# Threads Email Scraper (`solid-scraper/threads-email-scraper`) Actor 📩 Threads Email Scraper extracts verified contact emails from Threads profiles using advanced filtering by keyword, location & niche. Save time, find leads faster, and boost outreach for sales, agencies & growth teams. 🚀 - **URL**: https://apify.com/solid-scraper/threads-email-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 ### Threads Email Scraper 📬 **Threads Email Scraper** helps you automatically collect email addresses associated with Threads profiles by using your own **keywords** and **custom email-domain filters**. If you’re searching for a *threads email scraper*, a *Threads scraper email* workflow, or a way to *scrape emails from Threads*, this actor is built for that exact goal—helping marketers, researchers, and data analysts scale outreach list building while saving hours of manual work. Whether you’re building a lead list, validating an outreach universe, or enriching datasets for analysis, Threads Email Scraper extracts emails from publicly available data on Threads profiles where emails appear in bios and posts related to your keywords. --- ### 🚀 Why choose Threads Email Scraper? | Feature | Benefit | |---|---| | ✅ **Keyword-based email discovery** | Use your own keywords to target relevant Threads bios and posts tied to your outreach themes. | | ✅ **Custom email-domain targeting** | Filter results to domains you care about (for example, `@gmail.com`) to improve lead quality. | | ✅ **Two scraping engines** | Choose between **Cost Effective (New)** and **Legacy** for speed/cost vs reliability tradeoffs. | | ✅ **Resilient execution with retries and fallbacks** | Includes retries and fallback logic to keep runs productive when results are limited. | | ✅ **Structured dataset output** | Produces consistent records (network, keyword, title, description, url, email, proxyGroups) you can load into your tools. | | ✅ **Stops at your email limit** | Set `maxEmails` to cap spending and runtime—useful for scalable email list building. | --- ### 🔎 Key features - 🧠 **Accurate Threads contact email harvesting**: Pulls emails that match your provided keywords and domain filters from publicly available Threads content. - 🧰 **Flexible targeting inputs**: Configure `keywords`, `location`, and `customDomains` to match your niche and audience. - 🛡️ **Reliable runs with resilience**: Includes retries and fallback behavior to keep scraping effective when results are constrained. - ⚙️ **Engine choice for performance**: Switch between **Cost Effective (New)** and **Legacy** depending on your priority. - 📊 **Structured, analysis-ready output**: Each discovered email is pushed as a dataset row with clear context fields (keyword, title, description, url). - 💾 **Progress saving during long runs**: Saves progress so runs can continue more smoothly during interruptions. - 🔄 **Stops when enough emails are collected**: Enforces `maxEmails` so you control output volume and runtime. - 🌐 **Built-in proxy support**: Works with your `proxyConfiguration` for controlled, repeatable scraping. --- ### 🧾 Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["founder", "marketing"], "location": "", "platform": "Threads", "customDomains": ["@gmail.com"], "maxEmails": 20, "engine": "cost-effective", "proxyConfiguration": {} } ```` #### Input Fields | Field | Required | Description | |---|---:|---| | `keywords` | ✅ | A list of keywords to search for. The scraper uses these keywords to find email-bearing bios/posts related to your themes. | | `location` | ❌ | Location to filter search results. Leave empty for no location filtering. | | `platform` | ❌ | Platform selection. The only supported value in this actor is `Threads` (default is `Threads`). | | `customDomains` | ❌ | A list of custom email domains used to filter extracted emails (for example, `@gmail.com`). | | `maxEmails` | ❌ | Maximum number of emails to collect. The scraper stops once this limit is reached. Higher values may take longer and don’t guarantee hitting the exact number. | | `engine` | ❌ | Choose scraping engine: `cost-effective` (Cost Effective (New)) or `legacy` (Legacy). | | `proxyConfiguration` | ❌ | Proxy configuration for this Actor (uses Apify proxy tooling when you provide it). | *** ### 📦 Output The actor saves each discovered email as a JSON dataset record. Each output row includes the email plus the context from the source where it was found. Example output record: ```json { "network": "threads.com", "keyword": "founder", "title": "No title", "description": "No data", "url": "No URL", "email": "example@gmail.com", "proxyGroups": [ "some-proxy-group" ] } ``` #### Output Fields | Field | Type | Description | |---|---|---| | `network` | string | The network/source label for the scraped data. | | `keyword` | string | The keyword currently being used for discovery. | | `title` | string | The title associated with the discovered content. | | `description` | string | The text