# pydantic-core
[](https://github.com/pydantic/pydantic-core/actions?query=event%3Apush+branch%3Amain+workflow%3Aci)
[](https://codecov.io/gh/pydantic/pydantic-core)
[](https://pypi.python.org/pypi/pydantic-core)
[](https://github.com/pydantic/pydantic-core)
[](https://github.com/pydantic/pydantic-core/blob/main/LICENSE)
This package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization.
Pydantic-core is currently around 17x faster than pydantic V1.
See [`tests/benchmarks/`](./tests/benchmarks/) for details.
## Example of direct usage
_NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core._
```py
from pydantic_core import SchemaValidator, ValidationError
v = SchemaValidator(
{
'type': 'typed-dict',
'fields': {
'name': {
'type': 'typed-dict-field',
'schema': {
'type': 'str',
},
},
'age': {
'type': 'typed-dict-field',
'schema': {
'type': 'int',
'ge': 18,
},
},
'is_developer': {
'type': 'typed-dict-field',
'schema': {
'type': 'default',
'schema': {'type': 'bool'},
'default': True,
},
},
},
}
)
r1 = v.validate_python({'name': 'Samuel', 'age': 35})
assert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}
# pydantic-core can also validate JSON directly
r2 = v.validate_json('{"name": "Samuel", "age": 35}')
assert r1 == r2
try:
v.validate_python({'name': 'Samuel', 'age': 11})
except ValidationError as e:
print(e)
"""
1 validation error for model
age
Input should be greater than or equal to 18
[type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]
"""
```
## Getting Started
### Prerequisites
You'll need:
1. **[Rust](https://rustup.rs/)** - Rust stable (or nightly for coverage)
2. **[uv](https://docs.astral.sh/uv/getting-started/installation/)** - Fast Python package manager (will install Python 3.9+ automatically)
3. **[git](https://git-scm.com/)** - For version control
4. **[make](https://www.gnu.org/software/make/)** - For running development commands (or use `nmake` on Windows)
### Quick Start
```bash
# Clone the repository (or from your fork)
git clone git@github.com:pydantic/pydantic-core.git
cd pydantic-core
# Install all dependencies using uv, setup pre-commit hooks, and build the development version
make install
```
Verify your installation by running:
```bash
make
```
This runs a full development cycle: formatting, building, linting, and testing
### Development Commands
Run `make help` to see all available commands, or use these common ones:
```bash
make build-dev # to build the package during development
make build-prod # to perform an optimised build for benchmarking
make test # to run the tests
make testcov # to run the tests and generate a coverage report
make lint # to run the linter
make format # to format python and rust code
make all # to run to run build-dev + format + lint + test
```
### Useful Resources
* [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) - Python API types
* [`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) - Core schema definitions
* [`tests/`](./tests) - Comprehensive usage examples
## Profiling
It's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (Tested on Linux.) You can install this with `cargo install flamegraph`.
Run `make build-profiling` to install a release build with debugging symbols included (needed for profiling).
Once that is built, you can profile pytest benchmarks with (e.g.):
```bash
flamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable
```
The `flamegraph` command will produce an interactive SVG at `flamegraph.svg`.
## Releasing
1. Bump package version locally. Do not just edit `Cargo.toml` on Github, you need both `Cargo.toml` and `Cargo.lock` to be updated.
2. Make a PR for the version bump and merge it.
3. Go to https://github.com/pydantic/pydantic-core/releases and click "Draft a new release"
4. In the "Choose a tag" dropdown enter the new tag `v<the.new.version>` and select "Create new tag on publish" when the option appears.
