serpyco-rs
Decision gist · record as of 2026-08-14
Yes. serpyco-rs is actively maintained, has no known vulnerabilities, and offers strong performance for dataclass serialization. The MIT license is permissive. Install friction is moderate due to compiled wheels, but pre-built binaries are available for common platforms and Python versions 3.10–3.14. Choose it if you need fast, schema-validated serialization of dataclasses for APIs or data transformation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Medium install friction due to compiled wheels for multiple platforms and Python versions (3.10–3.14).
- Active maintenance with a recent release and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — MIT license (permissive); you can use this freely in commercial and private projects with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 41 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 448,312 downloads/mo, #6,602 on PyPI
Alternatives
Verify before relying
import dataclasses
import serpyco_rs
@dataclasses.dataclass
class Example:
name: str
num: int
serializer = serpyco_rs.Serializer(Example)
result = serializer.dump(Example(name="foo", num=2))
print(result) # {'name': 'foo', 'num': 2}- Whether custom encoder/decoder support covers all common field types needed for your schema.
- Performance characteristics on deeply nested or very large dataclass hierarchies.
- Compatibility with third-party dataclass libraries beyond standard library dataclasses.
What it is and what it does
serpyco-rs is a serialization library for Python dataclasses that converts objects to and from built-in Python types (dict, list, etc.) or directly to/from bytes via JSON or MessagePack codecs. It analyzes dataclass field types at serializer creation time and supports a broad range of standard types—lists, tuples, optionals, enums, UUIDs, dates, decimals, unions, and nested dataclasses—along with validation of input data during deserialization.
The library is implemented in Rust for speed and offers both a simple dict-based API (dump/load) and a codec-based API for direct bytes handling. It includes features like field aliasing, custom encoders/decoders, JSON Schema generation, and query-string deserialization with type coercion. The main use case is serializing objects for APIs, though it works for any object-to-type transformation task.
Use it for
- Serialize dataclass instances to JSON for REST API responses without intermediate dict conversion.
- Deserialize and validate incoming JSON payloads against a dataclass schema in a web framework.
- Convert Python objects to MessagePack for efficient binary storage or network transmission.
- Generate JSON Schema specifications from dataclass definitions for API documentation.
- Deserialize query string parameters into typed dataclass instances with automatic coercion.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
serpyco-rs is actively maintained, has no known vulnerabilities, and offers strong performance for dataclass serialization. The MIT license is permissive. Install friction is moderate due to compiled wheels, but pre-built binaries are available for common platforms and Python versions 3.10–3.14. Choose it if you need fast, schema-validated serialization of dataclasses for APIs or data transformation.
Install
serpyco-rs on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms and Python versions (3.10–3.14). Active maintenance with a recent release and no known vulnerabilities. Lightweight runtime dependencies (attributes-doc, typing-extensions) keep the footprint small.
Requires Python 3.10 or later.
License in practice
MIT license (permissive); you can use this freely in commercial and private projects with minimal restrictions.
Quickstart
import dataclasses
import serpyco_rs
@dataclasses.dataclass
class Example:
name: str
num: int
serializer = serpyco_rs.Serializer(Example)
result = serializer.dump(Example(name="foo", num=2))
print(result) # {'name': 'foo', 'num': 2}Verify before relying
- Whether custom encoder/decoder support covers all common field types needed for your schema.
- Performance characteristics on deeply nested or very large dataclass hierarchies.
- Compatibility with third-party dataclass libraries beyond standard library dataclasses.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesattributes-doctyping-extensions |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 448,312 / month, #6,602 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Rust |
Evidence: serpyco_rs-1.22.0-cp310-cp310-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl; serpyco_rs-1.22.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; serpyco_rs-1.22.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; serpyco_rs-1.22.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl; serpyco_rs-1.22.0-cp310-cp310-manylinux_2_17_ppc64.manylinux2014_ppc64.whl; serpyco_rs-1.22.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl; serpyco_rs-1.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; serpyco_rs-1.22.0-cp310-cp310-win_amd64.whl; serpyco_rs-1.22.0-cp311-cp311-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl; serpyco_rs-1.22.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; serpyco_rs-1.22.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; serpyco_rs-1.22.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl; serpyco_rs-1.22.0-cp311-cp311-manylinux_2_17_ppc64.manylinux2014_ppc64.whl; serpyco_rs-1.22.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl; serpyco_rs-1.22.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; serpyco_rs-1.22.0-cp311-cp311-win_amd64.whl; serpyco_rs-1.22.0-cp312-cp312-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl; serpyco_rs-1.22.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; serpyco_rs-1.22.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; serpyco_rs-1.22.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl
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See also databind.json · pyserde · databind · srsly · jsons · databind.core · cattrs · msgpack-python · djangorestframework-dataclasses · jax-dataclasses