# LinkedIn Profile URL Finder (`automation-lab/linkedin-profile-url-finder`) Actor Find likely public LinkedIn profile URLs from names, companies, domains, titles, and locations with confidence scores for enrichment workflows. - **URL**: https://apify.com/automation-lab/linkedin-profile-url-finder.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Lead generation - **Stats:** 5 total users, 4 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## LinkedIn Profile URL Finder Find likely public LinkedIn profile URLs from person names, company/domain hints, job titles, and locations. ### What does LinkedIn Profile URL Finder do? LinkedIn Profile URL Finder helps recruiting, sales, operations, and enrichment teams turn a list of people into likely public LinkedIn profile URLs. It searches public search results for `linkedin.com/in` pages, extracts visible result titles and snippets, normalizes profile URLs, and scores each candidate. The actor does not require LinkedIn cookies, LinkedIn login, or private profile access. It is designed for bulk workflows where you already have a lead list and need a reliable best-effort URL column. ### Who is it for? - Recruiters enriching candidate spreadsheets. - Sales teams preparing account-based prospect lists. - RevOps teams cleaning CRM contacts. - Data enrichment teams building Clay, Zapier, Make, or Airtable workflows. - Agencies preparing lead lists for outreach. - Founders validating people data before manual research. ### Why use this actor? Manual LinkedIn URL lookup is slow. Search result pages often contain enough public information to identify the right profile. This actor makes that process repeatable, structured, and easy to export. You get one output row per input person, plus confidence evidence you can filter before sending data downstream. ### How it works 1. You provide people with `fullName` and optional company, domain, title, location, and keywords. 2. The actor builds a public search query using `site:linkedin.com/in`. 3. It fetches public search result HTML through Apify's Google SERP proxy. 4. It extracts LinkedIn `/in/` profile candidates. 5. It scores candidates by name, company, domain keyword, title, and location matches. 6. It saves the best URL and optional candidate list to the dataset. ### Data you can extract | Field | Description | | --- | --- | | `fullName` | Input person name | | `company` | Input company hint | | `domain` | Input company domain hint | | `title` | Input job title hint | | `location` | Input location hint | | `searchQuery` | Query used for public search | | `bestProfileUrl` | Best normalized LinkedIn profile URL | | `bestProfileTitle` | Search result title for the best match | | `bestProfileSnippet` | Search result snippet for the best match | | `confidence` | `high`, `medium`, `low`, or `none` | | `score` | Numeric match score from 0 to 100 | | `candidateCount` | Number of LinkedIn candidates found | | `candidates` | Optional scored candidate list | | `reasons` | Why the best match was selected | | `status` | `found`, `not_found`, or `error` | | `searchedAt` | ISO timestamp | ### How much does it cost to find LinkedIn profile URLs? This actor uses pay-per-event pricing. You pay a small start fee for each run and a per-person fee for each processed row. Start with a small test batch, review confidence scores, and then scale to larger lists. Because the actor uses a dedicated SERP proxy, the pricing is designed to include search infrastructure costs. ### Input example ```json { "people": [ { "fullName": "Satya Nadella", "company": "Microsoft", "domain": "microsoft.com", "title": "CEO", "location": "Redmond" }, { "fullName": "Sundar Pichai", "company": "Google", "domain": "google.com", "title": "CEO", "location": "Mountain View" } ], "maxCandidates": 5, "includeCandidates": true, "requestDelayMs": 500 } ```` ### Output example ```json { "fullName": "Satya Nadella", "company": "Microsoft", "domain": "microsoft.com", "title": "CEO", "location": "Redmond", "bestProfileUrl": "https://www.linkedin.com/in/satyanadella", "confidence": "high", "score": 100, "candidateCount": 5, "status": "found", "reasons": [ "all name tokens matched in result title", "company matched", "domain keyword matched", "title matched", "location matched", "public LinkedIn profile URL" ] } ``` ### Input fields #### `people` Required array of people to enrich. Each row needs `fullName`. Optional hints improve matching and reduce false positives. #### `maxCandidates` Number of candidates to keep per person. Use 3 to 5 for most workflows. #### `includeCandidates` Set to `true` when you want evidence and alternatives. Set to `false` when you only need the best URL column. #### `requestDelayMs` Delay between searches. Use the default for normal batches. Increase it for very large runs. ### Tips for better matches - Include company names when possible. - Include company domains for stronger disambiguation. - Add titles for common