# LinkedIn Post Metrics Scraper (`iron-crawler/linkedin-post-metrics-scraper`) Actor Scrape full engagement metrics from any LinkedIn post URL. Returns likes, comments, shares, full reaction type breakdown, post text, author details, and attached media. Clean JSON output. No cookies or login required. - **URL**: https://apify.com/iron-crawler/linkedin-post-metrics-scraper.md - **Developed by:** [Iron Crawler](https://apify.com/iron-crawler) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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. 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 ## LinkedIn Post Metrics Scraper Pull complete engagement analytics from any LinkedIn post — likes, comments, shares, reactions broken down by type, author data, and post content — via a clean API-friendly output. No session tokens. No cookies. No LinkedIn account. ### Endpoint Behavior Each post URL call returns a single structured record with: - **Engagement counts:** `num_likes`, `num_comments`, `num_shares` - **Reaction breakdown:** Like, Celebrate, Support, Love, Insightful, Funny — count per type - **Post metadata:** full post text, post URL, `created_at` timestamp, `urn` - **Author record:** name, headline/description, profile URL, avatar image, entity type (person vs company) - **Media:** attached image URLs, document previews, video thumbnails - **Repost signal:** `is_repost` flag + original post reference if applicable - **URN references:** `reactions_urn`, `comments_urn`, `reposts_urn` — usable for deeper extraction ### Input ```json { "post_urls": [ "https://www.linkedin.com/posts/satyanadella_ai-activity-7201234567890-ABCD/", "https://www.linkedin.com/feed/update/urn:li:activity:7201234567890/" ] } ```` Multiple URL formats accepted — both `/posts/` and `/feed/update/urn:li:activity:` resolve correctly. ### Sample Output ```json { "post_url": "https://www.linkedin.com/posts/satyanadella_ai-activity-7201234567890-ABCD/", "post_text": "Excited to share...", "created_at": "2024-11-01T14:22:00Z", "author_name": "Satya Nadella", "author_headline": "Chairman and CEO at Microsoft", "author_linkedin_url": "https://www.linkedin.com/in/satyanadella/", "author_type": "person", "num_likes": 4821, "num_comments": 312, "num_shares": 198, "reaction_counts": { "LIKE": 2900, "PRAISE": 1100, "EMPATHY": 400, "INTEREST": 321, "APPRECIATION": 100 }, "is_repost": false, "media_attachments": [], "reactions_urn": "urn:li:activity:7201234567890", "comments_urn": "urn:li:activity:7201234567890", "reposts_urn": "urn:li:activity:7201234567890" } ``` ### Use Cases - **Content performance benchmarking** — compare engagement rates across posts or authors programmatically - **Influencer vetting** — verify claimed engagement figures before a partnership - **Monitoring pipelines** — call via Apify API on a schedule to track post performance over time - **Data journalism** — extract engagement data for research or reporting at scale ### Pricing $15 per 1,000 results. No subscription. Charged only on successful records. ### FAQ **What URL formats are supported?** Both `/posts/username_slug-activity-ID/` and `/feed/update/urn:li:activity:ID/` formats work. Share URLs from the LinkedIn app also resolve. **Is pagination included?** This actor returns top-level post analytics only. It does not paginate through individual commenters or reactors — those are separate endpoints. URNs returned in the output can be used to trigger deeper extraction. **What does `is_repost` mean?** If the post is a reshare of another post, `is_repost: true` and the `header` field contains the original content reference. # Actor input Schema ## `linkedin_url` (type: `string`): The LinkedIn Post Url to scraping ## Actor input object example ```json { "linkedin_url": "https://www.linkedin.com/posts/anthropicresearch_statement-on-comments-from-the-secretary-activity-7433337757906923520-p2nH" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("iron-crawler/linkedin-post-metrics-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("iron-crawler/linkedin-post-metrics-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 iron-crawler/linkedin-post-metrics-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iron-crawler/linkedin-post-metrics-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Metrics Scraper", "description": "Scrape full engagement metrics from any LinkedIn post URL. Returns likes, comments, shares, full reaction type breakdown, post text, author details, and attached media. Clean JSON output. No cookies or login required.\n", "version": "1.0", "x-build-id": "oXh2FtDvHfz6iJyJd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iron-crawler~linkedin-post-metrics-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iron-crawler-linkedin-post-metrics-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/iron-crawler~linkedin-post-metrics-scraper/runs": { "post": { "operationId": "runs-sync-iron-crawler-linkedin-post-metrics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/iron-crawler~linkedin-post-metrics-scraper/run-sync": { "post": { "operationId": "run-sync-iron-crawler-linkedin-post-metrics-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "linkedin_url" ], "properties": { "linkedin_url": { "title": "LinkedIn Post Url", "type": "string", "description": "The LinkedIn Post Url to scraping", "default": "https://www.linkedin.com/posts/anthropicresearch_statement-on-comments-from-the-secretary-activity-7433337757906923520-p2nH" } } }, "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 } } } } } } } } } } ```