description where the email was found. | | `url` | string | The source URL tied to the discovered content. | | `email` | string | The extracted email address. | | `proxyGroups` | array | Proxy group information associated with the run. | Closing note: after the run, you can export the dataset to common formats like JSON or CSV from Apify’s UI. *** ### ▶️ How to use Threads Email Scraper (via Apify Console) 1. **Open Apify Console**\ Log in at [console.apify.com](https://console.apify.com) and open the **Actors** tab. 2. **Find the actor**\ Search for **Threads Email Scraper** and open the actor page. 3. **Add your INPUT values**\ In the **INPUT** section, set: `keywords` (required), and optionally `customDomains`, `location`, and `maxEmails`. 4. **Choose an engine (optional)**\ If you need faster, cost-effective runs choose `engine: "cost-effective"`. If you prefer the other mode, choose `engine: "legacy"`. 5. **Configure proxies (optional)**\ Use `proxyConfiguration` if you want controlled proxy behavior for this run. 6. **Run the actor**\ Click **Run**. During execution, you’ll see logs showing progress and discovered emails being pushed to the dataset. 7. **Review results in the OUTPUT tab**\ Open the dataset produced by the run and preview rows to verify email/domain relevance. 8. **Export your dataset**\ Export to JSON/CSV for your CRM, spreadsheet workflow, or analysis pipeline. No coding required—get accurate Threads email scraping results in minutes. 🚀 *** ### 🧠 Advanced features & SEO optimization - 🔧 **Engine-tuned runs for Threads email harvesting tool use cases**: Threads Email Scraper is designed to support both a **cost-effective** option and a **legacy** option depending on your needs—great for automated Threads email scraping at scale. - 🎯 **Domain-filtered contact discovery**: Uses your `customDomains` to reduce noise when extracting emails from Threads profiles. - 📌 **Progress tracking for long searches**: Built to save progress during execution so large workloads are more manageable. - 🧾 **Structured output for outreach workflows**: Each row includes keyword and source context, making Threads leads email extraction easier to validate and clean. - ⏱️ **Runtime awareness**: Large searches or high `maxEmails` can take longer, and you can adjust run options (timeout) for bigger jobs. *** ### 💡 Best use cases - 📈 **B2B lead generation for outreach**: Build targeted email lists by running Threads email scraper searches around founder/marketing themes and filtering to the right domains. - 🔍 **Audience email extraction for research**: Compare how different keywords perform and how often relevant emails appear in Threads bios/posts. - 🧪 **Marketing validation for campaigns**: Quickly test whether your target audience on Threads Email Scraper actually provides email contact signals. - 🎓 **Recruiting & hiring market mapping**: Use Threads profile email finder workflows to identify potential industry contacts with public email mentions. - 🧰 **Data analyst dataset enrichment**: Combine email findings with keyword context fields (keyword, title, description, url) for cleaner downstream analysis. - 🛠️ **CRM integration pipeline inputs**: Export the results and ingest them into your CRM or outreach system to automate contact list building. - 🤝 **Partnership prospecting**: Find domain-filtered contact emails tied to specific niches using an automated Threads outreach email scraper approach. *** ### 🧰 Technical specifications - **Supported Input Formats** - ✅ `keywords` as an array of strings (required) - ✅ `customDomains` as an array of domains (optional) - ✅ `location` as a string (optional) - ✅ `platform` supports `Threads` (optional, default `Threads`) - **Proxy Support** - ✅ Uses `proxyConfiguration` to configure proxies for the run. - **Retry Mechanism** - ✅ Includes retries and fallbacks for resilience when results are constrained. - **Dataset Structure** - ✅ JSON dataset rows with: `network`, `keyword`, `title`, `description`, `url`, `email`, `proxyGroups` - **Rate Limits & Performance** - ✅ Performance depends on run size and results availability; `maxEmails` caps output volume to manage runtime. - **Limitations** - ❌ Only captures emails that appear in publicly available Threads content (where emails are present in bios and posts related to your keywords). - ❌ Higher `maxEmails` does not guarantee collecting that exact number—results may be limited by availability. *** ### ❓ FAQ #### What does Threads Email Scraper extract? ✅ Threads Email Scraper extracts email addresses that match your `customDomains` filter from publicly available Threads content, using your `keywords` to target relevant bios and posts. #### Do I need a location filter to get results? ❌ No. `location` is optional—leave it empty to run without location filtering. #### Can I limit how many emails are returned? ✅ Yes. Set `maxEmails` to define the maximum number of emails to collect. The actor stops once this limit is reached. #### Which engine should I use: cost-effective or legacy? ✅ `cost-effective` is described as **Cost Effective (New)** and `legacy` as **Legacy**. Choose based on your preference for speed/cost vs the other mode’s reliability-oriented approach. #### Does this actor save progress during long runs? ✅ Yes. It saves progress (including a cursor and seen emails) as the run proceeds, helping with long searches. #### How can I use Threads Email Scraper in a workflow? 