![[NO COOKIES] LinkedIn Search Posts Scraper avatar](https://images.apifyusercontent.com/x1I7vmmzwUXCqfJGJ792cAlruYPuSPQbdnxLLC0zGC8/rs:fill:250:250/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vMFV6Z1BsTjFPZ3RzU2RkVVQtYWN0b3ItYXM2T0hrS1ZmQlpRRU8xcmstaFBkdVJJR1k0Ty1saW5rZWRpbi1zdmdyZXBvLWNvbS5zdmcucG5n.webp){kind=avatar}
Pricing
Pay per event
Easily and quickly search LinkedIn for posts matching your exact keywords and filters without needing to risk your own LinkedIn account or using your cookies.
0.0 (0)
Pricing
Pay per event
0
7
7
Last modified
7 days ago
You can access the [NO COOKIES] LinkedIn Search Posts Scraper 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 Posts Scraper [NO COOKIES] avatar](https://images.apifyusercontent.com/s_O_fFD0AYINXh8_JVN3CD3Ofg4QN-ZCDgVnobPoBp8/rs:fill:76:76/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vdFhKaEQ5SFNvdTY4N2NEUTQtYWN0b3ItTFFRSVhOOU90aGY4ZjdSNW4tS1loTXZ4SVB0Vy1saW5rZWRpbl9jb2xvcmVkXzIuanBn.webp){kind=avatar}
{ "openapi": "3.0.1", "info": { "version": "0.0", "x-build-id": "mEznoX4SVUuTWQ5OL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scary_good_apis~linkedin-search-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scary_good_apis-linkedin-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scary_good_apis~linkedin-search-posts/runs": { "post": { "operationId": "runs-sync-scary_good_apis-linkedin-search-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scary_good_apis~linkedin-search-posts/run-sync": { "post": { "operationId": "run-sync-scary_good_apis-linkedin-search-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query", "limiter" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "Search query for LinkedIn posts. Follows LinkedIn's search syntax. See https://www.linkedin.com/help/linkedin/answer/a524335 for more details." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of posts to return. Use this to control your costs.", "default": 10 }, "limiter": { "title": "Search Limiter", "type": "object", "description": "A limiter for the search request. Use either previousPost OR maxTime, not both.", "properties": { "previousPost": { "title": "Previous Post", "nullable": true, "type": "string", "pattern": "^urn:([^:]*:){2}[^:]*$", "description": "The URN (LinkedIn post ID) of the post to go back to at a maximum. IE Fetch post up until this post chronologically. Example: urn:li:activity:7250170481335758841" }, "maxTime": { "title": "Max Time", "nullable": true, "type": "integer", "minimum": 1, "description": "The timestamp in unix millisecond format between now and 7 days ago. Fetch posts up until this time. Example: 1695926400000 (Sep 28, 2023)" } }, "additionalProperties": false }, "filters": { "title": "Post Filters", "type": "object", "description": "Advanced filtering options for posts", "properties": { "posterType": { "type": "string", "enum": [ "Person", "Company" ], "description": "Filter by author type", "title": "Poster Type", "nullable": true }, "inGroup": { "type": "boolean", "description": "Only group posts (true) or no group posts (false)", "title": "In Group", "nullable": true }, "hasHashtags": { "type": "boolean", "description": "Filter posts with/without hashtags", "title": "Has Hashtags", "nullable": true }, "hasImage": { "type": "boolean", "description": "Filter posts with/without images", "title": "Has Image", "nullable": true }, "hasVideo": { "type": "boolean", "description": "Filter posts with/without videos", "title": "Has Video", "nullable": true }, "hasDocument": { "type": "boolean", "description": "Filter posts with/without documents", "title": "Has Document", "nullable": true }, "hasLink": { "type": "boolean", "description": "Filter posts with/without links", "title": "Has Link", "nullable": true }, "isCelebration": { "type": "boolean", "description": "Filter for celebration posts", "title": "Is Celebration", "nullable": true }, "isJobPosting": { "type": "boolean", "description": "Filter for job posting content", "title": "Is Job Posting", "nullable": true }, "isQuoting": { "type": "boolean", "description": "Filter for posts quoting other posts", "title": "Is Quoting", "nullable": true }, "permission": { "type": "object", "title": "Permissions", "description": "Filter by engagement permissions", "editor": "json", "properties": { "canReact": { "type": "boolean", "description": "Filter posts where user can react", "title": "Can React", "nullable": true }, "canComment": { "type": "boolean", "description": "Filter posts where user can comment", "title": "Can Comment", "nullable": true }, "canShare": { "type": "boolean", "description": "Filter posts where user can share", "title": "Can Share", "nullable": true }, "canMessage": { "type": "boolean", "description": "Filter posts where user can message author", "title": "Can Message", "nullable": true } } } } } } }, "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 [NO COOKIES] LinkedIn Search Posts Scraper 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: