pydantic-function-models
Migrating v1 Pydantic ValidatedFunction to v2.
Decision gist · record as of 2026-08-14
Yes, if you are migrating from Pydantic v1's ValidatedFunction or need to model and validate function signatures as structured Pydantic models. The package is actively maintained, has no known vulnerabilities, and carries a permissive MIT license. Install friction is minimal. However, if you only need basic argument validation without signature introspection, Pydantic's validate_call decorator may be simpler.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; pydantic must be installed as the sole runtime dependency.
- Low friction: pure Python wheel with a single runtime dependency on pydantic.
- Actively maintained with a recent commit (2026-08-10) and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-04-16 (120 days) · last repo commit 2026-08-10 · 6 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 589,678 downloads/mo, #5,858 on PyPI
Alternatives
Verify before relying
from pydantic_function_models import ValidatedFunction
def add(a: int, b: int) -> int:
return a + b
vf = ValidatedFunction(add)
validated = vf.model.model_validate({"a": 1, "b": 2})
result = add(**validated.model_dump(exclude_unset=True))
print(result) # 3- Whether ValidatedFunction fully replicates Pydantic v1 behavior or has known gaps for complex signatures.
- Performance characteristics when validating functions with many parameters or nested type hints.
- Compatibility with Pydantic's validate_call decorator and how the two approaches differ in practice.
What it is and what it does
pydantic-function-models lets you wrap any Python function to enforce type validation on its arguments using Pydantic's validation engine. It builds an internal Pydantic model from the function's signature and type hints, then validates incoming arguments (positional or keyword) against that model before execution. The package was created to bridge the gap left by Pydantic v2's removal of ValidatedFunction from v1, offering a direct migration path for code that relied on that feature.
The library is designed for cases where you need to model and validate a function's full signature as a structured entity—distinct from Pydantic's validate_call decorator, which is simpler but less flexible for introspection. It includes safeguards against reserved parameter names that could conflict with internal validation logic, and raises clear Pydantic ValidationError messages when arguments don't match declared types.
Use it for
- Migrating Pydantic v1 code that used ValidatedFunction to v2 without rewriting validation logic.
- Building CLI tools or APIs that need to validate function arguments against strict type contracts before execution.
- Introspecting and modeling function signatures as Pydantic models for serialization or schema generation.
- Enforcing type safety in plugin systems or dynamic function dispatch where arguments come from untrusted sources.
- Creating wrapper layers that log or audit function calls with validated, structured argument data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are migrating from Pydantic v1's ValidatedFunction or need to model and validate function signatures as structured Pydantic models.
The package is actively maintained, has no known vulnerabilities, and carries a permissive MIT license. Install friction is minimal. However, if you only need basic argument validation without signature introspection, Pydantic's validate_call decorator may be simpler.
Install
pydantic-function-models on PyPI
Before you install
Low friction: pure Python wheel with a single runtime dependency on pydantic. Actively maintained with a recent commit (2026-08-10) and no known vulnerabilities.
Requires Python 3.10 or later; pydantic must be installed as the sole runtime dependency.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
from pydantic_function_models import ValidatedFunction
def add(a: int, b: int) -> int:
return a + b
vf = ValidatedFunction(add)
validated = vf.model.model_validate({"a": 1, "b": 2})
result = add(**validated.model_dump(exclude_unset=True))
print(result) # 3Verify before relying
- Whether ValidatedFunction fully replicates Pydantic v1 behavior or has known gaps for complex signatures.
- Performance characteristics when validating functions with many parameters or nested type hints.
- Compatibility with Pydantic's validate_call decorator and how the two approaches differ in practice.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepydantic |
| Maintenance | Actively maintained 120 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 589,678 / month, #5,858 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaFramework :: PydanticFramework :: Pydantic :: 2Intended Audience :: DevelopersNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Libraries |
Evidence: pydantic_function_models-0.1.12-py3-none-any.whl
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See also pydantic · django-pydantic-field · pydantic-compat · pydantic_core · backcall · drf-pydantic · jsonschema-pydantic-converter · python-flirt · Flask-Pydantic · pydantic-xml