What is auto-fastapi?

55/100
Trust Score (C)
⚠️ Use Caution

auto-fastapi is a pypi that A pythonic functional way to construct FastAPI applications be declaring endpoints in separation of their functional definition, enabeling to separate, replicate, and reuse functions in different APIs. It has a Nerq Trust Score of 55/100 (C). 0 GitHub stars. Published by Shahaf Frank-Shapir. Last analyzed August 2026.

Why This Score

Trust & Safety Overview

55
TRUST SCORE
C
GRADE
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STARS
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DOWNLOADS

What auto-fastapi Does

auto-fastapi is a pypi in the pypi category. A pythonic functional way to construct FastAPI applications be declaring endpoints in separation of their functional definition, enabeling to separate, replicate, and reuse functions in different APIs at the same time, and also run multiple of them.. It is published by Shahaf Frank-Shapir and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.

Who Should Use auto-fastapi

auto-fastapi is suitable for evaluation and non-critical use. Review the trust score breakdown before using in production.

Details

AuthorShahaf Frank-Shapir
Categorypypi
LicenseNot specified
Typepypi
SourceView on GitHub
Security Score0/100
Activity Score0/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=auto-fastapi

Setup guide · Full safety report · Production review · Is it safe?

Frequently Asked Questions

What is auto-fastapi used for?
auto-fastapi is a pypi tool. A pythonic functional way to construct FastAPI applications be declaring endpoints in separation of their functional definition, enabeling to separate, replicate, and reuse functions in different APIs.
Is auto-fastapi free?
License: Check project page. auto-fastapi has 0 GitHub stars.
Is auto-fastapi safe?
auto-fastapi has a Nerq Trust Score of 55/100 (C). Use with caution.
What are alternatives to auto-fastapi?
Top alternatives: . See full comparison.

Last updated August 2026. Trust scores based on automated analysis of public data.

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