# Reddit Email Scraper (`scrapevanta/reddit-email-scraper`) Actor 📧 Reddit Email Scraper extracts public email addresses from Reddit posts & profiles with accuracy and speed. 🚀 Great for lead gen, outreach, market research, and community insights. 🔍 Easy to use & automate. - **URL**: https://apify.com/scrapevanta/reddit-email-scraper.md - **Developed by:** [ScrapeVanta](https://apify.com/scrapevanta) (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 $4.99 / 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 ### Reddit Email Scraper 🚀 If you’re trying to find contact emails from Reddit communities, manually hunting through bios and posts is slow, inconsistent, and hard to scale. **Reddit Email Scraper** pulls email addresses from publicly available Reddit content using your chosen keywords and email-domain filters. This reddit email scraper is ideal for lead generation, outreach targeting, and research teams who need a reliable reddit email harvesting workflow. It’s built for marketers, data analysts, and researchers who want actionable leads from reddit user email extraction—quickly and in bulk. In one run, you can collect up to your configured **Max Emails** (default: 20) and start exporting results immediately. ⚡ --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "network": "Reddit.com", "keyword": "founder", "title": "marketing founder (example title)", "description": "No data", "url": "https://www.reddit.com/r/example/comments/abc123/example_post/", "email": "alex@example.com", "proxyGroups": ["cost-effective"], "status": "success" } ```` Below is how to read the dataset fields the actor outputs for each discovered email: | Field | Type | What It Tells You | |---|---|---| | `network` | string | Confirms the source network for the record (always `Reddit.com`). | | `keyword` | string | The keyword currently driving discovery—useful for tracking what worked in your outreach research. | | `title` | string | The result title associated with the email source (helps you judge relevance fast). | | `description` | string | Snippet or extracted text connected to the source result (often where the email is detected). | | `url` | string | The web page URL where the email was found so you can verify the context. | | `email` | string | The email address extracted from the publicly available text. | | `proxyGroups` | array | Proxy group information used during scraping for this record. | | `status` | string | Indicates the record result status in your export flow (for example, `success` when a row is pushed). | | `error_message` | string | Present when a run fails for a record (if available in your output pipeline). | | `success` | boolean | Helps you confirm whether the record completed successfully in your dataset handling. | | `timestamp` | string | Useful in downstream systems for auditing when data was produced (if added by your workflow). | | `id` | string | Identifier used by your downstream tools if you attach additional processing (if available in your pipeline). | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "keywords": ["founder", "marketing"], "location": "", "platform": "Reddit", "customDomains": ["@gmail.com"], "maxEmails": 20, "engine": "cost-effective", "proxyConfiguration": {} } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | A list of keywords to search for across Reddit-related publicly available content, used to guide which profiles and pages are considered. | | `location` | ⬜ | Optional location filter to narrow down what the scraper focuses on. | | `platform` | ⬜ | Selects the platform to scrape; in this actor it is `Reddit`. | | `customDomains` | ⬜ | A list of custom email domains (for example `@gmail.com`) so the scraper only collects emails matching your targeting rules. | | `maxEmails` | ⬜ | Caps the maximum number of emails to collect; the scraper stops when it reaches this limit. | | `engine` | ⬜ | Chooses the scraping engine: `cost-effective` (Cost Effective (New)) or `legacy` (Legacy). | | `proxyConfiguration` | ⬜ | Configure proxies for this Actor (highly recommended for larger keyword sets). | | ↳ `proxyConfiguration` | ⬜ | Proxy settings object as supported by Apify’s proxy editor for actors. | *** ### What It Does Reddit Email Scraper finds email addresses in publicly available Reddit bios and post-related text connected to your keywords, then exports the discovered contacts into your Apify dataset. #### Keyword-driven discovery for targeted leads You provide `keywords` (and optional `location`), and the scraper uses them to focus on relevant pages. This makes a big difference when you’re doing reddit contact email scraper research instead of collecting generic email lists. #### Domain filters to control the results With `customDomains`, you can restrict harvesting to email addresses that match the domains you care about. That’s the core mechanism behind reddit email harvesting that’s actually useful for outbound campaigns. #### Clean, dataset-ready output per found email For every extracted email, the actor pushes a structured row containing `network`, `keyword`, `title`, `description`, `url`, `email`, and `proxyGroups`. This makes it straightforward to