inboxpattern 2025.12.21181804

A new package that helps users and organizations analyze and categorize email account usage patterns. The package takes user-submitted text input describing their email management habits and returns a

inboxpattern

inboxpattern is a lightweight Python package that helps users and organizations analyze and categorize email account usage patterns.
Give it a brief text describing your email habits – it will return a structured reply that outlines:

• How many email accounts you use
• The purpose of each account
• Any challenges you face managing them

The output is a list of strings that can be fed straight into a workflow, shared in dashboards, or used for tooling that reduces inbox clutter.

Author: Eugene Evstafev ([e-mail])
GitHub owner: chigwell

Quick Start

pip install inboxpattern

Basic usage

from inboxpattern import inboxpattern

user_input = (
    "I maintain three email addresses: a personal Gmail, a work Outlook account, "
    "and a project-specific ProtonMail. I often forget which account to use for "
    "which purpose, and I sometimes receive spam in my personal address."
)

response = inboxpattern(user_input)
print(response)
# Example output: [
#   "Accounts: 3",
#   "Personal: Gmail",
#   "Work: Outlook",
#   "Project: ProtonMail",
#   "Challenges: Misattributed emails, spam in personal inbox"
# ]

Using a different LLM

inboxpattern ships with ChatLLM7 from the langchain_llm7 package by default.
If you already have a LangChain LLM provider (OpenAI, Anthropic, Google, etc.), you can pass it in:

OpenAI

from langchain_openai import ChatOpenAI
from inboxpattern import inboxpattern

llm = ChatOpenAI()      # your own OpenAI key already configured
response = inboxpattern(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from inboxpattern import inboxpattern

llm = ChatAnthropic()
response = inboxpattern(user_input, llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from inboxpattern import inboxpattern

llm = ChatGoogleGenerativeAI()
response = inboxpattern(user_input, llm=llm)

Optional API key

The free tier of LLM7 comes with generous limits that are usually enough for most use‑cases.
If you need higher throughput, obtain a key at https://token.llm7.io/ and provide it:

export LLM7_API_KEY="your_llm7_token"    # or
inboxpattern(user_input, api_key="your_llm7_token")

Parameters

Parameter	Type	Description
user_input	str	Text describing your email‑management habits.
llm	Optional[BaseChatModel]	A LangChain LLM instance to use; defaults to ChatLLM7.
api_key	Optional[str]	LLM7 API key; if omitted, the library will look for the LLM7_API_KEY environment variable or default to "None".

Development & Issues

If you encounter bugs or want to request a feature, please open an issue in the repository:

https://github.com/chigwell/inboxpattern/issues

Happy coding, and may your inboxes stay tidy!