docs/guide/python/cross-language.md

title: Cross-Language Serialization sidebar_position: 10 id: cross_language license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

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pyfory supports cross-language object graph serialization, allowing you to serialize data in Python and deserialize it in Java, Go, Rust, or other supported languages.

Enable Cross-Language Mode

To use xlang mode, create Fory with xlang=True:

import pyfory
fory = pyfory.Fory(xlang=True, ref=False, strict=True)

Cross-Language Example

Python (Serializer)

import pyfory
from dataclasses import dataclass

# Cross-language mode for interoperability
f = pyfory.Fory(xlang=True, ref=True)

# Register type for cross-language compatibility
@dataclass
class Person:
    name: str
    age: pyfory.int32

f.register(Person, typename="example.Person")

person = Person("Charlie", 35)
binary_data = f.serialize(person)
# binary_data can now be sent to Java, Go, etc.

Java (Deserializer)

import org.apache.fory.*;

public class Person {
    public String name;
    public int age;
}

Fory fory = Fory.builder()
    .withLanguage(Language.XLANG)
    .withRefTracking(true)
    .build();

fory.register(Person.class, "example.Person");
Person person = (Person) fory.deserialize(binaryData);

Rust (Deserializer)

use fory::Fory;
use fory::ForyObject;

#[derive(ForyObject)]
struct Person {
    name: String,
    age: i32,
}

let mut fory = Fory::builder()
    .compatible(true)
    .xlang(true).build();

fory.register_by_namespace::<Person>("example", "Person");
let person: Person = fory.deserialize(&binary_data)?;

Type Annotations for Cross-Language

Use pyfory type annotations for explicit cross-language type mapping:

from dataclasses import dataclass
import pyfory

@dataclass
class TypedData:
    int_value: pyfory.int32      # 32-bit integer
    long_value: pyfory.int64     # 64-bit integer
    float_value: pyfory.float32  # 32-bit float
    double_value: pyfory.float64 # 64-bit float

Reduced-Precision Types

pyfory.serialization exports Cython-only carrier types for xlang reduced-precision values:

float16 and float16array
bfloat16 and bfloat16array

These names are compiled into the pyfory.serialization extension and re-exported from pyfory. There is no pure-Python fallback module for them.

The scalar wrappers behave like reduced-precision numeric value types. They support arithmetic and ordering with Python numeric operands, and each operation quantizes the result back to the wrapper's own format (pyfory.float16 or pyfory.bfloat16).

The array wrappers are value-oriented public APIs. Construct them from Python numeric values with pyfory.float16array([...]), pyfory.float16array.from_values([...]), pyfory.bfloat16array([...]), or pyfory.bfloat16array.from_values([...]). Use from_buffer(...) and to_buffer() only when you already need packed little-endian uint16 storage and want the raw-buffer fast path. Both array carriers also implement the CPython buffer protocol, so memoryview(pyfory.float16array(...)) and memoryview(pyfory.bfloat16array(...)) expose the packed uint16 storage directly.

Type Mapping

Python	Java	Rust	Go
`str`	`String`	`String`	`string`
`int`	`long`	`i64`	`int64`
`pyfory.int32`	`int`	`i32`	`int32`
`pyfory.int64`	`long`	`i64`	`int64`
`float`	`double`	`f64`	`float64`
`pyfory.float32`	`float`	`f32`	`float32`
`pyfory.float16`	`Float16`	`Float16`	`float16.Float16`
`pyfory.bfloat16`	`BFloat16`	`BFloat16`	`bfloat16.BFloat16`
`pyfory.float16array`	`Float16List`	`Vec<Float16>`	`[]float16.Float16`
`pyfory.bfloat16array`	`BFloat16List`	`Vec<BFloat16>`	`[]bfloat16.BFloat16`
`list`	`List`	`Vec`	`[]T`
`dict`	`Map`	`HashMap`	`map[K]V`

Differences from Python Native Mode

The binary protocol and API are similar to pyfory's python-native mode, but Python-native mode can serialize any Python object—including global functions, local functions, lambdas, local classes, and types with customized serialization using __getstate__/__reduce__/__reduce_ex__, which are not allowed in xlang mode.