💻 Export the dataset (JSON/CSV) and plug it into your outreach tooling, CRM, spreadsheets, or analytics pipeline. Each row includes context fields like `keyword`, `title`, `description`, and `url` to help you validate results. #### Is this tool compliant for lead generation? ✅ The actor collects information from **publicly accessible sources**. You’re responsible for following applicable laws and platform policies when using the exported emails for outreach. #### Can I request data removal? ✅ Yes. Contact for data-removal requests. *** ### 🤝 Support & feature requests If you’re using **Threads Email Scraper** and have ideas or run into issues, we’d love to hear from you. - 💡 **Feature Requests**: Want enhancements like better exports, additional fields, or more control over filtering? Share what you need (for example, improved CSV-friendly output or new domain filtering behaviors). - 📧 **Contact**: Email us at . Your feedback helps shape the roadmap for this Threads leads email extraction workflow. *** ### ✅ Threads Email Scraper — final thoughts *Threads Email Scraper is a practical, SEO-friendly threads email scraper built for scalable outreach list building from public Threads content.*\ *Run it with the right keywords and domain filters to turn Threads into an actionable email dataset.* 🚀 *** ### Disclaimer **This tool only accesses publicly accessible sources.** It does not access private profiles, authenticated data, or password-protected pages. You are responsible for complying with applicable laws (including GDPR/CCPA where relevant), spam regulations, and the platform’s terms of service. For data removal requests, contact . Use Threads Email Scraper responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `keywords` (type: `array`): A list of keywords to search for. ## `location` (type: `string`): Location to filter search results. ## `platform` (type: `string`): Select platform. ## `customDomains` (type: `array`): List of custom email domains ## `maxEmails` (type: `integer`): Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time. ## `engine` (type: `string`): Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE\_SERP proxy with traditional selectors - more reliable but slower and more expensive. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "founder", "marketing" ], "location": "", "platform": "Threads", "customDomains": [ "@gmail.com" ], "maxEmails": 20, "engine": "cost-effective" } ``` # 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 = { "keywords": [ "founder", "marketing" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/threads-email-scraper").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 = { "keywords": [ "founder", "marketing", ], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/threads-email-scraper").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 '{ "keywords": [ "founder", "marketing" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call solid-scraper/threads-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/threads-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Threads Email Scraper", "description": "📩 Threads Email Scraper extracts verified contact emails from Threads profiles using advanced filtering by keyword, location & niche. Save time, find leads faster, and boost outreach for sales, agencies & growth teams. 🚀", "version": "0.0", "x-build-id": "9Ym2Qe3L8xrQUaHND" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~threads-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-threads-email-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~threads-email-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-threads-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~threads-email-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-threads-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "A list of keywords to search for.", "default": [ "founder", "marketing" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Threads" ], "type": "string", "description": "Select platform.", "default": "Threads" }, "customDomains": { "title": "Custom Email Domains", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.", "default": 20 }, "engine": { "title": "Engine", "enum": [ "cost-effective", "legacy" ], "type": "string", "description": "Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.", "default": "cost-effective" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "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 } } } } } } } } } } ```