Email Parser for Gmail,
Outlook & Any Mailbox
Connect your mailbox, get clean Excel, CSV & JSON in seconds
MailParse is an email parser that connects directly to your mailbox, Gmail, Microsoft 365, Outlook, or any IMAP server, and automatically extracts the data from every email into clean Excel, CSV, or JSON.
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Free to start · 3 parses per session, no sign-up required
Connects to every major mailbox
Email parser integrations: Microsoft Excel, Outlook & Google Sheets
MailParse has a native Microsoft Excel integration that writes parsed email data straight into a formatted .xlsx file, a Microsoft Outlook integration that reads .msg files without Outlook installed, and a Google Sheets integration with no Apps Script to maintain. Pick your fields once and every message lands in the right columns.
See all email parser integrations
Yes, MailParse has a Microsoft Excel integration. It parses the data in your emails and their attachments and writes it straight into a formatted .xlsx file, with the exact columns you pick, no macros and no manual copy-paste. The Microsoft Excel integration and the Microsoft Outlook integration work the same way, so Outlook does not even need to be installed to read .msg files, and the Google Sheets integration drops the same rows into a live sheet.
Yes, MailParse has a Microsoft Outlook integration too. It reads Outlook .msg and .eml files and mail synced from a Microsoft 365 or Exchange mailbox, then extracts the sender, subject, dates, body fields, and attachment data into Excel, CSV, or JSON. Because the Microsoft Outlook integration parses the raw .msg format directly, you do not need the Outlook desktop app installed to pull data out of saved messages.
Yes, MailParse has a Google Sheets integration for Gmail too. Connect a Gmail or Google Workspace mailbox and the Google Sheets integration writes the fields you pick from every matching email straight into a live Google Sheet, one clean row per message. There is no Apps Script to write and no macro to maintain, so a Gmail to Google Sheets workflow that used to take a script now runs on its own. Prefer to keep the data in your Google Drive as a file? The same rows export to Excel, CSV, or JSON in one click.
How the email parser works
Three steps from inbox to structured data, no setup, no rules engine, no waiting.
Drop in one or more .eml or .msg files from Outlook, Gmail, Apple Mail, or any email client, or switch to the paste tab and drop in raw RFC 822 email text. Bulk uploads are supported for batch processing.
MailParse reads the full MIME structure, headers, multipart body, inline attachments, and extracts sender, recipients (To/Cc/Bcc), date, subject, plain-text body, HTML body, and attachment metadata. On Plus and higher, AI field detection auto-suggests custom fields like order numbers, invoice amounts, tracking numbers, and more.
Preview the extracted rows instantly, then download as Excel (.xlsx), CSV, or JSON, whichever format your workflow needs. Paid plans unlock row-level detail, XLSX formatting, and JSON for API pipelines.
Parse Emails into Spreadsheets in Seconds
Whether you need to parse emails to Excel, export emails to CSV for a CRM import, or convert email data to JSON for an API pipeline, MailParse handles all three with a single click. There is no faster path from "email in your inbox" to "clean rows in a spreadsheet." For spreadsheet teams, the dedicated Microsoft Excel integration turns every parsed message into a formatted .xlsx row with the exact columns you choose.
The most common pain point operations teams describe is this: structured data is locked inside email. Order confirmations arrive formatted as HTML. Invoice totals are buried in a paragraph. Shipping notifications scatter tracking numbers across three lines. Copying that data by hand into Excel is slow, error-prone, and simply does not scale once volume picks up.
MailParse solves this with a file-first approach. Export your emails from any client as .eml files (Gmail, Apple Mail, Thunderbird, Fastmail) or .msg files (Outlook), drop the batch into MailParse, and every email becomes a row in seconds. Each row captures the full header set: from address, from name, to addresses, Cc, Bcc, subject, sent date, body text, and a structured list of attachments. Custom fields let you go further, define order_id, invoice_number, tracking_number, or any pattern and MailParse extracts those values too.
| From | Date | Subject | Order ID | Total | Status |
|---|---|---|---|---|---|
| [email protected] | Jun 3, 2026 | Order #1201 Confirmed | #1201 | $249.00 | Paid |
| [email protected] | Jun 2, 2026 | Order #1200 Confirmed | #1200 | $89.50 | Paid |
| [email protected] | Jun 1, 2026 | Your Amazon order #112-3... | 112-3456789 | $34.99 | Shipped |
| + 47 more rows | |||||
.eml to CSV & .msg to Excel: Any Email File Format
MailParse is the fastest eml to csv converter and msg to excel tool available, no desktop software, no import wizards, no Python scripts.
EML Files to CSV, Excel & JSON
Outlook Express, Gmail export, Thunderbird, Apple Mail
EML is the standard RFC 822 email file format used by virtually every email client. When you export messages from Gmail (using Google Takeout), Thunderbird, Fastmail, or Apple Mail, you get .eml files. MailParse reads EML natively, parsing the full MIME structure including multipart bodies, base64-encoded attachments, quoted-printable encoding, and non-ASCII character sets.
Converting EML to CSV is a common task for data analysts who export a folder of emails and need the headers as spreadsheet columns. MailParse handles this in seconds: drop a folder of EML files, select your field checklist, and download a single CSV with one row per email. For richer workflows, EML to Excel (.xlsx) output gives you proper column headers, formatted dates, and multi-column layouts that CSV cannot replicate faithfully.
- EML to CSV (flat rows, any tool)
- EML to Excel (.xlsx with headers)
- Convert EML to JSON for APIs
- Extract data from EML file in bulk
- Open EML file in Excel without macros
MSG Files to CSV, Excel & JSON
Microsoft Outlook (Windows & Mac), Exchange, Office 365
MSG is Microsoft's proprietary compound-document email format used by Outlook on Windows and macOS. Unlike EML, MSG files use a binary CFBF (Compound File Binary Format) container that cannot be opened by a text editor or parsed by a simple regex. MailParse includes a dedicated MSG parser that reads the full Outlook MAPI property set, headers, RTF and HTML bodies, embedded objects, and attachment streams, without requiring any Outlook installation.
MSG to CSV and MSG to Excel are the two most requested conversions from Outlook-heavy enterprise ops teams. MailParse extracts everything you need: From, To, Cc, Bcc, Subject, SentOn, Body, and a complete list of attachments (filename, MIME type, size in bytes). The result is a clean spreadsheet row ready for reconciliation, analysis, or CRM import.
