dibox 0.1.4

Async-native dependency injection framework based on type hints

DIBox

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⚠️ Project Status: Early Development

This library is in its early stages. The design and API are not yet fully established and may change significantly in future releases. Feedback, suggestions, and contributions are very welcome!

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Async-native dependency injection framework based on type hints.

• Installation
• What is DIBox?
• Key Features
• QuickStart
  • 1. Define your application as usual
  • 2. Wire and Run
• Advanced usage
  • Using the @inject Decorator
  • Advanced Binding Patterns
    • Binding Interfaces & Instances
    • Factory Functions
    • Named dependencies
    • Dynamic Predicate-Based Binding
• Why use DIBox?
  • The Power of Auto-Wiring
  • Comparison with Other Frameworks
    • vs. Manual Dependency Injection
    • vs. dependency-injector
    • vs. Injector
    • vs. Punq
    • vs. Dishka
    • vs. FastAPI's Depends
• Contributing

Installation

pip install dibox

Requires Python 3.10+.

What is DIBox?

DIBox is an async‑native dependency injection container that uses standard Python type hints to build and manage your service dependency graph automatically. The core philosophy is to remove factory and wiring boilerplate so you can focus on application logic.

DIBox resolves, instantiates, and injects dependencies by following naturally defined type hints in constructors or entry points. It also orchestrates asynchronous startup and safe teardown for resources like database connections, credential loaders, or HTTP clients without extra glue code.

Key Features

• Easy to Adopt: Minimal concepts and minimal binding for most internal code.
• Pragmatic Auto-Wiring: If a class can be constructed based on type hints, DIBox will build it. This convention-first approach eliminates nearly all factory boilerplate for your internal services.
• Async‑Native Core: Seamlessly injects into async call chains and supports async factories out of the box.
• Lifecycle Automation: Detects and runs start()/close(), or context manager hooks (__aenter__/__aexit__, __enter__/__exit__) to manage resources safely.
• Advanced Binding Options: Supports predicate bindings, named injections, and factory functions (with auto‑injected factory parameters).
• Non‑Invasive: Works with any class using type hints—including third-party SDKs, dataclasses, and attrs — no wrappers or base classes required.
• No Forced Global State: A global container exists for convenience, but DIBox works equally well with per‑scope/local instances—ideal for unit tests and isolated runtimes without hidden singletons.
• Optional Decorators: @inject is convenience only; explicit await box.provide(...) stays fully supported for framework integration.
• Typed API: The public API is strictly type-annotated, so it works well type checkers and IDE autocompletion.

QuickStart

DIBox requires almost no setup. Define your classes as usual—whether you use standard Python classes with __init__, dataclasses, or attrs models.

1. Define your application as usual

DIBox can manage common lifecycle hooks, it detects and calls the commonly used methods like start()/close() or context manager methods like __aenter__/__aexit__.

import asyncio

class Credentials:
    def __init__(self, username: str):
        self.username = username

class Database:
    def __init__(self, creds: Credentials):
        self.creds = creds

class Service:
    def __init__(self, db: Database):
        self.db = db

    async def start(self):
        await asyncio.sleep(0.05)  # simulate warm-up
        print("Service started")

    async def close(self):
        await asyncio.sleep(0.05)  # simulate cleanup
        print("Service closed")

    def run(self):
        print("Service is running...")

2. Wire and Run

We only bind Credentials manually because it is raw data. DIBox automatically figures out how to create Database and inject it into Service.

The only wiring you need to do is tell DIBox how to create the things it can’t infer on its own (like Credentials)

def setup_bindings(box: DIBox):
    box.bind(Credentials, Credentials(username="admin"))

To get a service instance, just call provide() with the target type. DIBox will inspect the constructor, see that it needs a Database, then see that Database needs Credentials, and automatically build the whole graph for you. Moreover, the container ensures all context manager classes are properly entered and exited.

box = DIBox()

async def run():
        # DIBox creates Credentials -> Database -> Service + awaits start()
        service = await box.provide(Service)
        service.run()

There is also another way if you prefer decorators instead of explicit provide() calls:

box = DIBox()

@inject(box)
async def run(service: Injected[Service]):
    service.run()

Finally, you just need to run the main loop with the container managing the lifecycle:

async def async_main():
    async with box:
        setup_bindings(box)
        await run()
    # dibox automatically calls close() on Service and any other resources
    # when exiting the context

if __name__ == "__main__":
    asyncio.run(async_main())

That’s the core loop: bind the bits DIBox can’t infer, then @inject or provide(...) at the entry point and let the container manage construction + cleanup.

