Pricing
Pay per event
Extract leads from LinkedIn Sales Navigator with unlimited and safe Deep profile extraction + email search
Pricing
Pay per event
Rating
3.9
(4)
Developer
HarvestAPI
Actor stats
19
Bookmarked
236
Total users
32
Monthly active users
4.8 hours
Issues response
21 days ago
Last modified
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You can access the Linkedin Sales Navigator Lead Search Scraper 🍪 + email 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 Sales Navigator | Lead Search Scraper [NO COOKIE/URL] avatar](https://images.apifyusercontent.com/9HwTrz7sxmJb_dzzcVCcFRjHxrhenbyF3Ev_HQxn0mE/rs:fill:76:76/cb:1/aHR0cHM6Ly9hcGlmeS1pbWFnZS11cGxvYWRzLXByb2QuczMudXMtZWFzdC0xLmFtYXpvbmF3cy5jb20vNVRrZ2MySEM2a3ZnZWhmNmMtYWN0b3ItVUx6NjdlVkFPZmJCT2VLOWktamlORk5nRjBodC1zYWxlcy1uYXYtbGVhZC1zZWFyY2gtc2NyYXBlci5wbmc.webp){kind=avatar}
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The Short mode provides basic information, while the Full mode includes full detailed profile data." }, "salesNavUrl": { "title": "Sales Navigator Lead Search URL", "type": "string", "description": "Provide a Sales Navigator Lead Search URL to scrape profiles from. If provided, other search parameters will be ignored." }, "cookie": { "title": "LinkedIn Cookie", "type": "string", "description": "Your LinkedIn cookie value. You can get it from your browser's developer tools under the 'Application' tab." }, "userAgent": { "title": "User Agent", "type": "string", "description": "User agent to use for scraping. If not provided, the actor will use its default user agent (might get you logged out). Copy your User Agent from https://www.whatismybrowser.com/detect/what-is-my-user-agent/ and paste it here." }, "proxy": { "title": "Proxy", "type": "string", "description": "Proxy URL to use for scraping. If not provided, the actor will use its default proxy. For safe scraping, we recommend you to find and provide a clean residential proxy." }, "startPage": { "title": "Start Page", "minimum": 1, "maximum": 100, "type": "integer", "description": "The page number to start scraping from. ", "default": 1 }, "takePages": { "title": "Scrape Pages", "minimum": 0, "maximum": 100, "type": "integer", "description": "The number of search pages to scrape. Each page contains 25 profiles." }, "searchQuery": { "title": "Search query (fuzzy search)", "type": "string", "description": "Query to search LinkedIn profiles by name." }, "locations": { "title": "Locations Filter", "maxItems": 30, "type": "array", "description": "Filter Profiles by these LinkedIn locations. Example: `San Francisco`. LinkedIn does not always understand your text queries. For example for \"UK\" query it will apply \"Ukraine\" location, so you should use \"United Kingdom\" in this case. Try this out first in the location filter input of LinkedIn search at `https://www.linkedin.com/search/results/people/?geoUrn=%5B%22103644278%22%5D` - we will use the first suggestion from the autocomplete popup when you type your location", "items": { "type": "string" } }, "currentCompanies": { "title": "Current Company Filter", "maxItems": 30, "type": "array", "description": "Filter Profiles by these LinkedIn companies. Provide full LinkedIn URLs", "items": { "type": "string" } }, "pastCompanies": { "title": "Past Company Filter", "maxItems": 30, "type": "array", "description": "Filter Profiles by these LinkedIn past companies. Provide full LinkedIn URLs", "items": { "type": "string" } }, "schools": { "title": "School Filter", "maxItems": 30, "type": "array", "description": "Filter Profiles by these LinkedIn schools. Example: `Stanford University`.", "items": { "type": "string" } }, "currentJobTitles": { "title": "Current Job Title Filter (exact search)", "maxItems": 40, "type": "array", "description": "Filter Profiles by these LinkedIn current job titles. Example: `Software Engineer`.", "items": { "type": "string" } }, "pastJobTitles": { "title": "Past Job Title Filter", "maxItems": 40, "type": "array", "description": "Filter Profiles by these LinkedIn past job titles. Example: `Software Engineer`.", "items": { "type": "string" } }, "industryIds": { "title": "Industry IDs Filter", "maxItems": 40, "type": "array", "description": "Filter Profiles by these LinkedIn industry IDs. Example: `4` for 'Software Development'. Full list: `https://github.com/HarvestAPI/linkedin-industry-codes-v2/blob/main/linkedin_industry_code_v2_all_eng_with_header.csv`", "items": { "type": "string" } }, "firstNames": { "title": "First Names Filter", "maxItems": 50, "type": "array", "description": "Filter Profiles by these LinkedIn first names. Example: `John`.", "items": { "type": "string" } }, "lastNames": { "title": "Last Names Filter", "maxItems": 50, "type": "array", "description": "Filter Profiles by these LinkedIn last names. Example: `Doe`.", "items": { "type": "string" } } } }, "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 Sales Navigator Lead Search Scraper 🍪 + email from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
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