- MSG to CSV (Outlook bulk export)
- MSG to Excel (.xlsx with all headers)
- Convert MSG to spreadsheet format
- Extract data from MSG file without Outlook
- Bulk MSG parsing for Outlook archives
Paste Raw Email Text, No File Needed
Don't have a file? No problem. MailParse also accepts raw RFC 822 email text pasted directly into the text area. This is useful when you're working in a webmail client like Gmail or Outlook.com, use "Show original" / "View source" to copy the raw message and paste it into MailParse. The parser handles all encodings: quoted-printable, base64, UTF-8, ISO-8859-1, and Windows-1252. It correctly handles multipart/mixed, multipart/alternative, and nested MIME structures that trip up simpler parsers.
Extract Every Field From Every Email
MailParse is a complete email data extraction tool, not just a header scraper. Here is exactly what it extracts.
Full From header: display name and email address. Supports RFC 2047 encoded names in any language or character set.
All recipient fields as structured arrays. Multiple addresses per field, parsed individually. Bcc is extracted from the raw headers when present.
Parsed and normalized to ISO 8601 format. Handles all common email date formats and timezone offsets. Ready for Excel date columns without manual conversion.
Full decoded subject. Handles long subjects, encoded words, and Unicode characters that appear garbled in raw headers.
Clean plain-text extraction from multipart emails. HTML is stripped and whitespace is normalized. Quoted reply content is preserved but optionally separable.
Full attachment inventory: filename, MIME type, file size (bytes), and Content-ID. Plus plan adds attachment content extraction, parse the PDF or CSV inside the attachment into the same row.
Define your own extraction targets: order_id, invoice_number, tracking_number, amount, vendor, anything that appears in the email body. Use regex patterns or simple label matching.
AI reads the email content and auto-suggests custom fields based on what it finds, amounts, dates, reference numbers, names, addresses. No manual pattern writing required.
Many transactional emails contain HTML tables, order line items, itemized invoices, shipping manifests. MailParse Plus extracts these tables as additional rows in your spreadsheet.
Email Parser Use Cases
From invoice data capture to order extraction and lead parsing, MailParse handles every structured-email workflow.
Extract Order Data from Email
Every time a customer places an order on Shopify, WooCommerce, Amazon, or a marketplace, an order confirmation email lands in your inbox. Over days and weeks, hundreds of these accumulate. When it is time to reconcile sales, calculate channel fees, or catch fulfillment gaps, the answer to "how many orders did we process?" is buried across hundreds of emails.
MailParse solves this by letting you parse order confirmation emails into a sales spreadsheet. Export the folder from Gmail or Outlook as .eml files, drop them into MailParse, and define custom fields for order_id, order_total, customer_name, and product_name. In seconds you have a clean Excel file with one row per order, ready for pivot tables, channel analysis, and reconciliation against your payment processor.
This is the difference between a 15-minute data pull and a half-day of manual copy-paste. Operations managers at e-commerce companies with 5 to 200 staff use MailParse every week to keep their spreadsheets current without engineering involvement. No Zapier, no webhook, no custom code, just drop the files and download the rows.
Extract Tracking Numbers from Email
Logistics teams receive dozens to hundreds of shipment notification emails every day from UPS, FedEx, USPS, DHL, Amazon Logistics, and carrier APIs. Each email contains a tracking number, ship date, estimated delivery date, and destination, but that data sits locked in the email body with no easy way to get it into a spreadsheet for monitoring or customer updates.
MailParse's extract tracking number from email workflow takes seconds. Export your shipment notification folder as .eml files, define tracking_number and carrier as custom fields, and MailParse extracts them alongside standard headers. The result: a shipping log spreadsheet with every tracking number, carrier, and ship date, ready for filtering, sorting, and sharing with your customer service team.
Parse shipping notification emails in bulk and your team stops copying tracking numbers by hand. The Pro plan adds webhook delivery, as soon as a new shipment email arrives, MailParse pushes the extracted row to your internal system automatically.
Extract Invoice & Receipt Data from Email
Small businesses and bookkeepers regularly receive invoice emails, payment confirmation emails, and SaaS subscription receipts. Getting this data into a ledger spreadsheet or accounting system means opening each email, reading the amount, date, and vendor, and manually entering it, or, increasingly, forwarding emails to a rules-based parser and hoping the template still matches.
MailParse's extract invoice data from email capability sidesteps both approaches. Define custom fields for invoice_number, amount, due_date, and vendor, MailParse uses a combination of regex patterns and AI-assisted detection (Plus+) to find and extract those values from the email body. The output is a flat CSV or Excel file with one row per invoice: vendor, amount, invoice number, due date, and the full email context. Ideal for invoice data capture software workflows, expense tracking, and VAT reconciliation.
Extract receipt data from email works the same way for consumer receipts, e-commerce purchase confirmations, and subscription renewals. MailParse can handle an entire year of receipts in one batch upload, taking what would be hours of manual entry down to minutes.
Extract Leads from Email, CRM-Ready CSV
When a contact form on your website fires, it sends an email notification to your inbox. That email contains the lead's name, email address, company, phone number, and message. Over time, your leads inbox fills up with hundreds of these notifications, and your CRM is still empty because nobody had time to manually enter them.
MailParse is a fast, no-code email lead extraction tool. Export your leads inbox as .eml files, define custom fields for name, company, phone, and message, and download a CRM-ready CSV in one click. Every lead becomes a row. Import directly into HubSpot, Salesforce, Pipedrive, or any CRM that accepts a CSV upload.
For RevOps teams that receive contact form submissions from multiple landing pages, MailParse's extract leads from email workflow eliminates the manual middle step entirely. Parse the entire backlog in a batch, deduplicate in Excel, and import into your CRM, all without writing a single line of code.
Email Parsing Across Every Industry
Structured email data is hidden in every department. MailParse surfaces it for every team, without code, without forwarding rules, and without waiting.
Real Estate Inquiry & Listing Email Parser
Real estate agents and brokers receive buyer and renter inquiry emails from Zillow, Realtor.com, Trulia, Homes.com, and their own website contact forms, often across multiple email inboxes simultaneously. Each inquiry contains the prospect's name, contact details, the property address they are interested in, their timeline, and budget range. Without a dedicated CRM integration, this data sits buried in email threads.
