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Email preprocessing for LLMs

Project description

langmail

Email preprocessing for LLMs. Fast, typed, Rust-powered.

PyPI CI license

Emails are messy — nested MIME parts, quoted reply chains, HTML cruft, signatures, forwarded headers. LLMs don't need any of that. langmail strips it all away and gives you clean, structured text optimized for language model consumption.

from langmail import preprocess_string

result = preprocess_string(raw_email)

print(result.body)
# → "Hi Alice! Great to hear from you."
# (no quoted replies, no signature, no HTML noise)

print(result.from_address)
# → Address(name='Bob', email='bob@example.com')

Why langmail?

  • Built for LLMs — minimizes token waste by stripping quoted replies, signatures, and HTML noise
  • Fast — Rust core with zero-copy parsing via mail-parser
  • Typed — full type stubs, every field documented
  • Multilingual — detects quote patterns in English, German, French, and Spanish
  • One functionpreprocess() does everything; options available when you need them

Install

pip install langmail

Requires Python 3.9 or later.

Prebuilt wheels for Linux (x64, arm64), macOS (x64, arm64), and Windows (x64). No Rust toolchain needed.

Usage

Basic

from langmail import preprocess, preprocess_string

# From raw .eml bytes
with open("message.eml", "rb") as f:
    result = preprocess(f.read())

# Or from a string
result = preprocess_string(raw_email_string)

With options

from langmail import preprocess_with_options, PreprocessOptions

options = PreprocessOptions(
    strip_quotes=True,       # Remove quoted replies (default: True)
    strip_signature=True,    # Remove email signatures (default: True)
    max_body_length=4000,    # Truncate body to N chars (default: 0 = no limit)
)

result = preprocess_with_options(raw, options)

Format for LLM prompts

to_llm_context converts a ProcessedEmail into a compact, deterministic plain-text block ready to paste into an LLM prompt:

from langmail import preprocess, to_llm_context

result = preprocess(raw)
print(to_llm_context(result))
# FROM: Bob <bob@example.com>
# TO: Alice <alice@example.com>
# SUBJECT: Re: Project update
# DATE: 2024-01-15T10:30:00Z
# CONTENT:
# Hi Alice! Great to hear from you.

Missing fields (no from, empty to, etc.) are simply omitted. The CONTENT: line is always present.

Output structure

class ProcessedEmail:
    body: str                           # Clean text, ready for your LLM
    subject: str | None
    from_address: Address | None        # Address(name='Bob', email='bob@example.com')
    to: list[Address]
    cc: list[Address]
    date: str | None                    # ISO 8601
    rfc_message_id: str | None          # RFC 2822 Message-ID header
    in_reply_to: list[str] | None       # Threading
    references: list[str] | None        # Threading
    signature: str | None               # Extracted signature (if found)
    raw_body_length: int                # Before cleaning
    clean_body_length: int              # After cleaning
    primary_cta: CallToAction | None    # Primary CTA from HTML emails

class CallToAction:
    url: str                            # Action URL
    text: str                           # Button/link label
    confidence: float                   # 0.0–1.0

Error handling

preprocess, preprocess_with_options, and preprocess_string raise ParseError (a ValueError subclass) if the input cannot be parsed as a valid RFC 5322 message:

from langmail import preprocess, ParseError

try:
    result = preprocess(raw)
except ParseError as e:
    print(e)  # "Failed to parse email message"

to_llm_context never raises.

What it does

Step Before After
MIME parsing Raw RFC 5322 bytes Structured parts
HTML to text <p>Hello <b>world</b></p> Hello world
Quote stripping Gmail/Outlook/Apple Mail quoted replies Just the new message
Signature removal -- \nJohn Doe\nCEO, Acme Corp\n555-0123 Body without signature
Whitespace cleanup Excessive blank lines, trailing spaces Clean, normalized text

Supported quote patterns

  • Gmail: On <date>, <name> <email> wrote:
  • Outlook: -----Original Message----- and From: ... Sent: ...
  • Apple Mail: On <date>, at <time>, <name> wrote:
  • Forwarded: -------- Forwarded Message --------
  • German: Am <date> schrieb <name>:
  • French: Le <date>, <name> a ecrit :
  • Spanish: El <date>, <name> escribio:
  • Generic: > prefixed quote lines

License

MIT OR Apache-2.0


Built by the team behind Marbles.

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