Usage#

msgspec supports multiple serialization protocols, accessed through separate submodules:

Each supports a consistent interface, making it simple to switch between protocols as needed.

Encoding#

Each submodule has an encode method for encoding Python objects using the respective protocol.

>>> import msgspec

>>> # Encode as JSON
... msgspec.json.encode({"hello": "world"})
b'{"hello":"world"}'

>>> # Encode as msgpack
... msgspec.msgpack.encode({"hello": "world"})
b'\x81\xa5hello\xa5world'

Note that if you’re making multiple calls to encode, it’s more efficient to create an Encoder once and use the Encoder.encode method instead.

>>> import msgspec

>>> # Create a JSON encoder
... encoder = msgspec.json.Encoder()

>>> # Encode as JSON using the encoder
... encoder.encode({"hello": "world"})
b'{"hello":"world"}'

Decoding#

Each submodule has decode method for decoding messages using the respective protocol.

>>> import msgspec

>>> # Decode JSON
... msgspec.json.decode(b'{"hello":"world"}')
{'hello': 'world'}

>>> # Decode msgpack
... msgspec.msgpack.decode(b'\x81\xa5hello\xa5world')
{'hello': 'world'}

Note that if you’re making multiple calls to decode, it’s more efficient to create a Decoder once and use the Decoder.decode method instead.

>>> import msgspec

>>> # Create a JSON decoder
... decoder = msgspec.json.Decoder()

>>> # Decode JSON using the decoder
... decoder.decode(b'{"hello":"world"}')
{'hello': 'world'}

Typed Decoding#

msgspec optionally supports specifying the expected output types during decoding. This serves a few purposes:

  • Often serialized data has a fixed schema (e.g. a request handler in a REST api expects a certain JSON structure). Specifying the expected types allows msgspec to perform validation during decoding, with no added runtime cost.

  • Python has a much richer type system than serialization protocols like JSON or MessagePack. Specifying the output types lets msgspec decode messages into types other than the defaults described above (e.g. decoding JSON objects into a Struct instead of the default dict).

  • The type annotations used to describe the expected types are compatible with tools like mypy or pyright, providing excellent editor integration.

msgspec uses Python type annotations to describe the expected types. A wide variety of builtin types are supported.

Here we define a user schema as a Struct type. We then pass the type to decode via the type keyword argument:

>>> import msgspec

>>> class User(msgspec.Struct):
...     name: str
...     groups: set[str] = set()
...     email: str | None = None

>>> msgspec.json.decode(
...     b'{"name": "alice", "groups": ["admin", "engineering"]}',
...     type=User
... )
User(name='alice', groups={'admin', 'engineering'}, email=None)

If a message doesn’t match the expected type, an error is raised.

>>> msgspec.json.decode(
...     b'{"name": "bill", "groups": ["devops", 123]}',
...     type=User
... )
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
msgspec.ValidationError: Expected `str`, got `int` - at `$.groups[1]`

“Strict” vs “Lax” Mode#

Unlike some other libraries (e.g. pydantic), msgspec won’t perform any unsafe implicit conversion by default (“strict” mode). For example, if an integer is specified and a string is provided instead, an error is raised rather than attempting to cast the string to an int.

>>> msgspec.json.decode(b'[1, 2, "3"]', type=list[int])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
msgspec.ValidationError: Expected `int`, got `str` - at `$[2]`

For cases where you’d like a more lax set of conversion rules, you can pass strict=False to any decode function or Decoder class (“lax” mode). See Supported Types for information on how this affects individual types.

>>> msgspec.json.decode(b'[1, 2, "3"]', type=list[int], strict=False)
[1, 2, 3]