MailParse lets real estate professionals extract data from property inquiry emails in batch. Export a month of inquiries from your leads inbox as .eml files, define custom fields for property_address, buyer_name, contact_phone, budget, and move_in_date, and download a clean Excel sheet with every prospect in a row. Import directly into any CRM or use the spreadsheet for manual pipeline management.
Real estate teams also use MailParse to parse listing alert emails. MLS systems and property portals send automated alerts when new listings match saved search criteria. By parsing these alert emails, buyer's agents can build a live comparison spreadsheet of available properties, addresses, prices, listing dates, and agent contact details, without manually copying data from each alert notification.
Recruiting & HR Email Parser: Parse Job Applications
When candidates apply for a job, an email notification lands in the recruiting inbox, or directly in the hiring manager's inbox. Each notification contains the applicant's name, email, phone number, the position they applied for, and often a cover letter body. When hiring volume picks up, a dozen applicants per role, multiple open roles, multiple email inboxes, consolidating this data becomes a significant coordination burden.
MailParse is used by recruiting coordinators to parse job application emails into a structured ATS-ready spreadsheet. Export the applications inbox as .eml files, define fields for applicant_name, applicant_email, position_applied, phone_number, and cover_letter_snippet. The output is a consolidated Excel file with one row per applicant, ready for sorting by date, position, or status, and for bulk import into an ATS system.
HR teams also use MailParse to process background check notification emails, reference check responses, and offer acceptance confirmations. Any email that arrives on a predictable schedule with structured content can be parsed. Define the custom fields once, run the batch parse weekly, and keep your hiring pipeline spreadsheet current without manual data entry.
applicant_name
applicant_email
phone_number
position_applied
application_date
cover_letter
years_experience
linkedin_url
referral_source
salary_expectation
Time saved per hiring cycle
Teams processing 20 to 60 applications per open role report saving 2 to 4 hours per hiring cycle when using MailParse to consolidate application emails into a single ATS-import spreadsheet.
Customer Support Email Parser: Build a Ticket Log
Support teams that receive customer inquiries directly to an email inbox, rather than through a ticketing platform, face a growing problem as volume scales: there is no structured record of what came in, when, who it was from, or whether it was resolved. Building that log manually from the inbox is tedious and incomplete.
MailParse helps support managers build a structured support ticket log from email. Export the support inbox as .eml files (weekly or monthly), define custom fields for customer_name, issue_category, product, and urgency_indicator, and download a complete support log with one row per email. This log can feed reporting dashboards, SLA tracking sheets, or retrospective analysis without a dedicated helpdesk tool.
Support teams also use MailParse to parse escalation notification emails from internal systems, extract data from complaint emails for compliance reporting, and build customer satisfaction summary spreadsheets from CSAT survey notification emails. Any email-based workflow that produces structured text can be turned into a spreadsheet with MailParse.
Finance Teams: Parse Vendor Invoices, Bank Notifications & Compliance Emails
Finance departments receive a constant stream of structured emails: vendor invoices, payment reminders, bank transfer notifications, subscription renewals, credit card statement alerts, and regulatory compliance notifications. Each email contains specific financial data, amounts, dates, reference numbers, account identifiers, that needs to make its way into a ledger, ERP system, or compliance log.
MailParse handles the full finance email stack. Vendor invoice emails yield invoice number, vendor name, amount, due date, and line items. Bank notification emails yield transaction amount, account reference, and transaction date. Subscription renewal emails yield vendor, renewal amount, and next billing date. Define a custom field set once and batch-parse an entire quarter's worth of emails in a single drop.
For compliance and audit workflows, MailParse's structured output serves as a verifiable email-to-record log. Finance controllers use the CSV output to reconcile against bank statements, while compliance officers use it to document vendor relationships and payment patterns. The Pro API allows direct integration with ERP systems, so the parsed data flows into your financial records without a manual import step.
How Email Parsing Works, A Technical Deep Dive
Understanding the email format reveals why email parsing is hard, and why MailParse handles edge cases that simpler tools miss.
What is MIME and Why Does It Matter?
Every email you receive is encoded in MIME (Multipurpose Internet Mail Extensions), a standard defined in RFC 2045 to 2049. MIME extends the basic RFC 822 email format to support non-ASCII characters, HTML bodies, file attachments, and complex nested message structures. When you look at the raw source of an email, you see a series of headers followed by a body, but the body can itself contain multiple nested parts, each with its own content type, encoding, and character set.
A typical HTML email with an attachment is a multipart/mixed message that contains a multipart/alternative part (with both a text/plain and a text/html version of the body) plus one or more application/octet-stream or typed attachment parts. Each part boundary is delimited by a unique MIME boundary string defined in the Content-Type header. Parsing this structure correctly requires a proper recursive MIME parser, not a regex applied to the raw text.
MailParse includes a full recursive MIME parser that handles every standard MIME structure: multipart/mixed, multipart/alternative, multipart/related, multipart/report, and nested combinations. It correctly handles encoded words (RFC 2047) in headers, inline images referenced by Content-ID, and message/rfc822 attachments (forwarded emails embedded as attachments).
Character Encoding, Quoted-Printable & Base64
Email content encoding is a common source of data corruption in simple parsers. Email headers and bodies can be encoded in several different ways depending on the sending client and the content type. Quoted-printable encoding replaces non-ASCII bytes with =XX hex sequences and is common in HTML email bodies. Base64 encoding encodes binary data (attachments, images) as ASCII-safe character strings. RFC 2047 encoded words encode non-ASCII characters in header values like subject lines and display names.
MailParse decodes all three encodings correctly for every part of the email. Subject lines with Japanese, Arabic, or accented European characters are fully decoded. HTML bodies with encoded special characters are cleaned to readable plain text. Base64-encoded PDF attachments are correctly identified, their metadata extracted, and their content made available for extraction on the Plus plan.
Character set (charset) handling is equally important. Emails can arrive in UTF-8, ISO-8859-1, ISO-8859-2, Windows-1252, Shift_JIS, or EUC-KR. MailParse detects and correctly decodes all standard charsets, converting everything to normalized UTF-8 in the output. This means your extracted spreadsheet contains clean, readable text, not garbled character sequences or question marks where accented characters should appear.
The .eml File Format Explained
An .eml file is simply a plain text file that stores an RFC 822 / RFC 2822 email message exactly as it was transmitted over SMTP. The file begins with a sequence of header lines (From:, To:, Subject:, Date:, MIME-Version:, Content-Type:, etc.) followed by a blank line and then the message body. Despite being a plain text format, EML files can contain binary content in the form of base64-encoded MIME parts.