Advanced usage

Using the @inject Decorator

While strict type-hinting in constructors covers most of your application, you eventually reach the "entry points"—places where a framework (like Azure Functions, AWS Lambda, or a CLI) calls your code.

You can use the @inject decorator here. It inspects your arguments, identifies which ones need injection using the Injected marker, and passes them in automatically.

Example: Azure Function Handler

In this scenario, the Azure runtime calls main with a req object. DIBox intercepts the call, creates your ProcessingService (and its dependencies), and injects it alongside the request.

import azure.functions as func
from dibox import inject, Injected

class ProcessingService:
    def process(self, body: str) -> str:
        return f"Processed: {body}"

# The decorator modifies the signature so Azure sees: main(req: func.HttpRequest)
# But DIBox calls it as: main(req, service=instance_of_processing_service)
@inject()
async def main(req: func.HttpRequest, service: Injected[ProcessingService]) -> func.HttpResponse:
    result = service.process(req.get_body().decode())
    return func.HttpResponse(f"Success! {result}", status_code=200)

By default, @inject uses a convenient global_dibox singleton container. You can bind dependencies to it from anywhere in your application.

from dibox import global_dibox, inject, Injected

# You can access global_dibox to bind things manually
global_dibox.bind(APIKey, APIKey("secret_key"))

# Uses global_dibox implicitly
@inject()
def my_handler(service: Injected[Service]):
    ...

For greater control, you can pass a dedicated DIBox instance to the decorator. This ensures dependency resolution is isolated and predictable.

from dibox import DIBox, inject

# Create a specific container for this app or test
local_box = DIBox()
local_box.bind(APIKey, APIKey("test_key"))

# Pass it explicitly to the decorator
@inject(local_box)
async def specific_handler(service: Injected[Service]):
    ...

Advanced Binding Patterns

DIBox shines when you need precise control over object creation. You can mix and match these patterns to handle everything from cloud clients to dynamic configuration.

Binding Interfaces & Instances

You can bind a base class to a concrete implementation or a specific instance.

from dibox import DIBox

box = DIBox()

azure_credentials = DefaultAzureCredential()
# Any request for TokenCredential will receive azure_credentials object
# box.bind(TokenCredential, azure_credentials) works too!
box.bind(TokenCredential, instance=azure_credentials)
# Or bind an interface to a concrete class
box.bind(DatabaseInterface, CosmosDBDatabase)

Factory Functions

Sometimes a simple constructor isn't enough—you may need asynchronous setup (fetching secrets, warming a client) or a third‑party initialization step. Bind the target type to a factory function—sync or async.

Key Feature: DIBox inspects your factory’s signature, auto‑injects its parameters, and if it is async it awaits it automatically before wiring downstream dependencies.

# Async factory: simulate secret fetch / remote handshake
async def create_cosmos_client(settings: Settings) -> CosmosClient:
    await asyncio.sleep(0.05)  # simulate IO
    return CosmosClient(url=settings.url, key=settings.key)

# Sync factory depending on the async-created client
def create_orders_container(client: CosmosClient) -> OrderContainer:
    return OrderContainer(client.get_container("orders"))

# Bind factories (DIBox auto-injects Settings, awaits async factory)
box.bind(CosmosClient, create_cosmos_client)
# to be more explicit, you can use the factory= keyword argument:
box.bind(OrderContainer, factory=create_orders_container)

order_container = await box.provide(OrderContainer)  # auto sequence:
# Settings -> await create_cosmos_client -> create_orders_container

Named dependencies

If you need multiple instances of the same type (like two different storage containers), use the name parameter. DIBox matches this binding to the argument name.

box.bind(ContainerClient, "users", factory=create_users_container)
box.bind(ContainerClient, "orders", factory=create_orders_container)

class DataService:
    def __init__(self, users: ContainerClient, orders: ContainerClient):
        self.users = users    # Injected with "users" binding
        self.orders = orders  # Injected with "orders" binding

# Requesting DataService will get both ContainerClients injected correctly
data_service = await box.provide(DataService)

Dynamic Predicate-Based Binding

For repeatable patterns, you can use a predicate function to match types dynamically. This is useful for generic loaders or handlers. The factory function can also receive the requested type as a parameter for more context-aware creation.

def load_settings(t: type) -> object:
    # Load settings based on type t
    ...