5. Enter the release title in the form "v<the.new.version> <YYYY-MM-DD>"
6. Click Generate release notes button
7. Click Publish release
8. Go to https://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.
9. Go to https://pypi.org/project/pydantic-core/ and ensure that the latest release is published.
10. Done 🎉
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"author_email": "Samuel Colvin <s@muelcolvin.com>, Adrian Garcia Badaracco <1755071+adriangb@users.noreply.github.com>, David Montague <david@pydantic.dev>, David Hewitt <mail@davidhewitt.dev>, Sydney Runkle <sydneymarierunkle@gmail.com>, Victorien Plot <contact@vctrn.dev>",
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"description": "# pydantic-core\n\n[](https://github.com/pydantic/pydantic-core/actions?query=event%3Apush+branch%3Amain+workflow%3Aci)\n[](https://codecov.io/gh/pydantic/pydantic-core)\n[](https://pypi.python.org/pypi/pydantic-core)\n[](https://github.com/pydantic/pydantic-core)\n[](https://github.com/pydantic/pydantic-core/blob/main/LICENSE)\n\nThis package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization.\n\nPydantic-core is currently around 17x faster than pydantic V1.\nSee [`tests/benchmarks/`](./tests/benchmarks/) for details.\n\n## Example of direct usage\n\n_NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core._\n\n```py\nfrom pydantic_core import SchemaValidator, ValidationError\n\n\nv = SchemaValidator(\n {\n 'type': 'typed-dict',\n 'fields': {\n 'name': {\n 'type': 'typed-dict-field',\n 'schema': {\n 'type': 'str',\n },\n },\n 'age': {\n 'type': 'typed-dict-field',\n 'schema': {\n 'type': 'int',\n 'ge': 18,\n },\n },\n 'is_developer': {\n 'type': 'typed-dict-field',\n 'schema': {\n 'type': 'default',\n 'schema': {'type': 'bool'},\n 'default': True,\n },\n },\n },\n }\n)\n\nr1 = v.validate_python({'name': 'Samuel', 'age': 35})\nassert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}\n\n# pydantic-core can also validate JSON directly\nr2 = v.validate_json('{\"name\": \"Samuel\", \"age\": 35}')\nassert r1 == r2\n\ntry:\n v.validate_python({'name': 'Samuel', 'age': 11})\nexcept ValidationError as e:\n print(e)\n \"\"\"\n 1 validation error for model\n age\n Input should be greater than or equal to 18\n [type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]\n \"\"\"\n```\n\n## Getting Started\n\n### Prerequisites\n\nYou'll need:\n1. **[Rust](https://rustup.rs/)** - Rust stable (or nightly for coverage)\n2. **[uv](https://docs.astral.sh/uv/getting-started/installation/)** - Fast Python package manager (will install Python 3.9+ automatically)\n3. **[git](https://git-scm.com/)** - For version control\n4. **[make](https://www.gnu.org/software/make/)** - For running development commands (or use `nmake` on Windows)\n\n### Quick Start\n\n```bash\n# Clone the repository (or from your fork)\ngit clone git@github.com:pydantic/pydantic-core.git\ncd pydantic-core\n\n# Install all dependencies using uv, setup pre-commit hooks, and build the development version\nmake install\n```\n\nVerify your installation by running:\n\n```bash\nmake\n```\n\nThis runs a full development cycle: formatting, building, linting, and testing\n\n### Development Commands\n\nRun `make help` to see all available commands, or use these common ones:\n\n```bash\nmake build-dev # to build the package during development\nmake build-prod # to perform an optimised build for benchmarking\nmake test # to run the tests\nmake testcov # to run the tests and generate a coverage report\nmake lint # to run the linter\nmake format # to format python and rust code\nmake all # to run to run build-dev + format + lint + test\n```\n\n### Useful Resources\n\n* [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) - Python API types\n* [`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) - Core schema definitions\n* [`tests/`](./tests) - Comprehensive usage examples\n\n## Profiling\n\nIt's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (Tested on Linux.) You can install this with `cargo install flamegraph`.\n\nRun `make build-profiling` to install a release build with debugging symbols included (needed for profiling).\n\nOnce that is built, you can profile pytest benchmarks with (e.g.):\n\n```bash\nflamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable\n```\nThe `flamegraph` command will produce an interactive SVG at `flamegraph.svg`.\n\n## Releasing\n\n1. Bump package version locally. Do not just edit `Cargo.toml` on Github, you need both `Cargo.toml` and `Cargo.lock` to be updated.\n2. Make a PR for the version bump and merge it.\n3. Go to https://github.com/pydantic/pydantic-core/releases and click \"Draft a new release\"\n4. In the \"Choose a tag\" dropdown enter the new tag `v<the.new.version>` and select \"Create new tag on publish\" when the option appears.\n5. Enter the release title in the form \"v<the.new.version> <YYYY-MM-DD>\"\n6. Click Generate release notes button\n7. Click Publish release\n8. Go to https://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.\n9. Go to https://pypi.org/project/pydantic-core/ and ensure that the latest release is published.\n10. Done \ud83c\udf89\n\n",
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