# LinkedIn Page Post Pay Per Result PPR (`expected_knight/linkedin-page-post-pay-per-result-ppr`) Actor Pull the posts from any public Linkedin page/profiles in a structured format. - **URL**: https://apify.com/expected\_knight/linkedin-page-post-pay-per-result-ppr.md - **Developed by:** [Ritozpal](https://apify.com/expected_knight) (community) - **Categories:** Social media - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.02 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Agent A — LinkedIn Posts Fetches LinkedIn posts via Agent B (`Uv1ePwZ3vMp9az67S`). Give it one or more LinkedIn profile or company URLs and it forwards the request to Agent B, then passes the resulting dataset back untouched. ### Input | Field | Type | Required | Description | |---------------------|------------------|----------|---------------------------------------------------------------------------------------------------------------| | `targetUrls` | array of string | Yes | LinkedIn profile/company URLs who posted or re-posted the content. | | `includeQuotePosts` | boolean | No | Filter out quote posts (shared posts with comments). Default `true` (all posts included). | | `includeReposts` | boolean | No | Filter out reposts (shared posts without comments). Default `true` (all posts included). | | `scrapeComments` | boolean | No | Scrape comments on posts. Comments are charged as a separate post and pushed into the dataset. Default `false`. | | `postNestedComments` | boolean | No | Also scrape reply comments (comments on comments), not just top-level comments. Default `false`. | | `maxComments` | integer | No | Maximum comments to scrape per post. `0` scrapes all comments. Default `0`. | | `scrapeReactions` | boolean | No | Scrape reactions on posts. Reactions are charged as a separate post and pushed into the dataset. Default `false`. | | `postNestedReactions`| boolean | No | Also scrape reactions on comments, not just reactions on posts. Default `false`. | | `maxReactions` | integer | No | Maximum reactions to scrape per post. `0` scrapes all reactions. Default `0`. | | `maxPosts` | integer | No | Maximum posts to scrape per search query. `0` scrapes all posts. Default `0`. | | `postedLimit` | string | No | Only scrape posts posted within this time window: `any`, `1h`, `24h`, `week`, `month`, `3months`, `6months`, `year`. Default `any`. | #### Example input ```json { "targetUrls": [ "https://www.linkedin.com/in/sundarpichai/" ], "includeQuotePosts": true, "includeReposts": true, "scrapeComments": false, "postNestedComments": false, "maxComments": 5, "scrapeReactions": false, "postNestedReactions": false, "maxReactions": 5, "maxPosts": 5, "postedLimit": "3months" } ```` ### Output All input parameters are forwarded as-is to Agent B (`Uv1ePwZ3vMp9az67S`), and Agent B's dataset output is pushed back to this Actor's dataset untouched — no fields are added, removed, or renamed. **Data You'll Receive:** - Post content - Author information - Social engagement metrics - Media: images, videos, links, Comments and reactions - Content of Re-posts ### Usage 1. Provide one or more LinkedIn profile/company URLs in `targetUrls`. 2. Optionally toggle `includeQuotePosts` / `includeReposts` to filter shared content. 3. Optionally enable `scrapeComments` and/or `scrapeReactions`, capping each with `maxComments` / `maxReactions` (`0` = unlimited). Note these are billed as separate posts. 4. Optionally set `maxPosts` to cap posts per query, and `postedLimit` to restrict to a recent time window (`any`, `1h`, `24h`, `week`, `month`, `3months`, `6months`, `year`). 5. Run the Actor. It calls Agent B with these parameters and relays Agent B's dataset back untouched. # Actor input Schema ## `targetUrls` (type: `array`): LinkedIn profile/company URLs who posted or re-posted the content. ## `includeQuotePosts` (type: `boolean`): Toggle to filter out quote posts (shared posts with comments). By default, all posts are scraped. ## `includeReposts` (type: `boolean`): Toggle to filter out reposts (shared posts without comments). By default, all posts are scraped. ## `scrapeComments` (type: `boolean`): Enable to scrape comments to posts. Comments will be charged as a separate post and pushed into the dataset. Each post will also contain a nested list of its own comments. ## `postNestedComments` (type: `boolean`): Enable to also scrape reply comments (comments on comments), not just top-level comments. ## `maxComments` (type: `integer`): Maximum number of comments to scrape per post. If set to 0, all comments are scraped. ## `scrapeReactions` (type: `boolean`): Enable to scrape reactions to posts. Reactions will be charged as a separate post and pushed into the dataset. Each post will also contain a nested list of its own reactions. ## `postNestedReactions` (type: `boolean`): Enable to also scrape reactions on comments, not just reactions on