configgle 1.1.3

Hierarchical experiment configuration and dependency injection using pure Python dataclass factories.

configgle🤭

Hierarchical configuration using pure Python dataclasses, with typed factory methods, covariant protocols, and full inheritance support.

Installation

python -m pip install configgle

Example

from configgle import Fig

class Model:
    class Config(Fig["Model"]):
        hidden_size: int = 256
        num_layers: int = 4

    def __init__(self, config: Config):
        self.config = config

# Create and modify config
config = Model.Config(hidden_size=512)

# Instantiate the parent class
model = config.make()
print(model.config.hidden_size)  # 512

Or use @autofig to auto-generate the Config from __init__:

from configgle import autofig

@autofig
class Model:
    def __init__(self, hidden_size: int = 256, num_layers: int = 4):
        self.hidden_size = hidden_size
        self.num_layers = num_layers

# Config is auto-generated from __init__ signature
model = Model.Config(hidden_size=512).make()
print(model.hidden_size)  # 512

Features

Type-safe make()

Fig tracks the parent class automatically. You can use bare Fig and everything works with no type warnings -- the type parameter defaults to Any:

class Model:
    class Config(Fig):
        hidden_size: int = 256

    def __init__(self, config: Config):
        self.hidden_size = config.hidden_size

model = Model.Config(hidden_size=512).make()

For tighter checking, parameterize with the parent class name and make() returns the exact type:

class Model:
    class Config(Fig["Model"]):
        hidden_size: int = 256

    def __init__(self, config: Config):
        self.hidden_size = config.hidden_size

model: Model = Model.Config(hidden_size=512).make()  # returns Model, not object

Inheritance with Makes

When a child class inherits a parent's Config, the make() return type would normally be the parent. Use Makes to re-bind it:

class Animal:
    class Config(Fig["Animal"]):
        name: str = "animal"

    def __init__(self, config: Config):
        self.name = config.name

class Dog(Animal):
    class Config(Makes["Dog"], Animal.Config):
        breed: str = "mutt"

    def __init__(self, config: Config):
        super().__init__(config)
        self.breed = config.breed

dog: Dog = Dog.Config(name="Rex", breed="labrador").make()  # returns Dog, not Animal

Makes contributes nothing to the MRO at runtime -- it exists purely for the type checker. It's a workaround for Python's lack of an Intersection type: MakerMeta.__get__ already narrows Dog.Config to type[Config] & type[Makeable[Dog]] at runtime, but there's no way to express that statically today. When Intersection lands, Makes will become unnecessary -- configgle is already forward-compatible with that change.

Covariant Makeable protocol

Makeable[T] is a covariant protocol satisfied by any Fig, InlineConfig, or custom class with make(), finalize(), and update(). Because it's covariant, Makeable[Dog] is assignable to Makeable[Animal]:

from configgle import Makeable

def train(config: Makeable[Animal]) -> Animal:
    return config.make()

# All valid:
train(Animal.Config())
train(Dog.Config(breed="poodle"))

This makes it easy to write functions that accept any config for a class hierarchy without losing type information.

Nested config finalization

Override finalize() to compute derived fields before instantiation. Nested configs are finalized recursively:

class Encoder:
    class Config(Fig["Encoder"]):
        c_in: int = 256
        mlp: MLP.Config = field(default_factory=MLP.Config)

        def finalize(self) -> Self:
            self = super().finalize()
            self.mlp.c_in = self.c_in  # propagate dimensions
            return self

update() for bulk mutation

Configs support bulk updates from another config, a dict, or keyword arguments:

cfg = Model.Config(hidden_size=256)
cfg.update(hidden_size=512, num_layers=8)

# Or copy from another config (kwargs take precedence):
cfg.update(other_cfg, num_layers=12)

@autofig for zero-boilerplate configs

When you don't need a hand-written Config, @autofig generates one from __init__ (see Example above).