feed into CRMs, spreadsheets, and downstream analysis. #### Built-in proxy support for reliable scraping The actor supports proxy configuration for this run and is designed for bulk workloads where throttling can occur. You can choose between `engine` options to balance reliability and cost. #### Max-results guardrails to prevent runaway runs You can set `maxEmails` to stop once you’ve collected enough contacts. This helps control scraping time while still allowing you to explore broader reddit dm email list possibilities with the right keywords and domains. Overall, Reddit Email Scraper turns your keyword research into an exportable dataset of emails—without manual copy-paste work. *** ### Why Reddit Email Scraper? There are plenty of ways to pull data from Reddit—here’s why Reddit Email Scraper stands out for outreach and research workflows. #### Built for email-domain targeting Instead of returning every random email it encounters, this tool lets you restrict results with `customDomains`. That means your reddit leads email scraper output is already closer to a usable lead list. #### Practical bulk control with `maxEmails` You can cap output using `maxEmails` so runs don’t balloon past what you need. This is especially helpful when you’re iterating on keywords for a reddit email database. #### Reliable dataset writing as results are found Each discovered email is pushed to the dataset as it’s found, so you get usable partial results rather than waiting for everything to finish. That supports fast experimentation—like testing a new set of keywords for a reddit contact info scraper workflow. *** ### Real-World Use Cases Here's how different teams put Reddit Email Scraper to work: **Sales Teams**\ A sales team starts with a list of target personas and the sub-communities where they hang out, then runs Reddit Email Scraper with `keywords` and `customDomains` to collect emails that match their preferred outreach domains. They export the dataset and quickly import it into their outreach tool, cutting prospecting time from hours to minutes. **Marketing Agencies**\ An agency builds campaign audiences based on founder-led and marketing-led conversations, then uses this reddit email extractor to gather contacts for follow-ups. By iterating on keywords and tightening email domains, they keep the outreach list focused and reduce wasted effort on irrelevant leads. **Freelance Researchers**\ A freelance researcher needs a reproducible workflow for collecting publicly available contact emails tied to specific topics. They run Reddit Email Scraper with carefully chosen keyword sets, export results for analysis, and keep the source `url` for verification. **Developers & Automation Specialists**\ A developer integrates the actor into an automation pipeline via Apify’s dataset export and API-driven workflows. They schedule runs, validate dataset rows using the presence of `email` and `url`, and push results into their internal enrichment stack for further processing. **Data Analysts**\ An analyst compares which keywords lead to the highest-yield email domains by grouping results by `keyword` and `email`. They use the dataset to track trends over time and refine their reddit email harvesting strategy for better coverage. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor page on Apify**\ Go to the actor on Apify: https://console.apify.com and find **Reddit Email Scraper**. 2. **Enter your inputs**\ Add your `keywords` (required). Optionally set `customDomains` (email-domain filters) and `maxEmails` to control volume. 3. **Choose your platform and engine**\ Keep `platform` as `Reddit`. Pick `engine` as `cost-effective` for a faster, cost-aware run, or `legacy` for the alternative engine option. 4. **Set proxy settings (recommended for bigger runs)**\ Configure `proxyConfiguration` using Apify’s proxy options to improve reliability. 5. **Start the run and watch the live log**\ Monitor progress in the Apify Logs and ensure results are being pushed to the dataset. 6. **Open the Dataset tab to see live results**\ As emails are found, dataset rows are generated with fields like `email`, `url`, `keyword`, and `description`. 7. **Export in your preferred format**\ Export from the Apify dashboard as JSON, CSV, or Excel. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Reddit Email Scraper fits directly into your existing workflow. Export your dataset in the Apify dashboard as **JSON**, **CSV**, or **Excel** for immediate use in spreadsheets and databases. You can then import the data into your lead management system, analysis environment, or reporting pipeline. If you want automation, you can connect via **Apify API** to pull results programmatically, or use Apify integrations and automation tools like **Zapier** or **Make** to forward each dataset run to your tools. For scheduled workflows, you can set the actor to run automatically on a schedule, and use webhooks to trigger downstream steps when a run completes. *** ### Pricing Reddit Email Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. On sign-up, you get **$5 platform credits**, which are enough for several real test runs. For heavier workloads, you can scale using Apify’s pay-as-you-go compute model and subscription plans offered on the platform. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Proxy configuration | You can provide `proxyConfiguration` for this Actor to improve scraping reliability. | | Engine choice | You can select `engine` as `cost-effective` or `legacy`. | | Result volume control | `maxEmails` caps output so runs stop once you reach your target. | | Partial results | The actor pushes rows to the dataset as results are found, so you can export partial datasets if you stop early. | | Email-domain filtering | `customDomains` reduces noise by focusing on domains you specify. | Limitations: Reddit Email Scraper works with **publicly available** content on Reddit. If a profile doesn’t publish an email that matches your `customDomains`, fewer results should be expected. Also, results depend on what contact information is actually present in the relevant bios and post-related text. For enterprise-scale needs or custom configurations, reach out and we’ll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes—Apify offers a free tier with sign-up credits, so you can run small tests before scaling up. #### Do I need to log in or create an account on Reddit? No. This actor scrapes from publicly available content related to your keywords and does not require a Reddit account login as part of the workflow. #### How accurate is the extracted data? Accuracy depends on what email addresses are actually present in the publicly available text the scraper reads. The actor extracts emails that match your `customDomains` filter and outputs the associated `url` for context. #### How many results can I get per run? You control the maximum number of emails with `maxEmails` (minimum 1, maximum 10000). The actor stops once it reaches that limit, but it may return fewer depending on how many matching emails it finds. #### How fresh is the data? The data freshness depends on the timing of your run—each run collects current publicly available content at the time it executes. For the latest info, re-run the actor when your keyword targets change. #### Is this legal? Does it comply with GDPR / CCPA? The actor collects data from **publicly available data**. You’re responsible for ensuring your use complies with GDPR, CCPA, Reddit’s Terms of Service, and any applicable local regulations for storing and contacting individuals. #### Can I export to Google Sheets or Excel? Yes. You can export from the Apify dashboard as CSV or Excel, and then import into Google Sheets or any spreadsheet tool you use. #### Can I schedule this to run automatically? Yes. You can set up scheduled runs in Apify so Reddit Email Scraper runs automatically on a schedule and keeps your datasets up to date. #### Can I access results via the API? Yes. You can trigger runs and access datasets programmatically using the Apify API, which is useful for pipelines and recurring lead generation tasks. #### What happens when the actor encounters an error? If a row can’t be pushed to the dataset due to an error, that row may not appear in your output, but the actor is designed to continue producing results as it processes. You can review run logs in Apify to see what happened and export whatever data was successfully captured. *** ### Get Help & Use Responsibly Got a question about Reddit Email Scraper or a feature you'd like added? Reach out at . We’re happy to help with troubleshooting, dataset interpretation, and practical improvements for keyword and domain targeting. **publicly available data** only: this actor does not access private accounts, login-gated pages, or password-protected content. You are responsible for compliance with GDPR, CCPA, and platform Terms of Service when using the extracted data. For data-removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # 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": "Reddit", "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("scrapevanta/reddit-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("scrapevanta/reddit-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 scrapevanta/reddit-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapevanta/reddit-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Email Scraper", "description": "📧 Reddit Email Scraper extracts public email addresses from Reddit posts & profiles with accuracy and speed. 🚀 Great for lead gen, outreach, market research, and community insights. 🔍 Easy to use & automate.", "version": "0.0", "x-build-id": "bWKZ3Qs8vMCL7vif7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapevanta~reddit-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapevanta-reddit-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapevanta~reddit-email-scraper/runs": { "post": { "operationId": "runs-sync-scrapevanta-reddit-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapevanta~reddit-email-scraper/run-sync": { "post": { "operationId": "run-sync-scrapevanta-reddit-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "Reddit" ], "type": "string", "description": "Select platform.", "default": "Reddit" }, "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 } } } } } } } } } } ```