Gmail exports emails as .eml files through Google Takeout. Apple Mail can export individual messages as .eml. Thunderbird exports are .eml by default when you drag a message to your desktop. Fastmail, ProtonMail, and most standards-compliant email clients support .eml export. The eml to csv and eml to excel workflows all start with this export step, which any email client can perform without technical knowledge.
One subtlety with EML files: the From line (note the trailing space) at the very beginning of an mbox file is sometimes confused with the From: header. MailParse correctly handles both mbox-format multi-message files and individual single-message EML files, extracting each message as a separate row in the output spreadsheet.
The .msg File Format Explained
The .msg file format is Microsoft's proprietary email storage format used by Outlook. Unlike the text-based EML format, MSG files use the CFBF (Compound File Binary Format), also known as OLE2 or Structured Storage, which is the same binary container format used by older .doc and .xls Microsoft Office files. This means MSG files cannot be opened in a text editor or parsed by a simple string parser.
Inside a CFBF container, an MSG file stores email data as a tree of named MAPI (Messaging Application Programming Interface) properties. Each property is identified by a 4-digit hexadecimal property tag. Key properties include 0x0037 (Subject), 0x0C1F (Sender email address), 0x0039 (Client submit time), 0x1000 (Plain text body), and 0x1013 (HTML body). Attachments are stored as child directory entries within the compound storage.
MailParse's MSG parser reads the CFBF binary format directly and maps the MAPI property tree to clean structured fields, without requiring Outlook to be installed, without COM automation, and without any Windows dependency. This makes MailParse the most accessible msg to excel and msg to csv tool available: upload from any browser on any operating system and receive clean structured data.
How AI Field Detection Works
Standard email parsers extract well-known fields (From, To, Date, Subject) because these are defined in the RFC specification and appear in predictable header positions. The hard part is extracting domain-specific values from the email body, an invoice amount buried in a sentence, an order number in a table cell, a tracking number in a formatted string.
MailParse's AI field detection (Plus plan) reads the full parsed email body and uses a large language model to identify likely custom fields automatically. Rather than writing a regex like /Order #(\d+)/i, the AI sees the email and says: "this email contains an order ID in the format #XXXX, an order total formatted as $X,XXX.XX, and a customer name." It proposes these as custom field candidates, which you can confirm or dismiss in one click. This is a fundamentally different approach from rules-based parsers, which break whenever the sending vendor changes their email template.
The AI approach has two important advantages over regex-based extraction. First, it handles format variation: the same logical field (invoice amount) might appear as "$1,234.56", "USD 1,234.56", "Total: 1234.56 USD", or "Amount Due: $1,234.56", all of which an AI can correctly identify as the same field, while a regex would only match one pattern. Second, it handles novel fields without prior training: if your vendor emails contain a field you have never extracted before, the AI can identify it without you needing to know its exact format in advance.
For teams with consistent email formats and a preference for deterministic extraction, MailParse also supports manual custom fields with explicit regex patterns and label-matching rules. The two approaches can be combined: use AI detection to discover fields in a sample email, then lock them down with a regex for production batch runs. This gives you both the convenience of AI and the reliability of deterministic extraction.
Email Parser API, Attachments & Bulk Parsing
Beyond the drag-and-drop UI, MailParse offers a full email parser API (Pro plan) and bulk batch upload for high-volume workflows.
Parse Email Attachments
Emails often carry the real data inside an attachment rather than in the body. An invoice might arrive as a PDF attachment, an order manifest as a CSV, or a shipping label as an image. MailParse Plus adds attachment content extraction: the parser reads PDF and CSV attachments and merges their content into the same row as the email headers. You get the full picture, email metadata plus attachment data, in a single spreadsheet row.
Email Parser API (Pro)
The Pro plan exposes a REST API for programmatic email parsing. POST a raw email body or a stored file path, specify the fields and format, and receive structured JSON back within milliseconds. Ideal for integration engineers building internal workflows, developers connecting email parsing to a data pipeline, or teams needing an email-to-database bridge without maintaining a custom parser. The email parser API handles authentication via API keys, rate limits per plan, and returns RFC-compliant structured data.
Bulk Email Parser
The Starter plan adds bulk upload: drop multiple .eml or .msg files at once, and MailParse combines all parsed results into one consolidated spreadsheet. Analysts who export an entire Gmail label or Outlook folder, potentially thousands of emails, can process the whole batch in a single operation. The bulk email parser queues files asynchronously, shows progress, and emails a download link when the batch is complete. Starter handles up to 100 emails per month; Pro handles unlimited volume.
Webhook Delivery (Pro)
Pro plan users can configure a webhook endpoint. Every time a new email is parsed, MailParse POSTs the structured JSON payload to your endpoint, enabling real-time email to database pipelines, live CRM updates, and event-driven automation without polling. Combined with API access, this makes MailParse a fully programmable email data extraction layer for any application.
{
"parse_job_id": 18421,
"status": "completed",
"email": {
"from_address": "[email protected]",
"from_name": "Shopify Orders",
"to": ["[email protected]"],
"date": "2026-06-03T10:42:00Z",
"subject": "Order #1201 confirmed",
"body_text": "Hi Jane, your order...",
"attachments": [
{
"filename": "invoice_1201.pdf",
"mime_type": "application/pdf",
"size_bytes": 42880
}
]
},
"custom_fields": {
"order_id": "#1201",
"order_total": "$249.00",
"customer_name": "Jane Smith"
},
"output_format": "json"
}POST https://mailparse.ai/api/v1/parse
Returns structured JSON for every field in <500ms. Supports file upload, raw body, and stored path inputs.
The Best Email Parser: Why MailParse Wins on Speed
Every other email parsing software makes you set up a mailbox, forward emails, build templates, and wait for webhooks before you see a single row. MailParse is instant and file-first.
| Feature | MailParse | Mailparser.io | Parseur | Parsio |
|---|---|---|---|---|
| File upload (.eml/.msg) | ✓ | ✗ | Partial | ✗ |
| Paste raw email text | ✓ | ✗ | ✗ | ✗ |
| Zero setup to first row | ✓ | ✗ (mailbox + rules) | ✗ (template first) | ✗ (mailbox first) |
| No forwarding rules | ✓ | ✗ | ✗ | ✗ |
| Excel (.xlsx) output | ✓ | ✓ | ✓ | ✓ |
| CSV output | ✓ | ✓ | ✓ | ✓ |
| JSON output | ✓ | ✓ | ✓ | ✓ |
| AI field detection | ✓ Plus+ | ✗ | ✓ | ✓ |
| Email parser API | ✓ Pro | ✓ | ✓ | ✓ |
| Bulk file batch upload | ✓ Starter+ | Via forward | Via forward | ✗ |
| Attachment extraction | ✓ Plus+ | Limited | ✓ | ✓ |
Looking for a Mailparser alternative or Parseur alternative?
