LinkedIn Profile Search Scraper No Cookies ✅ Find all people 📧
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Pay per event
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LinkedIn Profile Search Scraper No Cookies ✅ Find all people 📧
Search for LinkedIn profiles with filters and extract detailed profile information, including work experience, education history, location and more. No cookies or account required.
5.0 (2)
Pricing
Pay per event
59
Total users
771
Monthly users
488
Runs succeeded
>99%
Issues response
8.7 hours
Last modified
2 days ago
You can access the LinkedIn Profile Search Scraper No Cookies ✅ Find all people 📧 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.
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The Short mode provides basic information, while the Full mode includes full detailed profile data." }, "searchQuery": { "title": "Search query (fuzzy search)", "type": "string", "description": "Query to search LinkedIn profiles by name." }, "maxItems": { "title": "Maximum number of profiles to scrape for all queries", "type": "integer", "description": "Maximum number of profiles to scrape for all queries. The actor will stop scraping when this limit is reached." }, "locations": { "title": "Locations Filter", "maxItems": 10, "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": 10, "type": "array", "description": "Filter Profiles by these LinkedIn companies. Provide full LinkedIn URLs", "items": { "type": "string" } }, "pastCompanies": { "title": "Past Company Filter", "maxItems": 10, "type": "array", "description": "Filter Profiles by these LinkedIn past companies. Provide full LinkedIn URLs", "items": { "type": "string" } }, "schools": { "title": "School Filter", "maxItems": 10, "type": "array", "description": "Filter Profiles by these LinkedIn schools. Example: `Stanford University`.", "items": { "type": "string" } }, "currentJobTitles": { "title": "Current Job Title Filter (exact search)", "maxItems": 20, "type": "array", "description": "Filter Profiles by these LinkedIn current job titles. Example: `Software Engineer`.", "items": { "type": "string" } }, "pastJobTitles": { "title": "Past Job Title Filter", "maxItems": 20, "type": "array", "description": "Filter Profiles by these LinkedIn past job titles. Example: `Software Engineer`.", "items": { "type": "string" } }, "industryIds": { "title": "Industry IDs Filter", "maxItems": 20, "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": 10, "type": "array", "description": "Filter Profiles by these LinkedIn first names. Example: `John`.", "items": { "type": "string" } }, "lastNames": { "title": "Last Names Filter", "maxItems": 10, "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 } } } } } } } } }}LinkedIn Profile Search Scraper No Cookies ✅ Find all people 📧 OpenAPI definition
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 Search Scraper No Cookies ✅ Find all people 📧 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:
LinkedIn Profile Search Scraper No Cookies ✅ Find all people 📧 API in Python
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