# LinkedIn Post Comments Scraper � Get Commenter Profiles (`kuldip_parmar/linkedin-post-comments-scraper`) Actor Extract comments from any LinkedIn post with commenter name, LinkedIn profile URL, job title, and more. No login or cookies required. Perfect for lead generation and engagement analysis. - **URL**: https://apify.com/kuldip\_parmar/linkedin-post-comments-scraper.md - **Developed by:** [kuldip parmar](https://apify.com/kuldip_parmar) (community) - **Categories:** Lead generation, Social media, Other - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $3.00 / 1,000 results 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.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 ## LinkedIn Post Comments Scraper — Get Commenter Profiles Extract every comment from any LinkedIn post and get the commenter's name, LinkedIn profile URL, job title, profile picture, comment text, likes, and timestamp — all in one flat dataset ready to export as CSV or JSON. **No login required. No cookies. No browser.** --- ### What you get Each row in the output dataset contains: | Field | Example | |-------|---------| | `commenterName` | Sarah Johnson | | `commenterLinkedinUrl` | https://www.linkedin.com/in/sarah-johnson | | `commenterPosition` | VP of Marketing at Acme Corp | | `commenterPictureUrl` | https://media.licdn.com/... | | `commentText` | Great insights — sharing this with my team. | | `commentUrl` | https://www.linkedin.com/feed/update/urn:li:... | | `commentedAt` | 2026-06-17T08:14:24.000Z | | `likes` | 12 | | `replies` | 3 | | `postUrl` | https://www.linkedin.com/posts/... | --- ### Use cases - **Lead generation** — identify who engaged with a competitor's or influencer's post, then reach out directly - **Audience research** — understand who follows and engages with a specific topic or creator - **Engagement analysis** — measure comment volume, sentiment, and top commenters on your own posts - **Sales prospecting** — find decision-makers who commented on relevant industry posts - **Influencer vetting** — audit the quality of an influencer's audience before a partnership --- ### How to use #### Step 1 — Find a LinkedIn post URL Copy the URL of any LinkedIn post. It looks like: ```` https://www.linkedin.com/posts/username\_some-title-ugcPost-1234567890-XXXX ``` Or a feed update URL: ``` https://www.linkedin.com/feed/update/urn:li:activity:1234567890/ ```` #### Step 2 — Set the input | Field | Required | Default | Description | |-------|----------|---------|-------------| | `postUrl` | Yes | — | Full URL of the LinkedIn post | | `maxComments` | No | 100 | Max comments to return (1–1,000) | | `scrapeReplies` | No | false | Include replies to comments as separate rows | #### Step 3 — Run and export Click **Start**, wait a few seconds, then export the dataset as **CSV**, **JSON**, or **Excel** from the dataset tab. --- ### Pricing This actor charges **$0.002 per comment** (pay-per-result). | Comments | Estimated cost | |----------|---------------| | 50 | ~$0.10 | | 100 | ~$0.20 | | 500 | ~$1.00 | | 1,000 | ~$2.00 | The minimum charge per run is $0.01. --- ### Example input ```json { "postUrl": "https://www.linkedin.com/posts/satyanadella_ai-activity-7472541762985664512-Vbei", "maxComments": 200, "scrapeReplies": false } ```` ### Example output ```json { "commenterName": "Sarah Johnson", "commenterLinkedinUrl": "https://www.linkedin.com/in/sarah-johnson", "commenterPosition": "VP of Marketing at Acme Corp", "commenterPictureUrl": "https://media.licdn.com/dms/image/...", "commentText": "This is exactly what our team has been discussing. Sharing this.", "commentUrl": "https://www.linkedin.com/feed/update/urn:li:ugcPost:7472541762985664512?commentUrn=...", "commentedAt": "2026-06-17T08:14:24.000Z", "likes": 12, "replies": 3, "postUrl": "https://www.linkedin.com/posts/satyanadella_ai-activity-7472541762985664512-Vbei" } ``` *** ### Limitations - Only public LinkedIn posts can be scraped (posts visible without logging in) - LinkedIn may limit the number of visible comments on very large posts - Reply counts reflect the total per LinkedIn, but replies are only included in results if `scrapeReplies` is enabled *** ### Support For issues or questions, open a bug report via the Issues tab on the actor page. **Disclaimer:** This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedIn® is a registered trademark of LinkedIn Corporation. # Actor input Schema ## `postUrl` (type: `string`): Full URL of the LinkedIn post you want to scrape comments from. ## `maxComments` (type: `integer`): Maximum number of comments to scrape. Set to 0 to scrape all available comments (up to 1,000). ## `scrapeReplies` (type: `boolean`): When enabled, replies to comments are also scraped and counted as separate results. ## Actor input object example ```json { "postUrl": "https://www.linkedin.com/posts/satyanadella_ai-activity-7472541762985664512-Vbei", "maxComments": 100, "scrapeReplies": false } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("kuldip_parmar/linkedin-post-comments-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 = {} # Run the Actor and wait for it to finish run = client.actor("kuldip_parmar/linkedin-post-comments-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 '{}' | apify call kuldip_parmar/linkedin-post-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kuldip_parmar/linkedin-post-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Comments Scraper � Get Commenter Profiles", "description": "Extract comments from any LinkedIn post with commenter name, LinkedIn profile URL, job title, and more. No login or cookies required. Perfect for lead generation and engagement analysis.", "version": "1.0", "x-build-id": "adrVSAroBfLPIwYGa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kuldip_parmar~linkedin-post-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kuldip_parmar-linkedin-post-comments-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/kuldip_parmar~linkedin-post-comments-scraper/runs": { "post": { "operationId": "runs-sync-kuldip_parmar-linkedin-post-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/kuldip_parmar~linkedin-post-comments-scraper/run-sync": { "post": { "operationId": "run-sync-kuldip_parmar-linkedin-post-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "postUrl" ], "properties": { "postUrl": { "title": "LinkedIn Post URL", "type": "string", "description": "Full URL of the LinkedIn post you want to scrape comments from." }, "maxComments": { "title": "Max Comments", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of comments to scrape. Set to 0 to scrape all available comments (up to 1,000).", "default": 100 }, "scrapeReplies": { "title": "Include Replies", "type": "boolean", "description": "When enabled, replies to comments are also scraped and counted as separate results.", "default": false } } }, "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 } } } } } } } } } } ```