maskflow

Mask PII before it reaches an LLM. Unmask the response. Works with any provider.

from maskflow import mask_and_call

def call_claude(masked_prompt: str) -> str:
    return anthropic_client.messages.create(
        model="claude-sonnet-5",
        max_tokens=1024,
        messages=[{"role": "user", "content": masked_prompt}],
    ).content[0].text

response = mask_and_call(
    "Hi, I'm Jane Doe (jane@example.com). My order shipped to 123 Main St but never arrived.",
    call_claude,
)
# Claude only ever sees "Hi, I'm <PERSON_NAME_1> (<EMAIL_1>). My order shipped to
# <ADDRESS_1> but never arrived." -- response comes back with the real values restored.

Install

pip install maskflow-sdk

Why this shape

mask_and_call takes a plain function, not a specific provider's client. You write the one line that actually calls your LLM (Claude, OpenAI, Gemini, a local model, anything) -- maskflow never parses or depends on any provider's SDK, so it doesn't break when a provider changes their API and works with providers it's never heard of.

response = mask_and_call(prompt, lambda masked: my_llm_client.generate(masked))

Lower-level API

For more control than the wrapper gives, mask/unmask are available directly and are pure, stateless functions -- no files, no database. Persisting the mapping between calls is your responsibility.

from maskflow import mask, unmask

result = mask("Email me at alice@example.com.")
result.masked_text  # "Email me at <EMAIL_1>."
result.mapping       # {"<EMAIL_1>": "alice@example.com"}

unmask(result.masked_text, result.mapping)  # original text, restored

What gets detected

Email, phone, SSN, credit card, IP address, AWS access key, API key / generic secret, JWT, IBAN, street address, person name, date of birth -- via regex + structural validation (Luhn, mod-97, etc.) plus spaCy NER for names and dates, with keyword-context confidence boosting. See maskflow-core for detection internals.

Tests

uv run pytest

Key dates

PyPI data
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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for maskflow from gravatar.com maskflow

License

MIT

Requires

Python >=3.9

Provides Extra

dev

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