names. - Add city or country when the company has many employees. - Filter downstream rows where `confidence` is `high` or `score` is above your threshold. - Review `candidates` for ambiguous names before automated outreach. ### Common workflows #### Recruiting sourcing Upload a list of candidate names and current companies. Export `bestProfileUrl` to your applicant tracking system or sourcing spreadsheet. #### Sales prospecting Resolve contacts from CRM exports before building outreach sequences. Use confidence scores to separate automatic matches from manual review. #### Data enrichment Add a LinkedIn URL column to Clay, Airtable, or Google Sheets workflows. Use the Apify API to run this actor whenever a new batch is ready. #### CRM cleanup Find missing LinkedIn URLs for existing contacts. Keep `searchQuery` and `reasons` for auditability. ### Integrations - Google Sheets: export the dataset as CSV. - Clay: run the actor through Apify integration or API calls. - Zapier: trigger a run and send dataset rows into a CRM. - Make: enrich rows in scheduled batches. - Airtable: import URL and confidence fields. - HubSpot or Salesforce: update contact records after manual approval. ### API usage with Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/linkedin-profile-url-finder').call({ people: [{ fullName: 'Satya Nadella', company: 'Microsoft' }], maxCandidates: 5, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` ### API usage with Python ```python from apify_client import ApifyClient client = ApifyClient('YOUR_APIFY_TOKEN') run = client.actor('automation-lab/linkedin-profile-url-finder').call(run_input={ 'people': [{'fullName': 'Satya Nadella', 'company': 'Microsoft'}], 'maxCandidates': 5, }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items) ``` ### API usage with cURL ```bash curl "https://api.apify.com/v2/acts/automation-lab~linkedin-profile-url-finder/runs?token=$APIFY_TOKEN" \ -H 'Content-Type: application/json' \ -d '{"people":[{"fullName":"Satya Nadella","company":"Microsoft"}],"maxCandidates":5}' ``` ### MCP usage Use this actor with Apify MCP in Claude Desktop or Claude Code. MCP URL: ```text https://mcp.apify.com/?tools=automation-lab/linkedin-profile-url-finder ``` Claude Code setup command: ```bash claude mcp add apify-linkedin-profile-url-finder https://mcp.apify.com/?tools=automation-lab/linkedin-profile-url-finder ``` Claude Desktop JSON config: ```json { "mcpServers": { "apify-linkedin-profile-url-finder": { "url": "https://mcp.apify.com/?tools=automation-lab/linkedin-profile-url-finder" } } } ``` Example prompts: - "Find LinkedIn profile URLs for these 50 people and return only high-confidence matches." - "Enrich this recruiting list with profile URLs and explain ambiguous candidates." - "Create a CSV-ready table with name, company, best LinkedIn URL, score, and reasons." ### Accuracy and confidence This actor is a best-effort resolver, not an official identity verification service. A high score means the public search result strongly matched the provided hints. It does not guarantee employment, current role, or profile ownership. For compliance-sensitive workflows, review results before use. ### Limitations - Search engines may return different results by time and region. - Very common names can produce ambiguous candidates. - Some LinkedIn profiles are hidden from public search results. - The actor does not open private LinkedIn profile pages. - The actor does not bypass LinkedIn login walls. ### FAQ #### Why did a person return `not_found`? The public search page did not expose a LinkedIn `/in/` result for the exact hints. Try adding a company, removing overly specific title/location hints, or adding a domain. #### Why is confidence low? The candidate may match only a partial name or company clue. Review `candidates` and `reasons` before accepting the URL. #### Why do I see multiple candidates? Common names and large companies often produce several plausible profiles. Use the highest score first, then inspect alternatives when the decision matters. ### Legality and ethical use This actor works with publicly visible search result snippets and URLs. Use the data only for lawful purposes. Respect privacy, platform terms, outreach rules, and applicable data protection laws. Avoid using profile URLs for spam, harassment, discrimination, or sensitive profiling. ### Related scrapers and tools - https://apify.com/automation-lab/google-search-scraper - https://apify.com/automation-lab/website-contact-finder - https://apify.com/automation-lab/domain-availability-checker - https://apify.com/automation-lab/google-autocomplete-scraper ### Best practices for bulk runs Start with a sample of 10 to 25 people. Check confidence distribution. Adjust hints. Then run larger batches. For thousands of records, split the input into smaller chunks so you can inspect and retry safely. ### Changelog #### 0.1 Initial version with public search, LinkedIn `/in/` URL