# Instagram Lead Finder — Emails, Phones & Influencer Data (`inexhaustible_glass/instagram-lead-finder`) Actor Bulk Instagram profile scraper for lead generation and influencer research. Extracts emails and phone numbers from bios, classifies influencer tier (nano/micro/macro/mega), calculates lead quality scores (A/B/C/D). Powered by ipmask module + Apify Residential Proxy for zero-block scraping at scale. - **URL**: https://apify.com/inexhaustible\_glass/instagram-lead-finder.md - **Developed by:** [Hitman studio](https://apify.com/inexhaustible_glass) (community) - **Categories:** Social media - **Stats:** 0 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 undefined # Actor input Schema ## `usernames` (type: `array`): List of Instagram usernames to scrape (without the @ symbol). One username per line. Example: nike, garyvee, mkbhd ## `concurrency` (type: `integer`): How many profiles to scrape in parallel. Higher = faster, but uses more proxy credits. Recommended: 5 for balance of speed and reliability. ## `useResidentialProxy` (type: `boolean`): Use Apify Residential Proxy for best success rate (consumes proxy credits). Disable only for testing. ## `extractEmailsOnly` (type: `boolean`): If enabled, output only profiles where at least one email was found. Useful for lead generation. ## `minFollowers` (type: `integer`): Skip profiles with fewer followers than this. Set to 0 for no filtering. ## `maxFollowers` (type: `integer`): Skip profiles with more followers than this. Leave empty for no upper limit. ## Actor input object example ```json { "usernames": [ "nike", "garyvee", "mkbhd" ], "concurrency": 5, "useResidentialProxy": true, "extractEmailsOnly": false, "minFollowers": 0 } ```` # 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 = { "usernames": [ "nike", "garyvee", "mkbhd" ] }; // Run the Actor and wait for it to finish const run = await client.actor("inexhaustible_glass/instagram-lead-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 = { "usernames": [ "nike", "garyvee", "mkbhd", ] } # Run the Actor and wait for it to finish run = client.actor("inexhaustible_glass/instagram-lead-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 '{ "usernames": [ "nike", "garyvee", "mkbhd" ] }' | apify call inexhaustible_glass/instagram-lead-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=inexhaustible_glass/instagram-lead-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Lead Finder — Emails, Phones & Influencer Data", "description": "Bulk Instagram profile scraper for lead generation and influencer research. Extracts emails and phone numbers from bios, classifies influencer tier (nano/micro/macro/mega), calculates lead quality scores (A/B/C/D). Powered by ipmask module + Apify Residential Proxy for zero-block scraping at scale.", "version": "1.0", "x-build-id": "YH5sIs1gDKXtGHBfp" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/inexhaustible_glass~instagram-lead-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-inexhaustible_glass-instagram-lead-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/inexhaustible_glass~instagram-lead-finder/runs": { "post": { "operationId": "runs-sync-inexhaustible_glass-instagram-lead-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/inexhaustible_glass~instagram-lead-finder/run-sync": { "post": { "operationId": "run-sync-inexhaustible_glass-instagram-lead-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "usernames" ], "properties": { "usernames": { "title": "🎯 Instagram Usernames", "minItems": 1, "maxItems": 10000, "uniqueItems": true, "type": "array", "description": "List of Instagram usernames to scrape (without the @ symbol). One username per line. Example: nike, garyvee, mkbhd", "items": { "type": "string" } }, "concurrency": { "title": "⚡ Concurrency (Parallel Requests)", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many profiles to scrape in parallel. Higher = faster, but uses more proxy credits. Recommended: 5 for balance of speed and reliability.", "default": 5 }, "useResidentialProxy": { "title": "🏠 Use Residential Proxy (Recommended)", "type": "boolean", "description": "Use Apify Residential Proxy for best success rate (consumes proxy credits). Disable only for testing.", "default": true }, "extractEmailsOnly": { "title": "📧 Only Output Profiles With Emails", "type": "boolean", "description": "If enabled, output only profiles where at least one email was found. Useful for lead generation.", "default": false }, "minFollowers": { "title": "👥 Minimum Followers", "minimum": 0, "type": "integer", "description": "Skip profiles with fewer followers than this. Set to 0 for no filtering.", "default": 0 }, "maxFollowers": { "title": "👥 Maximum Followers", "minimum": 0, "type": "integer", "description": "Skip profiles with more followers than this. Leave empty for no upper limit." } } }, "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 } } } } } } } } } } ```