# LinkedIn Engagement Scraper — Comments & Reactions (`endspec/linkedin-instant-engagement-scraper`) Actor Scrape a LinkedIn member's recent comments and reactions. See what a person engages with — the posts, authors, and text. Fast, no cookies, no setup. - **URL**: https://apify.com/endspec/linkedin-instant-engagement-scraper.md - **Developed by:** [EndSpec](https://apify.com/endspec) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $4.70 / 1,000 engagement item returneds 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/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 ## LinkedIn Engagement Scraper — Comments & Reactions See what any LinkedIn member **engages with**. Give a profile handle and get back the person's recent **comments** and **reactions** — the posts they touched, who authored them, and the text — as clean, structured rows. No cookies, no login, no setup. ### What you get One row per engagement item: - `type` — `comment` or `reaction` - `reaction_type` — e.g. "Bill Gates likes this" (reactions) - `comment_text` — the comment the member wrote (comments) - `post_url`, `post_text` — the post they engaged with - `post_author_name`, `post_author_url` — who authored that post - `num_likes`, `num_comments` — engagement on the item - `created_at`, `item_urn` ### Input ```json { "username": "williamhgates", "include": "both", "count": 20 } ```` - **username** — a profile handle (`williamhgates`) or `/in/` URL. - **include** — `both` (default), `comments`, or `reactions`. - **count** — max items to return. > **Note on count:** person feeds return only the most recent page (~20 per type), so `count` is an upper bound, not a guarantee. ### Pricing - **Pay only per item returned.** No charge for failed runs or runs with zero results. - $0.0050 per item (launch) - Pay-as-you-go — no monthly subscription, no minimum ### Use cases - Social selling — find the posts and people a prospect actually engages with - Warm-intro and account-based outreach signals - Influencer and buyer-intent research - Competitive engagement analysis ### Why this actor - **Zero setup** — no cookies, no login; just run it - **Comments and reactions** in one actor - **Clean, flat schema** — import-ready for CRM or spreadsheet - **Pay-per-event** — only charged when items actually return ### Support If results are missing or the actor returns an error, our servers may be briefly busy or undergoing maintenance. Re-run a few minutes later — you will not be charged for failed items. # Actor input Schema ## `username` (type: `string`): A LinkedIn profile handle or /in/ URL. Examples: 'williamhgates', 'https://www.linkedin.com/in/williamhgates' ## `include` (type: `string`): Which engagement to return: the member's comments, their reactions, or both. ## `count` (type: `integer`): Max engagement items to return. Note: person feeds return only the most recent page (~20 per type). ## Actor input object example ```json { "username": "williamhgates", "include": "both", "count": 20 } ``` # Actor output Schema ## `dataset` (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 = { "username": "williamhgates" }; // Run the Actor and wait for it to finish const run = await client.actor("endspec/linkedin-instant-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 = { "username": "williamhgates" } # Run the Actor and wait for it to finish run = client.actor("endspec/linkedin-instant-engagement-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 '{ "username": "williamhgates" }' | apify call endspec/linkedin-instant-engagement-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=endspec/linkedin-instant-engagement-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Engagement Scraper — Comments & Reactions", "description": "Scrape a LinkedIn member's recent comments and reactions. See what a person engages with — the posts, authors, and text. Fast, no cookies, no setup.", "version": "0.1", "x-build-id": "sNndHfRw4LWeqW9pZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/endspec~linkedin-instant-engagement-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-endspec-linkedin-instant-engagement-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/endspec~linkedin-instant-engagement-scraper/runs": { "post": { "operationId": "runs-sync-endspec-linkedin-instant-engagement-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/endspec~linkedin-instant-engagement-scraper/run-sync": { "post": { "operationId": "run-sync-endspec-linkedin-instant-engagement-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "username" ], "properties": { "username": { "title": "Profile", "type": "string", "description": "A LinkedIn profile handle or /in/ URL. Examples: 'williamhgates', 'https://www.linkedin.com/in/williamhgates'" }, "include": { "title": "Include", "enum": [ "both", "comments", "reactions" ], "type": "string", "description": "Which engagement to return: the member's comments, their reactions, or both.", "default": "both" }, "count": { "title": "Number of Items", "minimum": 1, "maximum": 100, "type": "integer", "description": "Max engagement items to return. Note: person feeds return only the most recent page (~20 per type).", "default": 20 } } }, "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 } } } } } } } } } } ```