Email List Cleaner & Validator (Syntax, MX, Disposable)

Clean an email list in seconds, for a fraction of the price of full SMTP verifiers. Paste addresses, point to a dataset from another Actor, or give a CSV/TXT link. You get one row per unique address:

• Syntax check: practical RFC rules, IDN domains, mailto: and <...> wrappers cleaned.
• Domain and mail server check: does the domain exist, does it have MX records, or does it explicitly refuse mail (null MX, RFC 7505)?
• Disposable detection: about 10,000 throwaway domains (Mailinator, 10minutemail and similar), subdomains included.
• Role accounts: info@, sales@, support@, admin@ and about 90 more, including common Italian ones.
• Typo suggestions: gmial.com → gmail.com, hotmail.con → hotmail.com.
• Mail provider: Google Workspace, Microsoft 365, Zoho, Proton, Mimecast, Proofpoint and 20+ more, detected from MX records. Useful to segment B2B lists.
• Free vs business: Gmail, Outlook, Yahoo and 80+ consumer providers flagged.
• Domain security: SPF and DMARC records returned as-is.

Honest results

This Actor does not connect to the recipient's mailbox (SMTP), so it never claims an address "exists". The best verdict is good_domain: the address is well-formed and its domain accepts mail. Every row has "mailboxChecked": false. In return it is fast (about 500 addresses per second), cheap, and never puts your IPs or sender reputation at risk.

Verdict	Meaning	What to do
bad	Broken syntax, domain doesn't exist, domain accepts no mail, or disposable domain	Remove
risky	Role account, likely typo (see didYouMean), or no MX record (A-record fallback)	Review or fix
good_domain	Well-formed address on a domain that accepts mail	Keep
unknown	DNS lookup failed (server error or timeout)	Retry later. Not charged.

Input

Field	Description
emails	List of addresses, one per line
datasetId + emailField	Read addresses from an Apify dataset (e.g. a contact scraper's output). The field may hold a string or a list.
fileUrl	Public CSV/TXT link; every address found in the file is checked
maxEmails	Optional cap on unique addresses

Duplicates are removed case-insensitively before checking, so you never pay twice for the same address.

Quick start and verified demo

1. Paste your addresses into Email addresses and select Start. You can also supply a CSV/TXT URL or a dataset from another Actor.
2. Open Overview to review verdicts and All fields for the supporting DNS and detection details.
3. Use good_domain as a domain-level pre-check, review risky, remove or correct bad, and retry unknown later. A mailbox is never verified by this Actor.

The following synthetic sample was tested on October 28, 2026. Six input entries produced five unique rows in a completed run of about 3 seconds:

Demonstration input	Observed verdict	Interpretation
demo@gmail.com	good_domain	Domain accepts mail; mailbox existence is not checked
info@gmail.com	risky	Role-based address for review
demo@mailinator.com	bad	Disposable domain detected
not-an-email	bad	Invalid syntax
demo@example.invalid	bad	Invalid/nonexistent email domain
DEMO@GMAIL.COM	No additional row	Case-insensitive duplicate removed

All addresses above are demonstration strings. No messages are sent and no mailbox connection is made. DNS and detection results can change; runtime depends on list size and domain diversity.

{"emails":["demo@gmail.com","info@gmail.com","demo@mailinator.com","not-an-email","demo@example.invalid"," DEMO@GMAIL.COM "],"maxEmails":20}

Output example

{
    "email": "mario@gmial.com",
    "normalizedEmail": "mario@gmial.com",
    "verdict": "bad",
    "score": 0,
    "reasons": ["disposable_domain", "possible_typo"],
    "didYouMean": "mario@gmail.com",
    "mailboxChecked": false,
    "domain": "gmial.com",
    "domainExists": true,
    "acceptsMail": true,
    "mxHosts": ["…"],
    "mailProvider": null,
    "isDisposable": true,
    "isRoleAccount": false,
    "isFreeProvider": false,
    "hasPlusTag": false,
    "spfRecord": null,
    "dmarcRecord": null,
    "checkedAt": "2026-10-28T14:30:00+00:00"
}

Reason codes: missing_or_multiple_at, invalid_local_length, invalid_local_characters, invalid_domain_syntax, invalid_tld, invalid_domain_encoding, domain_not_found, domain_accepts_no_mail, no_mail_server, disposable_domain, role_account, possible_typo, no_mx_record_uses_a_record, dns_lookup_failed.

Pricing

$0.0003 per unique address with a decisive result (good_domain, risky or bad), equivalent to $0.30 per 1,000. Analysis platform usage is included. unknown results and duplicates are not charged. No start fee.

Chargeable unique addresses	Analysis price (USD)
100	$0.03
1,000	$0.30
10,000	$3.00
100,000	$30.00

Prices above cover the analysis event. Separate Apify charges for keeping or accessing stored results are governed by your Apify plan.

Use cases

• Clean scraped or purchased lead lists before importing into a CRM or email tool.
• Remove disposable and fake sign-ups from a user database.
• Fix typos in customer emails (didYouMean).
• Segment B2B leads by mail provider (Google Workspace vs Microsoft 365 vs others).
• Pre-filter lists before paying for a full SMTP verification, so you only verify addresses worth checking.

Limitations

• No mailbox (SMTP) check: an address on a valid domain may still not exist. Catch-all detection is not possible without SMTP.
• The disposable list is broad but not exhaustive; new throwaway domains appear daily.
• Role-based and free-provider detection are list-based.

Chain it with other Actors

The datasetId input lets you clean the output of any scraper without downloading anything:

1. Run a contact or lead Actor and note its dataset ID.
2. Start this Actor with datasetId set to that ID and emailField set to the field holding the address (a single string or a list both work).
3. Filter the results on verdict and import only good_domain rows into your CRM.

Use it from code

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("bifras/email-list-cleaner").call(
    run_input={"emails": ["demo@gmail.com", "info@gmail.com", "demo@mailinator.com", "not-an-email", "demo@example.invalid", " DEMO@GMAIL.COM "], "maxEmails": 20}
)
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row.get("email"), row.get("verdict"), row.get("reasons", []), row.get("didYouMean"))

It also works with the Apify API, schedules, webhooks, Make, Zapier, n8n and Google Sheets.

FAQ

Why no SMTP mailbox check? It needs a server with an open port 25 and a clean sending reputation, and results are often "unknown" anyway (Gmail, Outlook and catch-all servers do not confirm mailboxes). This Actor is built to be fast and cheap for the first pass, and to be honest about what it did not check: mailboxChecked is always false.

Is good_domain the same as "valid"? No. It means the address is well-formed and its domain accepts mail. The mailbox may still not exist.

What should I delete? Rows with verdict: "bad". Review risky rows: role accounts (info@) are often fine for B2B outreach, and didYouMean shows likely typos you can fix instead of dropping.

Am I charged for duplicates or errors? No. Duplicates are merged before checking, and unknown results (DNS failures) are free.

How big can a list be? Domains are looked up once and reused for every address that shares them, so large lists with repeated domains are fast (about 500 addresses per second in our benchmark of 5,000 addresses over 140 domains). Lists with mostly unique domains take longer, since each domain needs its own lookups. Use maxEmails to cap a run.

Data sources

The disposable domain list comes from disposable-email-domains (CC0). DNS queries go to public resolvers (Cloudflare, Google, Quad9), with DNS-over-HTTPS fallback.