MailParse is the only email parsing software that is fully file-first: no mailbox setup, no forwarding rules, no templates before you see your first row. Drop a file and get structured data, instantly.
Start free, no credit card requiredLooking for a Mailparser Alternative or Parseur Alternative?
Here is an honest comparison of the main email parsing tools on the market and where MailParse fits.
MailParse vs Mailparser.io
Mailparser.io is a forwarding-based email parsing service. To use it, you create a dedicated @mailparser.io inbox address, configure your email client or CRM to forward incoming messages to that address, build parsing rules (templates) that define what to extract, and then wait for emails to arrive and be processed. The forwarding model works well for high-volume automated pipelines but has significant downsides for teams that just want to parse emails they already have.
The most common complaint users have about Mailparser.io, and the reason many look for a Mailparser alternative, is the setup friction. You cannot simply upload a .eml file or paste an email and get data back. You must set up the forwarding address, build a template, forward a test email, verify the extraction, and then forward your real emails. If your vendor changes their email layout, the template breaks and you must rebuild it.
MailParse is designed to be the opposite experience. No forwarding address to create. No templates to build before you see your first row. No email client configuration required. Drop in a .eml or .msg file you already have and get structured data in seconds. For one-time data pulls, backlog cleanups, and teams that deal with email files rather than live email streams, MailParse is dramatically faster to start with.
MailParse vs Parseur
Parseur is a template-based email parser with a strong reputation for accuracy on well-defined email formats. The workflow: connect a mailbox, forward emails, create a template by clicking on the fields you want to extract in the first email that arrives, and then wait for subsequent emails to be parsed against that template. Parseur also offers a GPT-based extraction mode that reduces template maintenance burden.
Teams looking for a Parseur alternative typically do so for one of three reasons: pricing (Parseur scales by the number of emails processed per month and can become expensive at volume), setup time (the mailbox/forwarding setup is a barrier for one-time or infrequent jobs), or the need to parse .msg files from Outlook (Parseur is email-forward-only and does not accept file uploads of local .msg files).
MailParse fills these gaps directly. File upload means no forwarding setup. MSG file support covers Outlook archives. And the pricing model (flat monthly fee per plan rather than per-email metering) is predictable for teams processing variable volumes. For developers, the MailParse API (Pro) provides a comparable integration pathway to Parseur's webhook output, with the additional advantage of accepting file uploads rather than only email-forwarded content.
MailParse vs Parsio
Parsio is a newer AI-native email parsing tool that uses GPT-based extraction to eliminate the template-building step. Like Parseur and Mailparser.io, Parsio is mailbox-forward-only, there is no option to upload a file or paste raw email text. Parsio's main strength is that its AI extraction is genuinely good at handling varied email formats without custom rules, but this comes at a price point that may not be justified for small-volume users.
MailParse offers AI extraction (Plus plan) comparable to Parsio's GPT-based detection, but combines it with the file-first approach that Parsio lacks. If you have Outlook .msg files you need to parse, or a historical batch of emails exported from Gmail, MailParse is the only AI-powered option that can handle them without a forwarding setup.
When to Use Each Tool
Every tool has a legitimate use case. Here is a plain-language guide:
You have .eml or .msg files you want to parse now, or you need to process a backlog of existing emails, or you want to avoid forwarding setup entirely. Best for ops teams, analysts, and developers who work with email files.
You have a high-volume ongoing email stream (thousands per day) and are willing to invest in forwarding + template setup for reliable automated extraction. Best for production pipelines.
You have a well-defined email format from a specific vendor and want point-and-click template creation with reliable ongoing extraction. Best for teams with consistent email layouts.
You want AI-based extraction with minimal template work and you have a live email stream to forward. Best for varied email formats where template maintenance is the primary pain.
The File-First Difference
Every competing email parser tool is built around a forwarding mailbox model: set up an address, forward emails, wait for processing, retrieve results. This model is powerful for continuous live-stream parsing but creates a fundamental barrier for the most common actual need: "I have a bunch of emails that I already received and I need to get data out of them right now."
MailParse's file-first approach is the direct answer to that need. The emails are already in your inbox, or already exported as .eml or .msg files. There is no reason to forward them somewhere else, wait for them to be received, and then retrieve the extraction. MailParse works directly on the files you have, produces results immediately, and lets you download and move on. For one-time jobs, batch backlog processing, and any workflow where the emails already exist, MailParse is the right tool, and no other email parser offers this capability at comparable depth.
Enterprise-grade security for your email data
Email often contains sensitive business data. MailParse is built with security and privacy at every layer.
TLS 1.3 for all uploads and downloads. Files and extracted data are encrypted at rest with AES-256.
Role-based permissions for team members. Every file access and export is logged in the audit trail.
Uploaded email files and extracted records are auto-deleted after 30 days by default. Enterprise plans support custom retention policies.
MailParse follows SOC 2 Type II controls for data handling. Enterprise includes DPA and custom compliance support.
Email to Excel, Email to CSV, or Email to JSON: Which Format Should You Use?
Choosing the right output format from your email parser determines how smooth the next step in your workflow will be. Here is an honest breakdown.
Email to Excel
Excel (.xlsx) output is the right choice for any workflow where a human being will be working directly with the data, filtering, sorting, creating pivot tables, using VLOOKUP, building charts, or sharing with colleagues who are not technical. MailParse's Excel output uses proper column headers derived from your field set, formats date columns as Excel date values (not text strings), and handles special characters in field values without the quoting artifacts that plain CSV can produce.
Parse emails to Excel when your workflow ends in spreadsheet analysis. E-commerce reconciliation, finance reporting, HR pipeline management, and any use case where the final consumer is a spreadsheet user benefits from .xlsx output. Google Sheets users can import .xlsx files directly, so the format works for both Excel and Sheets workflows.
The only limitation of Excel output is file size for very large batches (5,000+ rows). For those volumes, CSV is lighter and faster to open.
