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This PR contains the following updates:

Package Change Age Confidence
fastapi (changelog) >=0.136.3 → >=0.141.1 age confidence
mypy (changelog) >=2.1.0 → >=2.3.0 age confidence
numpy (changelog) >=2.4.6 → >=2.5.1 age confidence
redis (changelog) >=8.0.0 → >=8.1.0 age confidence
ruff (source, changelog) >=0.15.16 → >=0.16.1 age confidence
uvicorn (changelog) >=0.49.0 → >=0.52.1 age confidence

Release Notes

fastapi/fastapi (fastapi)

v0.141.1

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Fixes
  • 🐛 Fix support for background tasks and headers from dependencies in app.frontend(). PR #​16105 by @​tiangolo.
Docs

v0.141.0

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Features
  • ✨ Add app.frontend(check_dir="auto"), to make local development more convenient with fastapi dev. PR #​16102 by @​tiangolo.

v0.140.13

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Fixes
Docs

v0.140.12

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Fixes

v0.140.11

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v0.140.10

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Fixes
Internal

v0.140.9

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Fixes
  • 🐛 Fix exclude_defaults not propagated to dict keys and values in jsonable_encoder. PR #​16043 by @​MBGrao.
Internal

v0.140.8

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Fixes

v0.140.7

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Refactors
Internal

v0.140.6

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Refactors
  • ⚡️ Avoid flattening dependencies for request parameters, mainly for OpenAPI. PR #​16073 by @​tiangolo.

v0.140.5

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Refactors

v0.140.4

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v0.140.3

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Refactors

v0.140.2

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Refactors
Internal

v0.140.1

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Refactors
  • ♻️ Update the lru_cache limit for dependencies to account for large apps. PR #​16062 by @​tiangolo.

v0.140.0

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Refactors
Docs
Internal

v0.139.2

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Fixes
  • 🐛 Refactor router route building to make it thread-safe, mainly relevant for tests running in parallel threads (uncommon). PR #​16013 by @​tiangolo.

v0.139.1

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Fixes
Docs
  • 📝 Fix topic repository list not being displayed and skip_users not being applied. PR #​15995 by @​YuriiMotov.
Translations
Internal

v0.139.0

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Features
  • ✨ Support dependencies in app.frontend(), e.g. for automatic cookie authentication for the frontend. PR #​15908 by @​tiangolo.
Translations
Internal

v0.138.2

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Refactors
  • ♻️ Make app.frontend() return 404 for methods other than GET or HEAD with no static file matches. PR #​15863 by @​tiangolo.
Internal

v0.138.1

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Refactors
Internal

v0.138.0

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Features
  • ✨ Add support for app.frontend("/", directory="dist") and router.frontend("/", directory="dist"). PR #​15800 by @​tiangolo.
Docs
Translations
Internal

v0.137.2

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Features
  • ✨ Add iter_route_contexts() for advanced use cases that used to use router.routes (e.g. Jupyverse). PR #​15785 by @​tiangolo.
Translations
Internal

v0.137.1

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Fixes

v0.137.0

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Breaking Changes

Unblocks ✨ SO MANY THINGS ✨

Before this, router.include_router(other_router) would take each path operation from other_router and "clone" it, or recreate it from scratch.

This would mean that in the end there was only one top level router, part of the app.

The way it is structured here is that there are a few additional classes to handle intermediate metadata for router and route inclusion. That way the information of "router X includes Y and Y includes Z" is stored somewhere, without affecting (recreating / clonning) the final route.

Non Objectives

Dependencies for 404: previously I intended to support dependencies that would be executed even for 404, but that would conflict with the fact that a router could not find a match, but the next router did find a match. Executing dependencies in the router that did not find a match would not make sense, they could consume the request, body, etc. This original idea was discarded.

Specific Breaking Changes

Now router.routes is no longer a plain list of APIRoute objects, it can contain these intermediate objects that can contain additional routers, forming a tree.

Any logic that depended on iterating on the router.routes directly would be affected, that logic cannot expect to be able to extract data from a plain list of routes, as it's no longer a plain list but a tree.

Additionally, any logic that iterated on router.routes to modify them would now also see these new objects, and would not see all the routes in the app.

router.routes should be considered an internal implementation detail, only passed around to the FastAPI functions that need it.

Features
  • Adding routes (path operations) after a router is included now works, they are reflected as they are not copied.
  • Including subrouter in mainrouter can be done before adding routes (path operations) to subrouter, because now the the entire object is stored instead of copying the routes.
  • As routes are not copied, in some cases that might save some memory.
Alpha Features

This is not documented yet, so it's not officially supported yet and could change in the future.

But, as APIRoute and APIRouter instances are now preserved, they could be customized.

APIRouter has two new methods, .matches() and .handle(), counterpart to the existing ones in APIRoute. With this a router could customize how it matches and handles requests. For example, it could match only requests that include some specific header, for example for handling versions in headers.

Still, for now, consider this very experimental and potentially changing and breaking in the future.

Future Features Enabled
  • Custom APIRoute subclasses (undocumented, but alraedy works as desccribed above)
  • Custom APIRouter subclasses (undocumented, but already works as described above)
  • Dependencies per router
  • Exception handlers per router
  • Middleware per router
  • Other features planned
Docs
Translations
Internal
python/mypy (mypy)

v2.3.0

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v2.2.0

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numpy/numpy (numpy)

v2.5.1

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v2.5.0: (June 21, 2026)

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NumPy 2.5.0 Release Notes

Numpy 2.5.0 is a transitional release. It drops support for Python 3.11,
marking the end of distutils, and expires a large number of deprecations made
in the 2.0.x release. It also improves free threading and brings sorting into
compliance with the array-api standard with the addition of descending sorts.
There is also a fair amount of preparation for Python 3.15, which will be
supported starting with the first rc.

This release supports Python versions 3.12-3.14.

Highlights

  • Distutils has been removed,
  • Many expired deprecations, see below,
  • Many new deprecations, see below,
  • Many static typing improvements.
  • Improved support for free threading,
  • Support for descending sorts,

See New Features below for other additions.

Deprecations

  • numpy.char.chararray is deprecated. Use an ndarray with a string or bytes dtype instead.

    (gh-30605)

  • numpy.take now correctly checks if the result can be cast to the provided
    out=out under the same-kind rule. A DeprecationWarning is given now
    when this check fails. Previously, take incorrectly checked if out
    could be cast to the result (the wrong direction). This deprecation also
    affects compress and possibly other functions. (Future versions of NumPy
    may tighten the casting check further.)

    (gh-30615)

  • The numpy.char.[as]array functions are deprecated. Use an
    numpy.[as]array with a string or bytes dtype instead.

    (gh-30802)

  • Setting the dtype attribute is deprecated because mutating an array is unsafe
    if an array is shared, especially by multiple threads. As an alternative,
    you can create a view with a new dtype via array.view(dtype=new_dtype).

    (gh-29244)

  • Setting the shape attribute is deprecated because mutating an array is
    unsafe if an array is shared, especially by multiple threads. As an
    alternative, you can create a new view via np.reshape or
    np.ndarray.reshape. For example: x = np.arange(15); x = np.reshape(x, (3, 5)).
    To ensure no copy is made from the data, one can use np.reshape(..., copy=False).

    While setting the shape on an array is discouraged, for cases where it is
    difficult to work around, e.g., in __array_finalize__, it is possible
    with the private method np.ndarray._set_shape.

    (gh-29536)

  • Using the generic unit in numpy.timedelta64 is deprecated since this
    can lead to unexpected behavior such as non-transitive comparison, see
    gh-28287 for details. As
    an alternative, specify an explicit unit such as 's' (seconds) or 'D'
    (days) when constructing numpy.timedelta64. Due to this change, operations
    that implicitly rely on the generic unit are also deprecated. For
    example:

    arr = np.array([1, 2, 3], dtype="m8[s]")

1 is implicitly converted to generic timedelta64

  arr + 1

(gh-29619)

  • Resizing a Numpy array in place is deprecated since mutating an array is
    unsafe if an array is shared, especially by multiple threads. As an
    alternative, you can create a resized array via np.resize.

    (gh-30181)

  • numpy.fix is deprecated, use numpy.trunc instead. It is faster and
    follows the Array API standard. Both functions provide identical
    functionality: rounding array elements towards zero.

    (gh-30644)

  • numpy.ma.round_ is deprecated. numpy.ma.round can be used as a
    replacement.

    (gh-30738)

  • numpy.typename is deprecated because the names returned by it were
    outdated and inconsistent. numpy.dtype.name can be used as a
    replacement.

