Linkedin User Posts
Under maintenancePricing
$3.00 / 1,000 posts
Scrape LinkedIn posts data for a given LinkedIn person profile including post text, post media attachments, reactions count, comments count, author basic info etc.
Pricing
$3.00 / 1,000 posts
Rating
5.0
(2)
Developer
Benjar Scraping API
Maintained by CommunityActor stats
2
Bookmarked
98
Total users
12
Monthly active users
a month ago
Last modified
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You can access the Linkedin User Posts programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
![Profile Posts Scraper for LinkedIn [No Cookies] avatar](https://images.apifyusercontent.com/2r1MxY4feneklD8BD7qTRtzsL_II2yIB-xqoqgYECkI/rs:fill:76:76/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vdFhKaEQ5SFNvdTY4N2NEUTQtYWN0b3ItTFFRSVhOOU90aGY4ZjdSNW4tOFpidkNJbW9JUy1hbS1sb2dvLnBuZw.webp){kind=avatar}
![Profile Comments Scraper for LinkedIn [No Cookies] avatar](https://images.apifyusercontent.com/u8ZkqL-wVvhhxkHGdW6pjTVkasgX_JajHXj0qeQeXLE/rs:fill:76:76/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vdFhKaEQ5SFNvdTY4N2NEUTQtYWN0b3ItN1ROY1JPZTFDMkNRRE8zd2wtQmZtWHZJYjJPby1hbS1sb2dvLnBuZw.webp){kind=avatar}
{ "openapi": "3.0.1", "info": { "version": "0.0", "x-build-id": "ByMHzzp6YZH5rneD1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benjarapi~linkedin-user-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benjarapi-linkedin-user-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/benjarapi~linkedin-user-posts/runs": { "post": { "operationId": "runs-sync-benjarapi-linkedin-user-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns 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"required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "profile" ], "properties": { "profile": { "title": "Profile URL or Username or URN of the LinkedIn profile to scrape", "type": "string", "description": "profile URL, username, or URN of the person to scrape (eg: \"https://www.linkedin.com/in/satyanadella\")" }, "maxPosts": { "title": "Maximum number of posts to collect", "type": "integer", "description": "Maximum number of posts to collect" } } }, "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": 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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 User Posts from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
You can also check out our other API clients: