# LinkedIn Profile Finder - Email to LinkedIn URL Lookup (`anshumanatrey/linkedin-harvester`) Actor Turn an email address into the person's public LinkedIn profile, with a confidence score on every match. Paste one email or a list and get back clean LinkedIn URLs for your CRM, sales, or recruiting pipeline. No LinkedIn login or cookies. Pay only for confident matches. - **URL**: https://apify.com/anshumanatrey/linkedin-harvester.md - **Developed by:** [Anshuman Atrey](https://apify.com/anshumanatrey) (community) - **Categories:** Lead generation, Developer tools, Social media - **Stats:** 2 total users, 1 monthly users, 16.7% 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. 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 Finder — Email to LinkedIn URL Turn an email address into the person's public LinkedIn profile — with a confidence score on every match. Paste one email or a whole list and get back a clean table of LinkedIn URLs, ready for your CRM, outreach, or recruiting pipeline. **No LinkedIn login, no cookies, nothing tied to your account.** --- ### What you get - **Email in → best-match LinkedIn URL out**, with a 0–100% confidence score on every row so you know which matches to trust. - **Bulk or one-off** — look up a single address or thousands, one result row each. - **Pay only for matches that land.** Emails that can't be matched cost almost nothing. - **Near-zero cost per match** — the lookup runs on your own free-tier keys, so there's no data-broker markup. - **Account-safe** — it never logs into LinkedIn or touches your session, so there's nothing to get flagged. ### Who it's for - **Sales & growth** — enrich inbound leads and sign-ups with the right LinkedIn profile before you reach out. - **Recruiting** — turn a list of candidate emails into LinkedIn profiles. - **RevOps / CRM** — clean, verify, and backfill contact records at scale. - **Founders & operators** — instantly see who actually emailed you. ### How to use it 1. Paste a single email, or a list of emails. 2. Pick a matching mode — **Balanced** is the default and works for most lists. 3. *(Optional)* add your free Brave Search and Groq keys for the widest coverage. 4. Hit **Start**. Results appear in the dataset, one row per email. #### Matching modes | Mode | Best for | |---|---| | **No AI** | Fastest and free — accepts only the clear, unambiguous matches. | | **Balanced** *(recommended)* | The default. Spends a little extra effort only on the uncertain matches. | | **Full AI** | Highest match rate — works hardest on every email. | #### What a result looks like | Email | Found | LinkedIn URL | Confidence | Name | |---|---|---|---|---| | satya.nadella@microsoft.com | ✅ | linkedin.com/in/satyanadella | 97% | Satya Nadella | | coolguy@gmail.com | — | — | — | — | Or as JSON: ```json { "email": "satya.nadella@microsoft.com", "linkedin_url": "https://www.linkedin.com/in/satyanadella", "confidence": 0.97, "found": true, "name": "Satya Nadella" } ```` ### Pricing **Pay-per-result.** A tiny fee per email processed, plus a small charge only when a confident match is found — a confident lookup costs well under a cent. Search and AI run on your own free-tier keys, so you're paying for the lookup itself, not data-broker markup. ### How good are the matches? - **Work emails** (`name@company.com`) match reliably — that's the sweet spot. - **People with a public professional presence** match even from a personal address. - **No public profile to find?** You get a clean *not found* — never a confident guess. Every accepted match has to clear the confidence bar you set, so junk stays out of your list. ### Good to know - Works on **public information only** — no private database, no broker-sourced contact data. - Best on professional/work emails; some personal addresses simply have no public profile to match. - **You stay in control** — set the confidence bar, choose how much AI to use, bring your own keys. ### Run it yourself (open source) The actor is open source under the MIT license. To run it from the command line with your own keys: ```bash git clone https://github.com/AnshumanAtrey/linkedin-harvester.git cd linkedin-harvester pip3 install -r requirements.txt GROQ_API_KEY=your_groq_key BRAVE_API_KEY=your_brave_key \ python3 -m harvester.find "satya.nadella@microsoft.com" ``` Free keys: Groq → console.groq.com/keys · Brave → brave.com/search/api ### Use responsibly Built for legitimate sales, recruiting, research, and verification. Follow the privacy laws that apply to you (GDPR, CCPA, and local equivalents) and honor opt-out and do-not-contact requests. ### License MIT — see [LICENSE](LICENSE). # Actor input Schema ## `email` (type: `string`): A single email address to look up. For a quick one-off. To process many at once, use the Emails list below instead. ## `emails` (type: `array`): A list of email addresses. Each one becomes one result row with its best-match LinkedIn profile and a confidence score. Role mailboxes like info@ or support@ are skipped automatically. ## `aiMode` (type: `string`): How much the AI is used. No AI: fastest and free, only the clear matches resolve, anything uncertain is left unscored. Balanced (recommended): the AI steps in only on the uncertain matches. Full AI: the AI works on every email, highest match rate but highest cost and slowest. ## `minConfidencePct` (type: `integer`): Only profiles the tool is at least this confident about are accepted (and scraped, if scraping is on). Lower means more coverage but more wrong matches. 80 is a safe default. ## `deepEnrich` (type: `boolean`): When the email itself has no usable name (e.g. addresses like coolguy@gmail.com), also check the person's public professional footprint to recover one. Catches more people but adds a few seconds per email. Off by default: most work emails (first.last@company.com) resolve from the address and company alone. ## `scrapeProfiles` (type: `boolean`): After a confident match, also fetch the full LinkedIn profile details. Needs a Bright Data key below. Off by default, so the actor just returns the profile URL and confidence (no scraping, no extra cost). ## `runMode` (type: `string`): Incremental uses only free resolvers (default, runs forever at near-zero cost). Backfill also allows the paid hosted email-to-LinkedIn vendor for a one-time history sweep (needs a Findymail key). ## `groqApiKey` (type: `string`): Your Groq key (starts with gsk\_), free tier is generous. Powers the AI steps. Only needed if AI assistance is Balanced or Full. Get one at console.groq.com/keys. ## `braveApiKey` (type: `string`): Your Brave Search key. Powers the web search that finds candidate LinkedIn profiles. Without a search key the actor still resolves what it can from the email's public footprint alone. ## `googleCseKey` (type: `string`): Alternative to Brave for the search step. Only works with a legacy Custom Search key (Google closed new sign-ups). Provide together with the CX id below. ## `googleCseCx` (type: `string`): The search engine id that pairs with the Google Custom Search key above. ## `brightdataApiKey` (type: `string`): Enables real LinkedIn profile scraping when 'Scrape the matched profile' is on. Without it, the actor resolves the URL but does not scrape. ## `findymailApiKey` (type: `string`): Optional paid email-to-LinkedIn fallback, used only in Backfill run mode for emails the free resolvers miss. ## Actor input object example ```json { "email": "anshumanatrey@gmail.com", "emails": [ "anshumanatrey@gmail.com", "jane.doe@stripe.com" ], "aiMode": "balanced", "minConfidencePct": 80, "deepEnrich": false, "scrapeProfiles": false, "runMode": "incremental" } ``` # Actor output Schema ## `items` (type: `string`): Email to LinkedIn results, one row per email. ## `log` (type: `string`): Full execution log. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("anshumanatrey/linkedin-harvester").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 = {} # Run the Actor and wait for it to finish run = client.actor("anshumanatrey/linkedin-harvester").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 '{}' | apify call anshumanatrey/linkedin-harvester --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=anshumanatrey/linkedin-harvester", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile Finder - Email to LinkedIn URL Lookup", "description": "Turn an email address into the person's public LinkedIn profile, with a confidence score on every match. Paste one email or a list and get back clean LinkedIn URLs for your CRM, sales, or recruiting pipeline. No LinkedIn login or cookies. Pay only for confident matches.", "version": "0.1", "x-build-id": "ybTWEgrgQYbZVg5uf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/anshumanatrey~linkedin-harvester/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-anshumanatrey-linkedin-harvester", "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/anshumanatrey~linkedin-harvester/runs": { "post": { "operationId": "runs-sync-anshumanatrey-linkedin-harvester", "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/anshumanatrey~linkedin-harvester/run-sync": { "post": { "operationId": "run-sync-anshumanatrey-linkedin-harvester", "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", "properties": { "email": { "title": "Email address", "type": "string", "description": "A single email address to look up. For a quick one-off. To process many at once, use the Emails list below instead." }, "emails": { "title": "Emails (bulk)", "type": "array", "description": "A list of email addresses. Each one becomes one result row with its best-match LinkedIn profile and a confidence score. Role mailboxes like info@ or support@ are skipped automatically.", "items": { "type": "string" } }, "aiMode": { "title": "AI assistance", "enum": [ "regex", "balanced", "full" ], "type": "string", "description": "How much the AI is used. No AI: fastest and free, only the clear matches resolve, anything uncertain is left unscored. Balanced (recommended): the AI steps in only on the uncertain matches. Full AI: the AI works on every email, highest match rate but highest cost and slowest.", "default": "balanced" }, "minConfidencePct": { "title": "Minimum confidence to scrape (%)", "minimum": 40, "maximum": 100, "type": "integer", "description": "Only profiles the tool is at least this confident about are accepted (and scraped, if scraping is on). Lower means more coverage but more wrong matches. 80 is a safe default.", "default": 80 }, "deepEnrich": { "title": "Find the name from public profiles (slower)", "type": "boolean", "description": "When the email itself has no usable name (e.g. addresses like coolguy@gmail.com), also check the person's public professional footprint to recover one. Catches more people but adds a few seconds per email. Off by default: most work emails (first.last@company.com) resolve from the address and company alone.", "default": false }, "scrapeProfiles": { "title": "Scrape the matched profile", "type": "boolean", "description": "After a confident match, also fetch the full LinkedIn profile details. Needs a Bright Data key below. Off by default, so the actor just returns the profile URL and confidence (no scraping, no extra cost).", "default": false }, "runMode": { "title": "Run mode", "enum": [ "incremental", "backfill" ], "type": "string", "description": "Incremental uses only free resolvers (default, runs forever at near-zero cost). Backfill also allows the paid hosted email-to-LinkedIn vendor for a one-time history sweep (needs a Findymail key).", "default": "incremental" }, "groqApiKey": { "title": "Groq API key", "type": "string", "description": "Your Groq key (starts with gsk_), free tier is generous. Powers the AI steps. Only needed if AI assistance is Balanced or Full. Get one at console.groq.com/keys." }, "braveApiKey": { "title": "Brave Search API key", "type": "string", "description": "Your Brave Search key. Powers the web search that finds candidate LinkedIn profiles. Without a search key the actor still resolves what it can from the email's public footprint alone." }, "googleCseKey": { "title": "Google Custom Search key", "type": "string", "description": "Alternative to Brave for the search step. Only works with a legacy Custom Search key (Google closed new sign-ups). Provide together with the CX id below." }, "googleCseCx": { "title": "Google Custom Search CX id", "type": "string", "description": "The search engine id that pairs with the Google Custom Search key above." }, "brightdataApiKey": { "title": "Bright Data API key", "type": "string", "description": "Enables real LinkedIn profile scraping when 'Scrape the matched profile' is on. Without it, the actor resolves the URL but does not scrape." }, "findymailApiKey": { "title": "Findymail API key", "type": "string", "description": "Optional paid email-to-LinkedIn fallback, used only in Backfill run mode for emails the free resolvers miss." } } }, "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 } } } } } } } } } } ```