Do you handle JSON numbers safely by default or do you require that people make their own deserializers for numbers that would lose precision when coerced into Python's float type? The most common mistake that I see JSON libraries make is using fixed precision floating point types somewhere in the process when handling numbers while JSON's number type specifies no such limitation, which then causes precision loss unless people catch the problem and do their own pre-serialization.
I built it after repeatedly running into friction with Python’s built-in json module when working with classes, dataclasses, nested objects, and type hints.
Jsonic focuses on:
- Zero-boilerplate serialization and deserialization
- Strict type validation with clear errors
- Natural support for dataclasses, enums, tuples, sets, nested objects etc.
- Optional field exclusion (e.g. hiding sensitive data)
- Extra features like transient fields definition, suport for __slots__ classes etc.
- Clean interop with Pydantic models
The goal is to make JSON round-tripping feel Pythonic and predictable without writing to_dict() / from_dict() everywhere.
I’d really appreciate feedback on the API design and tradeoffs.
The article would benefit from a very clear and explicit section on pydantic model_dump_json() vs your tool. As that's the primary thing you're tool is likely competing against
Pydantic is great lib and and has many advantages over Jsonic,
I think main use cases for Jsonic over Pydantic are:
- You already have plain Python classes or dataclasses and don’t want to convert them to BaseModel
- You prefer minimal intrusion - no inheritance, no decorators, no schema definitions
- You need to serialize and deserialize Pydantic models alongside non-Pydantic classes
Having said that, Pydantic is the better choice in most cases.
This is also why Jsonic integrate natively with Pydantic so you can serialize Pydantic models using Jsonic out of the box
I can see that. Pydantic is great but relatively slow (which matters on edge devices) and can be bloated.
The fact that all your projects use Pydantic makes it an easy starting point and created standardisation - of course.
Nevertheless, I can definitely see some use-cases for lightweight JSON-serialisation without bringing in Pydantic. Dataclasses are great, but lack proper json handling.
I built it after repeatedly running into friction with Python’s built-in json module when working with classes, dataclasses, nested objects, and type hints.
Jsonic focuses on: - Zero-boilerplate serialization and deserialization - Strict type validation with clear errors - Natural support for dataclasses, enums, tuples, sets, nested objects etc. - Optional field exclusion (e.g. hiding sensitive data) - Extra features like transient fields definition, suport for __slots__ classes etc. - Clean interop with Pydantic models
The goal is to make JSON round-tripping feel Pythonic and predictable without writing to_dict() / from_dict() everywhere.
I’d really appreciate feedback on the API design and tradeoffs.
your comment above has the same broken formatting
does not inspire confidence if you can't spot such obvious breakage
I think main use cases for Jsonic over Pydantic are: - You already have plain Python classes or dataclasses and don’t want to convert them to BaseModel - You prefer minimal intrusion - no inheritance, no decorators, no schema definitions - You need to serialize and deserialize Pydantic models alongside non-Pydantic classes
Having said that, Pydantic is the better choice in most cases.
This is also why Jsonic integrate natively with Pydantic so you can serialize Pydantic models using Jsonic out of the box
The fact that all your projects use Pydantic makes it an easy starting point and created standardisation - of course.
Nevertheless, I can definitely see some use-cases for lightweight JSON-serialisation without bringing in Pydantic. Dataclasses are great, but lack proper json handling.