Best for
Email to CSV
CSV is the universal interchange format. Every CRM, every database, every analytics tool, and every data pipeline accepts CSV. When your workflow ends with an import into another system, HubSpot, Salesforce, Pipedrive, Airtable, a PostgreSQL database, a data warehouse, CSV is typically the format the receiving system expects.
Parse emails to CSV when your next step is a system import. CRM lead imports require CSV. Database bulk inserts work best with CSV. Analytics tools like Tableau and Power BI accept CSV as a quick data source. The flat structure of CSV also makes it ideal for large batches, a 10,000-row CSV opens faster than an equivalent Excel file and is smaller on disk.
One caveat: if your email body text contains commas or newlines, those values will be quoted in the CSV. Most tools handle RFC 4180-compliant quoting correctly, but older or simpler import tools may misparse quoted fields. If you see import issues with CSV, switch to Excel output.
Best for
Email to JSON
JSON output from MailParse is designed for developer and API workflows. Unlike CSV and Excel, which are inherently flat row structures, JSON can represent complex nested data. Multi-value fields like the recipients list (multiple To/Cc addresses), attachment lists (multiple attachment objects with filename + MIME type + size), and custom fields with structured subfields are all preserved as proper JSON arrays and objects.
Email parser API output is always JSON. Integration engineers building internal tools, data pipeline architects connecting MailParse to a message queue or data warehouse, and developers building email-to-database bridges all use JSON output. The Pro plan's API accepts a POST request and returns structured JSON in under 500ms, making it fast enough for synchronous integration in most workflows.
JSON output also has the advantage of being schema-preserving across all field types: dates are returned as ISO 8601 strings, numbers as numeric types, and arrays as JSON arrays, rather than the flat string representation that CSV requires. This makes downstream type handling simpler for developers.
Best for
Export Emails to Excel: The Complete Workflow
The phrase export emails to Excel describes a workflow that millions of operations professionals attempt every week, and it has no good native solution in any email client. Gmail does not export to Excel. Outlook can export to CSV (with limited fields) but not to a clean multi-column Excel file with custom fields. Apple Mail has no export-to-spreadsheet feature at all.
The traditional workaround is to export to CSV using the email client's built-in export (which gives you only basic headers, no custom fields, and inconsistent date formatting) and then manually clean up the file in Excel. This takes significant time, produces inconsistent results, and completely breaks down if you need custom fields like order numbers or invoice amounts extracted from the body.
MailParse is the direct solution to export emails to Excel: export as .eml or .msg files (one step, built into every email client), drop into MailParse (one step, drag and drop), define your fields and choose Excel output (one minute of configuration), and click Parse Now. The result is a proper .xlsx file with your custom columns, not a messy flat CSV that needs post-processing.
For teams that do this regularly, weekly ops reconciliation, monthly invoice capture, daily shipping log, MailParse saves the field set as a template so the configuration step is eliminated on repeat runs. The workflow becomes: export emails → drop in MailParse → download Excel. Three steps, five minutes, done.
The email to CSV converter workflow is identical in setup but produces a .csv file instead of .xlsx. Use it when your next step is a system import rather than manual spreadsheet work. The same template that exports to Excel for your ops team can produce a CSV for your CRM import, just change the output format selector before running the parse.
For high-frequency workflows where emails arrive continuously, the Pro plan's email parser API and webhook delivery automates the entire pipeline: new email arrives → parsed automatically → structured row delivered to your system → no manual export required. This is the path from the simple "drop files and download" workflow to a fully automated email to database pipeline.
Trusted by operations teams, developers, and analysts
Who Uses MailParse
From solo operators to enterprise data teams, anyone who needs to get structured data out of email uses MailParse.
Operations Managers
Ops teams at e-commerce brands, logistics companies, and service businesses use MailParse to automate their weekly data pulls. Instead of spending Monday mornings copying data from order emails into reconciliation spreadsheets, they run a five-minute MailParse batch and have clean data before the 9am standup. Common workflows: order reconciliation, shipping log updates, vendor invoice capture.
Developers & Integration Engineers
Developers use MailParse to replace brittle custom MIME parsing code with a maintained API. Instead of maintaining a Python or Node.js email parser that breaks whenever a vendor changes their template, they POST to the MailParse API and get clean JSON back. The Pro API integrates with any data pipeline, webhook system, or internal tool in minutes. No more debugging RFC 2047 encoding edge cases.
Bookkeepers & Finance Teams
Bookkeepers use MailParse to extract invoice and receipt data from vendor emails into a structured ledger CSV. Finance teams use it to build expense reports, reconcile SaaS subscription costs, and document vendor payment records for audit. The AI field detection handles the format variation across different vendors' invoice emails without manual template creation per vendor.
Sales & RevOps Teams
Sales teams use MailParse to extract leads from contact form notification emails and import them into their CRM. RevOps teams use it to rebuild lead records from email backlogs when switching CRMs, to deduplicate inquiry history, and to build prospecting lists from inbound inquiry emails. Custom fields handle the non-standard formats that different form builders produce.
Data Analysts
Analysts use MailParse to pull email data into analytical workflows. Whether building a dataset of outbound email responses for A/B test analysis, extracting historical order data for a trend report, or building a multi-channel sales dataset from email archives, MailParse provides the clean, structured data layer that feeds Excel, Python, R, or any BI tool.
Small Business Owners
Solo operators and small business owners use MailParse without any technical background. The drag-and-drop UI requires no setup, no coding, and no forwarding rules. The free tier (3 parses, no sign-up) lets them test the tool on their own emails before upgrading. For small businesses that process 20 to 100 emails per month with structured data, the Starter plan pays for itself in the first hour of manual data entry it eliminates.
What teams say about MailParse
"We process 200+ Shopify order confirmation emails every week. MailParse turned what was a 3-hour Monday morning task into a 10-minute file drop. The custom field extraction for order IDs and totals is exactly what we needed."
"I build internal tools and needed a quick way to extract structured data from MSG files without writing a MIME parser. The JSON API is clean and the response time is impressive. No more brittle regex on email headers."
"My bookkeeper used to spend two hours every month extracting invoice data from vendor emails into our ledger. Now she drops the EML export into MailParse and downloads the CSV. Two hours became five minutes."
Email Parser Quick-Start Guide
Get from email to spreadsheet in under five minutes on your first try. Here is everything you need to know before you start.
