instagram-engagers-likes-and-comments-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "Posts", "resultsLimit", "dataToScrape" ], "properties": { "Posts": { "title": "Posts", "type": "array", "description": "Add a list of Posts. You can add them one per line.", "items": { "type": "string" } }, "resultsLimit": { "title": "Number of commenters o likers per post", "minimum": 1, "maximum": 200, "type": "integer", "description": "number of engagers for scraping, 1-200 are the allowed values", "default": 20 }, "dataToScrape": { "title": "What do you want to scrape from each post?", "enum": [ "comments", "likes" ], "type": "string", "description": "choose between comments and likes", "default": "comments" } } }, "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 } } } } } } } } } } ```