posts. ## `maxReactions` (type: `integer`): Maximum number of reactions to scrape per post. If set to 0, all reactions are scraped. ## `maxPosts` (type: `integer`): Maximum number of posts to scrape per search query. This overrides pagination. If set to 0, all posts are scraped. ## `postedLimit` (type: `string`): Only scrape posts posted within this time window. ## Actor input object example ```json { "targetUrls": [ "https://www.linkedin.com/in/sundarpichai/" ], "includeQuotePosts": true, "includeReposts": true, "scrapeComments": false, "postNestedComments": false, "maxComments": 0, "scrapeReactions": false, "postNestedReactions": false, "maxReactions": 0, "maxPosts": 0, "postedLimit": "any" } ``` # 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 = { "targetUrls": [ "https://www.linkedin.com/in/sundarpichai/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("expected_knight/linkedin-page-post-pay-per-result-ppr").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 = { "targetUrls": ["https://www.linkedin.com/in/sundarpichai/"] } # Run the Actor and wait for it to finish run = client.actor("expected_knight/linkedin-page-post-pay-per-result-ppr").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 '{ "targetUrls": [ "https://www.linkedin.com/in/sundarpichai/" ] }' | apify call expected_knight/linkedin-page-post-pay-per-result-ppr --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=expected_knight/linkedin-page-post-pay-per-result-ppr", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Page Post Pay Per Result PPR", "description": "Pull the posts from any public Linkedin page/profiles in a structured format.", "version": "0.0", "x-build-id": "rWDEVdCMAmZCovF9p" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/expected_knight~linkedin-page-post-pay-per-result-ppr/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-expected_knight-linkedin-page-post-pay-per-result-ppr", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/expected_knight~linkedin-page-post-pay-per-result-ppr/runs": { "post": { "operationId": "runs-sync-expected_knight-linkedin-page-post-pay-per-result-ppr", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/expected_knight~linkedin-page-post-pay-per-result-ppr/run-sync": { "post": { "operationId": "run-sync-expected_knight-linkedin-page-post-pay-per-result-ppr", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "targetUrls" ], "properties": { "targetUrls": { "title": "Target URLs", "type": "array", "description": "LinkedIn profile/company URLs who posted or re-posted the content.", "items": { "type": "string" } }, "includeQuotePosts": { "title": "Include quote posts", "type": "boolean", "description": "Toggle to filter out quote posts (shared posts with comments). By default, all posts are scraped.", "default": true }, "includeReposts": { "title": "Include reposts", "type": "boolean", "description": "Toggle to filter out reposts (shared posts without comments). By default, all posts are scraped.", "default": true }, "scrapeComments": { "title": "Scrape comments", "type": "boolean", "description": "Enable to scrape comments to posts. Comments will be charged as a separate post and pushed into the dataset. Each post will also contain a nested list of its own comments.", "default": false }, "postNestedComments": { "title": "Post nested comments", "type": "boolean", "description": "Enable to also scrape reply comments (comments on comments), not just top-level comments.", "default": false }, "maxComments": { "title": "Max comments", "minimum": 0, "type": "integer", "description": "Maximum number of comments to scrape per post. If set to 0, all comments are scraped.", "default": 0 }, "scrapeReactions": { "title": "Scrape reactions", "type": "boolean", "description": "Enable to scrape reactions to posts. Reactions will be charged as a separate post and pushed into the dataset. Each post will also contain a nested list of its own reactions.", "default": false }, "postNestedReactions": { "title": "Post nested reactions", "type": "boolean", "description": "Enable to also scrape reactions on comments, not just reactions on posts.", "default": false }, "maxReactions": { "title": "Max reactions", "minimum": 0, "type": "integer", "description": "Maximum number of reactions to scrape per post. If set to 0, all reactions are scraped.", "default": 0 }, "maxPosts": { "title": "Max posts", "minimum": 0, "type": "integer", "description": "Maximum number of posts to scrape per search query. This overrides pagination. If set to 0, all posts are scraped.", "default": 0 }, "postedLimit": { "title": "Posted limit", "enum": [ "any", "1h", "24h", "week", "month", "3months", "6months", "year" ], "type": "string", "description": "Only scrape posts posted within this time window.", "default": "any" } } }, "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 } } } } } } } } } } ```