![Linkedin Profile Posts Scraper [NO COOKIES] avatar](https://images.apifyusercontent.com/hB7hXeOxlDYVMGao7nIQ9tgueXsUlcC8b8w9MDGxvO4/rs:fill:250:250/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vdFhKaEQ5SFNvdTY4N2NEUTQtYWN0b3ItTFFRSVhOOU90aGY4ZjdSNW4tS1loTXZ4SVB0Vy1saW5rZWRpbl9jb2xvcmVkXzIuanBn.webp){kind=avatar}
Pricing
$5.00 / 1,000 results
Scrape LinkedIn posts data for a given LinkedIn profile including post content, reactions, comments count, and media attachments
4.7 (38)
Pricing
$5.00 / 1,000 results
419
9.4K
2.3K
Issues response
1.3 days
Last modified
8 days ago
You can access the Linkedin Profile Posts Scraper [NO COOKIES] programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
![Linkedin Profile Reactions [NO COOKIES] avatar](https://images.apifyusercontent.com/ysX-xkGflKONnTmAUTxVzvXzAjz7cn8KdszFUc9qGNE/rs:fill:76:76/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vdFhKaEQ5SFNvdTY4N2NEUTQtYWN0b3ItRk5oS0ZqZUw4aFdRdE1lWkktUGVqNEtWOGpxMi1saW5rZWRpbl9jb2xvcmVkXzIuanBn.webp){kind=avatar}
{ "openapi": "3.0.1", "info": { "version": "0.1", "x-build-id": "Nl8UfHVKJjTk9LoFo" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/apimaestro~linkedin-profile-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-apimaestro-linkedin-profile-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/apimaestro~linkedin-profile-posts/runs": { "post": { "operationId": "runs-sync-apimaestro-linkedin-profile-posts", "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/apimaestro~linkedin-profile-posts/run-sync": { "post": { "operationId": "run-sync-apimaestro-linkedin-profile-posts", "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 Username or Url", "type": "string", "description": "LinkedIn profile username (e.g., 'satyanadella' or 'linkedin.com/in/satyanadella')", "default": "satyanadella" }, "page_number": { "title": "Page Number", "minimum": 1, "type": "integer", "description": "Page number for pagination (first page returns a pagination token for subsequent pages)", "default": 1 }, "pagination_token": { "title": "Pagination Token", "type": "string", "description": "Token from previous page response for paginated requests (optional, only needed for pages after first). For example, to get result from page 2, use pagination token from response of page 1" }, "limit": { "title": "Result limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Optional: Limit the number of results per page (1-100)", "default": 100 }, "total_posts": { "title": "Total Posts to Scrape (if set, it will override manual pagination settings)", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Enable automatic pagination: Set the total number of posts to scrape. This will override manual pagination settings." } } }, "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 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for Linkedin Profile Posts Scraper [NO COOKIES] from the options below:
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