    (gh-30774)

  • Inputs other than integers are deprecated for numpy.triu_indices and
    numpy.tril_indices. Non-integer values for the M, k and N
    parameters of numpy.tri are deprecated. Non-integer values for the k
    parameter of both numpy.tril_indices_from and numpy.triu_indices_from
    are deprecated.

    (gh-30869)

  • Deprecations in custom dtype property and __array_finalize__.

    Previously arr.view(dtype=new_dtype) called arr.dtype = new_dtype
    also for subclasses, i.e., the attribute setting. That path is now
    deprecated and refined, meaning that even subclasses that do not see this
    DeprecationWarning may wish to update their code.

    A subclass that does any dtype specific logic (i.e. verifying the dtype
    in __array_finalize__ or has a dtype property) should now:

    • Set _set_dtype = None in which case arr.view(dtype=new_dtype)
      will call __array_finalize__ with the new dtype, ensuring that
      any validation __array_finalize__ will run is done.
    • Or, for a quick fix, define _set_dtype as a function (calling
      ndarray._set_dtype() to avoid DeprecationWarnings.
      (Future versions might migrate towards the _set_dtype = None path.)

    Ideally, follow NumPy's deprecation to prevent dtype mutation by users.
    The use of ndarray._set_dtype() may be necessary for some subclass
    finalization patterns, but should otherwise be avoided.

    (gh-31293)

Expired deprecations

  • numpy.distutils has been removed

    (gh-30340)

  • Passing None as dtype to np.finfo will now raise a TypeError
    (deprecated since 1.25)

    (gh-30460)

  • numpy.cross no longer supports 2-dimensional vectors.
    (Deprecated since 2.0)

    (gh-30461)

  • numpy._core.numerictypes.maximum_sctype has been removed.
    (deprecated since 2.0)

    (gh-30462)

  • numpy.row_stack has been removed in favor of numpy.vstack.
    (deprecated since 2.0)

    (gh-30463)

  • get_array_wrap has been removed.
    (deprecated since 2.0)

    (gh-30463)

  • recfromtxt and recfromcsv have been removed from numpy.lib._npyio
    in favor of numpy.genfromtxt.
    (deprecated since 2.0)

    (gh-30467)

  • The numpy.chararray re-export of numpy.char.chararray has been removed.
    (deprecated since 2.0)

    (gh-30604)

  • bincount now raises a TypeError for non-integer inputs.
    (deprecated since 2.1)

    (gh-30610)

  • The numpy.lib.math alias for the standard library math module has
    been removed.
    (deprecated since 1.25)

    (gh-30612)

  • Data type alias 'a' was removed in favor of 'S'.
    (deprecated since 2.0)

    (gh-30613)

  • _add_newdoc_ufunc(ufunc, newdoc) has been removed in favor of
    ufunc.__doc__ = newdoc.
    (deprecated since 2.2)

    (gh-30614)

Compatibility notes

linalg.eig and linalg.eigvals now always return complex arrays

Previously, the return values depended on whether the eigenvalues happen to lie
on the real line (which, for a general, non-symmetric matrix, is not
guaranteed).

This change makes consistent what was a value-dependent result. To retain the
previous behavior, do:

w = eigvals(a)
if np.any(w.imag == 0):  # this is what NumPy used to do
    w = w.real

If your matrix is symmetrix/hermitian, use eigh and eigvalsh instead of
eig and eigvals. These are guaranteed to return real values. A common
case is covariance matrices, which are symmetric and positive definite by
construction.

(gh-30411)

MSVC support

NumPy now requires minimum MSVC 19.35 toolchain version on Windows platforms.
This corresponds to Visual Studio 2022 version 17.5 Preview 2 or newer.

(gh-30489)

Cython support

NumPy's Cython headers (accessed via cimport numpy) now require Cython 3.0
or newer to build. If you try to compile a project that depends on NumPy's
Cython headers using Cython 0.29 or older, you will see a message like this:

Error compiling Cython file:
------------------------------------------------------------
...

versions.


#

See init.cyt

✂ Note

PR body was truncated to here.

@renovate renovate Bot added dependencies Pull requests that update a dependency file renovate labels Jul 25, 2026
@renovate
renovate Bot force-pushed the renovate/python-packages branch 9 times, most recently from 2beecc2 to 5e31098 Compare August 4, 2026 21:42
@renovate
renovate Bot force-pushed the renovate/python-packages branch from 5e31098 to 1690df7 Compare August 5, 2026 02:05
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