extraction, candidate scoring, confidence labels, and candidate evidence. ### Support If results look wrong, save the input row, output row, and candidate list. Those fields make it easier to reproduce and tune matching behavior. # Actor input Schema ## `people` (type: `array`): Rows to resolve. Full name is required; company, domain, title, and location improve confidence scoring. ## `maxCandidates` (type: `integer`): How many LinkedIn /in/ candidates to keep for each person. ## `includeCandidates` (type: `boolean`): Return all scored candidates, not only the best URL. ## `requestDelayMs` (type: `integer`): Polite delay between public search requests. Increase for large batches. ## Actor input object example ```json { "people": [ { "fullName": "Satya Nadella", "company": "Microsoft", "domain": "microsoft.com", "title": "CEO", "location": "Redmond" }, { "fullName": "Sundar Pichai", "company": "Google", "domain": "google.com", "title": "CEO", "location": "Mountain View" } ], "maxCandidates": 5, "includeCandidates": true, "requestDelayMs": 500 } ``` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "people": [ { "fullName": "Satya Nadella", "company": "Microsoft", "domain": "microsoft.com", "title": "CEO", "location": "Redmond" }, { "fullName": "Sundar Pichai", "company": "Google", "domain": "google.com", "title": "CEO", "location": "Mountain View" } ], "maxCandidates": 5, "includeCandidates": true, "requestDelayMs": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/linkedin-profile-url-finder").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "people": [ { "fullName": "Satya Nadella", "company": "Microsoft", "domain": "microsoft.com", "title": "CEO", "location": "Redmond", }, { "fullName": "Sundar Pichai", "company": "Google", "domain": "google.com", "title": "CEO", "location": "Mountain View", }, ], "maxCandidates": 5, "includeCandidates": True, "requestDelayMs": 500, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/linkedin-profile-url-finder").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "people": [ { "fullName": "Satya Nadella", "company": "Microsoft", "domain": "microsoft.com", "title": "CEO", "location": "Redmond" }, { "fullName": "Sundar Pichai", "company": "Google", "domain": "google.com", "title": "CEO", "location": "Mountain View" } ], "maxCandidates": 5, "includeCandidates": true, "requestDelayMs": 500 }' | apify call automation-lab/linkedin-profile-url-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/linkedin-profile-url-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile URL Finder", "description": "Find likely public LinkedIn profile URLs from names, companies, domains, titles, and locations with confidence scores for enrichment workflows.", "version": "0.1", "x-build-id": "LR9qlCjGe5K54XoTW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~linkedin-profile-url-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-linkedin-profile-url-finder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~linkedin-profile-url-finder/runs": { "post": { "operationId": "runs-sync-automation-lab-linkedin-profile-url-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~linkedin-profile-url-finder/run-sync": { "post": { "operationId": "run-sync-automation-lab-linkedin-profile-url-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "people" ], "properties": { "people": { "title": "People", "type": "array", "description": "Rows to resolve. Full name is required; company, domain, title, and location improve confidence scoring.", "items": { "type": "object", "required": [ "fullName" ], "properties": { "fullName": { "title": "Full name", "type": "string", "description": "Person name to search for." }, "company": { "title": "Company", "type": "string", "description": "Current or target company name." }, "domain": { "title": "Company domain", "type": "string", "description": "Company website domain used as an extra matching hint." }, "title": { "title": "Job title", "type": "string", "description": "Role/title hint, for example CEO, VP Sales, Recruiter." }, "location": { "title": "Location", "type": "string", "description": "City, region, or country hint." }, "keywords": { "title": "Extra keywords", "type": "array", "description": "Optional additional terms to include in the public search query.", "items": { "type": "string" } } } } }, "maxCandidates": { "title": "Maximum candidates per person", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many LinkedIn /in/ candidates to keep for each person.", "default": 5 }, "includeCandidates": { "title": "Include candidate list", "type": "boolean", "description": "Return all scored candidates, not only the best URL.", "default": true }, "requestDelayMs": { "title": "Delay between searches (ms)", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Polite delay between public search requests. Increase for large batches.", "default": 500 } } }, "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 } } } } } } } } } } ```