Before You Start: Five Things to Know
No sign-up for the first 3 parses
Try MailParse with your own emails immediately, no account, no credit card. The free parse limit resets per session. Upload a real email to see how it works before deciding to upgrade.
EML and MSG are the fastest input paths
Export emails from your client as .eml or .msg files. Single emails can also be pasted as raw text using the "Paste Email" tab, useful for quick one-off extractions from webmail.
Start simple, add custom fields after
On your first parse, select only the standard fields (From, Date, Subject, Body). Once you see the output and understand what your emails contain, add custom fields for the domain-specific values you need.
Custom fields match the body, not the headers
Standard fields (From, To, Date, Subject) are extracted from email headers. Custom fields (order_id, invoice_number, tracking_number) are extracted from the email body using pattern matching or AI. The two sets are combined in your output row.
Bulk upload saves the most time
The real time savings come from batch processing, dropping 50, 200, or 500 emails at once and downloading a single combined spreadsheet. Start with a single email to verify your field set, then run the full batch.
Supported Input Formats
.eml
RFC 822 email file, Gmail, Apple Mail, Thunderbird, Fastmail, any standards-compliant client
.msg
Microsoft Outlook binary format, Outlook Win/Mac, Exchange, Office 365
.mime
Raw MIME email file, any client that exports in raw MIME format
.email
Generic email text file, some clients save with this extension
.txt
Plain text email, raw RFC 822 content saved as .txt
Paste
Raw RFC 822 email text pasted into the text area, copy from "Show original" in Gmail or "View source" in Outlook
Email Parser Contact & Support
Questions about a specific email format, custom field pattern, or bulk processing workflow? Our team answers quickly.
[email protected]Start parsing emails for free
3 free parses per session, no sign-up required. Upgrade to Starter ($49/mo) for 100 emails per month, custom fields, and Excel output.
Email Parser: Frequently Asked Questions
An email parser is a tool that reads a raw email message, including all headers (From, To, Cc, Bcc, Date, Subject) and the message body, and extracts the data into structured fields. MailParse is an online email parser that works with .eml files (from Gmail, Apple Mail, Thunderbird), .msg files (from Microsoft Outlook), and raw pasted email text. The extracted data is exported as Excel, CSV, or JSON.
Export your emails from your email client as .eml files, drop them into MailParse, select the fields you want to extract (sender, date, subject, body, etc.), choose CSV or Excel (.xlsx) as the output format, and click Parse Now. MailParse reads the MIME structure and produces a clean spreadsheet with one row per email. No macros, no desktop software, no Python scripts required.
Yes. MailParse includes a native MSG parser that reads Microsoft Outlook's binary CFBF format without requiring Outlook to be installed. It extracts the full MAPI property set: sender, all recipient fields (To/Cc/Bcc), subject, sent timestamp, plain-text and HTML body, and a complete list of attachments with filenames and sizes.
Every other email parsing tool, Mailparser.io, Parseur, Parserr, Parsio, requires you to set up a forwarding mailbox, configure parsing rules or templates, and wait for emails to arrive before you see a single extracted row. MailParse is file-first: drop in a .eml or .msg file, or paste raw email text, and get structured data immediately. No setup, no forwarding, no template maintenance. MailParse is the fastest path from "email in inbox" to "rows in spreadsheet."
Standard fields: From (address + display name), To (all addresses), Cc, Bcc, Date (normalized to ISO 8601), Subject, Body text (plain text extracted from HTML), Body HTML, and Attachments (filename, MIME type, size in bytes). With Starter+ plans, you can define custom fields like order_id, invoice_number, tracking_number, or any value in the email body using label matching or regex. Plus plan adds AI-assisted field detection that auto-suggests custom fields.
Yes. The Starter plan and above support bulk upload: drop multiple .eml or .msg files at once, and MailParse produces a single combined spreadsheet with one row per email. This is the bulk email parser workflow used by data analysts processing entire Gmail exports or Outlook PST backlog folders. The Pro plan supports unlimited emails per month.
Yes. Pro plan subscribers get API access. POST a raw email body, multipart/form-data file, or stored path to /api/v1/parse. Specify the output fields and format. Receive structured JSON back within milliseconds. The API supports authentication via API keys, handles all MIME encodings, and returns the same field set as the UI. Webhook delivery of parsed results is also available in the Pro plan.
MailParse uses TLS 1.3 for all uploads and transfers, and AES-256 encryption for data at rest. Uploaded files are stored temporarily for processing and auto-deleted after 30 days (configurable on Enterprise). Team access uses role-based permissions. Enterprise plans support custom data retention, DPA, and SSO/SAML. We do not train any AI models on your email content.
Yes, with the Plus plan. Attachment content extraction reads PDF and CSV attachments embedded in your emails and merges their content into the same row as the email headers. This is useful when the critical data (invoice amounts, order line items, shipping manifests) is inside an attachment rather than the email body.
EML files from Gmail (Google Takeout), Apple Mail, Thunderbird, Fastmail, and any RFC 822-compliant email client. MSG files from Microsoft Outlook on Windows and macOS, Exchange, and Office 365. Raw pasted email text (RFC 822 format, copied from "Show original" in Gmail or "View source" in Outlook). MailParse handles all standard MIME encodings: quoted-printable, base64, UTF-8, ISO-8859-1, and Windows-1252.
Gmail does not have a direct single-email export to .eml, but there are two approaches. For individual emails: open the message, click the three-dot menu in the top-right of the message, and select "Download message", this saves a .eml file directly. For bulk export: use Google Takeout (takeout.google.com), select Gmail, choose the labels you want to export, and download the archive. Gmail Takeout exports as .mbox format, which MailParse also accepts. For a specific label folder, Takeout is the fastest path to getting all messages as files for bulk parsing.
In Microsoft Outlook on Windows, select one or more emails, drag them to your desktop or a folder, and they save as .msg files automatically. You can also right-click a message and choose "Save As" to save as .msg. For .eml format from Outlook, you can save as "Text Only" (which is less complete) or use Outlook's built-in "Save as EML" option available in newer versions. On Outlook for Mac, drag messages to the Finder to export as .eml files. For Outlook 365 web, use the "Download" option in the message menu. MailParse accepts both .msg (preferred for Outlook) and .eml files.