Pickling and cloudpickle

Configs are fully compatible with pickle and cloudpickle, including the parent class reference. This is important for distributed workflows (e.g., sending configs across processes):

import cloudpickle, pickle

cfg = Model.Config(hidden_size=512)
cfg_ = pickle.loads(cloudpickle.dumps(cfg))
model = cfg_.make()  # parent_class is preserved

Comparison

	configgle	Hydra	Sacred	OmegaConf	Gin	ml_collections	Fiddle	Confugue
Pure Python (no YAML/strings)	✅	❌	❌	🟡	❌	✅	✅	❌
Typed make()/build() return	✅	❌	❌	❌	❌	❌	✅	❌
Config inheritance	✅	🟡	❌	🟡	❌	❌	❌	🟡
Covariant protocol	✅	❌	❌	❌	❌	❌	❌	❌
Nested finalization	✅	❌	❌	❌	❌	❌	❌	❌
pickle/cloudpickle	✅	🟡	❌	✅	❌	🟡	✅	❌
Auto-generated configs	✅	🟡	❌	❌	❌	❌	✅	❌
GitHub stars	--	10.2k	4.4k	2.3k	2.1k	1.0k	374	21

✅ = yes, 🟡 = partial, ❌ = no. Corrections welcome -- open a PR.

How each library works

Hydra (Meta) -- YAML-centric with optional "structured configs" (Python dataclasses registered in a ConfigStore). Instantiation uses hydra.utils.instantiate(), which resolves a string _target_ field to an import path -- the return type is Any. Config composition is done via YAML defaults lists, not class inheritance. Dataclass inheritance works at the schema level. configen is an experimental code-generation tool (v0.9.0.dev8) that produces structured configs from class signatures. Configs survive pickle trivially since _target_ is a string, not a class reference.

Sacred -- Experiment management framework. Config is defined via @ex.config scopes (local variables become config entries) or loaded from YAML/JSON files. Sacred auto-injects config values into captured functions by parameter name (dependency injection), but does not auto-generate configs from function signatures. No typed factory methods, no config inheritance, no pickle support for the experiment/config machinery.

OmegaConf -- YAML-native configuration with a "structured config" mode that accepts @dataclass schemas. Configs are always wrapped in DictConfig proxy objects at runtime (not actual dataclass instances). Supports dataclass inheritance for schema definition. Good pickle support (__getstate__/__setstate__). No factory method (to_object() returns Any), no auto-generation, no protocols.

Gin (Google) -- Global string-based registry. You decorate functions with @gin.configurable and bind parameters via .gin files or gin.bind_parameter('fn.param', val). There are no config objects -- parameter values live in a global dict keyed by dotted strings. No typed returns, no config inheritance. The docs state "gin-configurable functions are not pickleable," though a 2020 PR added __reduce__ methods that improve support.

ml_collections (Google) -- Dict-like ConfigDict with dot-access, type-checking on mutation, and FieldReference for lazy cross-references between values. Pure Python, no YAML. No factory method or typed instantiation. Pickle works for plain configs, but FieldReference operations that use lambdas internally (.identity(), .to_int()) fail with standard pickle (cloudpickle handles them).

Fiddle (Google) -- Python-first. You build config graphs with fdl.Config[MyClass] objects and call fdl.build() to instantiate them. build(Config[T]) -> T is typed via @overload. Config modification is functional (fdl.copy_with), not inheritance-based -- there are no config subclasses. @auto_config rewrites a factory function's AST to produce a config graph automatically. Full pickle/cloudpickle support.

Confugue -- YAML-based hierarchical configuration. The configure() method instantiates objects from YAML dicts, with the class specified via a !type YAML tag. Returns Any. Partial config inheritance via YAML merge keys (<<: *base). No pickle support, no auto-generation, no protocols.

Citing

If you find our work useful, please consider citing:

@misc{dillon2026configgle,
      title={Configgle - Hierarchical experiment configuration using pure Python dataclass factories and dependency injection.},
      author={Joshua V. Dillon},
      year={2026},
      howpublished={Github},
      url={https://github.com/jvdillon/configgle},
}

License

Apache License 2.0