Yes. MailParse handles forwarded emails and extracts the original message headers from the forwarded content. When you forward an email, the original message is typically embedded either as a quoted section in the body (inline quoting) or as a message/rfc822 MIME attachment. MailParse parses both structures and extracts the original From, Date, and Subject from the forwarded content. Email threads (chains of replies) are parsed as a single email, with the full body (including quoted replies) available in the body field. Thread parsing, extracting each individual reply as a separate row, is available on the Plus plan.
CSV (Comma-Separated Values) is a plain text format where each row is a line and fields are separated by commas. It opens in Excel, Google Sheets, LibreOffice Calc, and virtually any data tool, but has limitations: no column header formatting, no data types (all values are strings), and special characters like commas and newlines in field values require quoting that some tools handle inconsistently. Excel (.xlsx) output from MailParse includes proper column headers, formatted date columns (so Excel recognizes dates as dates, not text), and handles embedded commas and newlines in body text gracefully. For most ops and finance workflows, .xlsx is the better choice. For import into a database, CRM, or another tool that specifies CSV, use CSV.
Yes. MailParse handles emails in any language. Subject lines and display names encoded with RFC 2047 encoded words (the standard way non-ASCII characters are represented in email headers) are fully decoded, whether the language is Japanese, Arabic, Chinese, French, German, or any other. Email bodies in multi-byte character sets (UTF-8, Shift_JIS, EUC-KR, etc.) are correctly decoded and output as normalized UTF-8 in the spreadsheet. Custom field patterns can match non-ASCII text, and AI field detection (Plus) works across any language the underlying model supports.
No. MailParse does not use your uploaded emails or extracted data to train any AI model. Email content is processed in memory, results are returned to you, and files are stored temporarily for download purposes only (auto-deleted within 30 days by default, or on a custom schedule for Enterprise). We do not share, sell, or use your data for model training, research, or any purpose beyond delivering the parsing service you requested.
Python's standard library includes an email.parser module that can parse EML files and extract headers. A basic implementation is straightforward. However, production-quality email parsing quickly becomes complex: handling malformed MIME, decoding all character encodings correctly, extracting attachment metadata, parsing MSG files (which require a separate library like msg-extractor or extract-msg), normalizing date formats, and supporting custom field extraction all add significant development and maintenance burden. MailParse handles all of this as a maintained service, updated continuously to handle edge cases and new email client formats. For teams without a dedicated developer or for workflows where reliability matters more than custom control, MailParse is faster to implement and cheaper to maintain.
Yes. Many agencies, consultants, and integration engineers use MailParse on behalf of clients. The Pro plan's API allows you to integrate MailParse into a client workflow programmatically, the client's emails are sent to the parsing endpoint, and results are returned as JSON for further processing. For non-API use, simply download the parsed spreadsheet and deliver it to the client. There are no restrictions on using MailParse output in client deliverables. The Pro plan also supports multiple API keys, allowing you to isolate usage and keys per client project.
MailParse applies fault-tolerant parsing strategies for malformed or non-standard emails. Real-world emails, especially those generated by older marketing systems, custom-built notification engines, or non-compliant clients, frequently contain MIME structures that violate the RFC specification: missing boundary declarations, incorrect content-transfer-encoding values, truncated base64 data, or mixed encoding schemes. MailParse falls back gracefully: it extracts what it can, flags fields that could not be parsed, and always returns the raw header values so you can inspect and handle edge cases. In our testing, over 98% of real-world emails from major email clients parse completely without errors.
Custom fields work best when you start with a sample email and use the AI field detection (Plus) or manual label matching to identify the patterns. For recurring workflows (weekly order reconciliation, monthly invoice parsing), save your custom field set as a template so you do not need to reconfigure it each time. Use specific, descriptive field names, "order_id" rather than "id", "invoice_amount_usd" rather than "amount", so your output spreadsheet is self-documenting. For fields that appear in consistent formats (order numbers like #XXXX, invoice amounts like $XX.XX), regex patterns give the most reliable extraction. For fields buried in unstructured prose, AI detection is more effective than regex. Always test your field set on a handful of representative sample emails before running a large batch, this catches pattern mismatches early and ensures your full batch output is clean.
Yes. MailParse normalizes all date formats to ISO 8601 (YYYY-MM-DDTHH:MM:SSZ) in the output, regardless of the original locale or date convention in the email. European dates (DD/MM/YYYY), US dates (MM/DD/YYYY), Japanese dates, and RFC 2822 email dates with timezone offsets are all correctly parsed and normalized. Amount fields extracted via custom field matching or AI detection are returned as strings preserving the original formatting, so your downstream tool or analyst can apply locale-specific parsing as needed. Subject lines and display names in any Unicode character set or language are fully decoded.
Popular email parsing tools
Jump straight to the converter or integration built for your workflow.
EML to CSV converter
Turn exported .eml files into a clean CSV with one row per email, no scripts or desktop software.
EML to Excel converter
Export .eml messages to a formatted .xlsx spreadsheet with proper headers and dated columns.
Email to Excel
Parse any inbox or forwarded message into an Excel spreadsheet with our Microsoft Excel integration, one clean row per email with the fields you pick.
MSG to Excel converter
Parse Outlook .msg files into Excel without Outlook installed, including all recipient fields.
Extract invoice data from email
Pull invoice numbers, amounts, vendors, and dates from billing emails into a spreadsheet for AP.
Extract order data from email
Capture order confirmations into rows for reconciliation, reporting, and fulfillment tracking.
Email parser API
POST a raw email to one endpoint and get structured JSON back in milliseconds, with webhook delivery.
Email to Google Sheets
Send parsed email fields straight into a Google Sheet your team already works in, a Google Sheets integration with no Apps Script to maintain.
Best email parser comparison
See how the leading email parsing tools compare on setup, accuracy, formats, and pricing.
Mailparser alternative
A file-first alternative with no forwarding mailbox to configure and no parsing rules to maintain.
Zapier Email Parser alternative
Read attachments and shifting layouts that rule-based Zapier parsing misses, without a Zap per sender.
Parseur alternative
Handle varied email layouts without training a template per sender, with attachment data read for you.
Email parsing software
No-code email parsing software that reads named fields across varied layouts and attachments, then exports Excel, CSV, or JSON.
Parserr alternative
Skip the rule per format: name your fields once and read attachment data Parserr leaves in the body.
Ready to parse your first email?
Drop in a .eml or .msg file, or paste raw email text, and get clean structured data in under 2 seconds, free, no account required.
Parse Now, It's FreeNo sign-up for first 3 parses · Excel, CSV & JSON output · .eml & .msg supported