# Bind ANY type ending in 'Settings' to the 'load_settings' function
box.bind(lambda t: t.__name__.endswith("Settings"), load_settings)

# Now requesting AppSettings or DBSettings will use load_settings automatically
app_settings = await box.provide(AppSettings)
db_settings = await box.provide(DBSettings)

Why use DIBox?

The Power of Auto-Wiring

Dependency Injection (DI) decouples your high-level business logic from low-level implementation details (like database drivers or API clients). This makes your code modular and effortless to test—you can easily swap a real database for a mock during unit tests.

However, traditional DI often trades one problem for another: Dependency Hell. You end up writing hundreds of lines of "glue code" just to instantiate your service graph.

DIBox's standout feature is its ability to automatically resolve and inject dependencies based on type hints. It inspects your classes, sees what they need, and assembles the puzzle for you. You stop writing factories and start writing features.

Comparison with Other Frameworks

There are many great DI frameworks for Python out there. Here is why you might choose DIBox:

• vs. Manual Dependency Injection

  • The Problem: Manually instantiating services (Service(Database(Config()))) works for small scripts but becomes tedious and error-prone as your app grows.
  • The DIBox Way: DIBox eliminates boilerplate factory code by auto-wiring based on type hints. You write less glue code and focus on your business logic.

• vs. dependency-injector

  • The Approach: Dependency Injector is a powerful, feature-rich framework that uses a declarative style. You explicitly define Container classes and Providers for every component.
  • The DIBox Difference: DIBox takes a more implicit, convention-over-configuration approach. You rarely need to define explicit providers—most wiring is automatic based on type hints. This makes it particularly seamless when integrating Third-Party SDKs (like Azure SDK or Boto3). You can simply bind an abstract class (e.g., TokenCredential) to a concrete instance, and DIBox automatically injects it into the SDK client's constructor without needing wrapper classes or complex factory providers.

• vs. Injector

  • The Approach: Injector encourages a structured configuration style using explicit Module classes and Provider methods. While it supports type hints, it often relies on the @inject decorator to explicitly mark constructors for injection—particularly when you need to mix injectable and non-injectable arguments or when auto_bind is disabled.
  • The DIBox Difference: DIBox favors a zero-boilerplate approach. It does not require separate Module definitions to wire your graph; it defaults to auto-wiring based on existing type hints. For lifecycle concerns, DIBox automatically detects common async/sync resource hooks (__aenter__/__aexit__, start()/close(), context managers) and runs them for you. Injector provides lifecycle and scoping control through its own mechanisms and explicit patterns; DIBox emphasizes convention and automatic detection for asynchronous workloads.

• vs. Punq

  • The Approach: Punq is a minimalistic DI container that shares our philosophy of simplicity and auto-wiring. It relies heavily on explicit bindings and does not support advanced features like async lifecycle management or predicate-based bindings.
  • The DIBox Difference: DIBox adds async lifecycle management and a few more binding patterns while keeping the same “type-hints-first” feel.

• vs. Dishka

  • The Approach: Dishka is a powerful DI framework built around a first-class scoping system and explicit Provider classes. This gives you fine-grained control over dependency lifetimes and structure, with ready-made integrations for many popular frameworks.
  • The DIBox Difference: DIBox offers a simpler, more minimal API. Instead of Provider classes, DIBox auto-wires any class with a type-annotated constructor, so you only bind what can't be inferred (e.g., interfaces, raw values). This convention-over-configuration approach reduces boilerplate for common cases. DIBox also offers unique features like predicate-based binding and named-argument injection. However, Dishka currently has a more mature feature set, including a robust scoping model, modular provider composition, and a dependency graph visualizer. If those features are critical for your project right now, Dishka is an excellent choice. Scopes and modules are on the DIBox roadmap.

• vs. FastAPI's Depends

  • The Approach: FastAPI revolutionized Python development with its intuitive, type-hint-based dependency injection. It is the primary inspiration behind DIBox. FastAPI's dependency injection system is tightly integrated with its web framework. It uses the Depends marker to declare dependencies in path operation functions.
  • The DIBox Difference: While FastAPI's DI is excellent for web applications, DIBox is a standalone framework that can be used across any Python application. It extends the same principles to a broader context, including CLI apps, serverless functions, and background services. DIBox also adds advanced features like async lifecycle management and predicate-based bindings that go beyond FastAPI's capabilities.

Contributing

The project is in early stages, and contributions are welcome! Please contact me (Alex Z.) via GitHub issues, LinkedIn or email for any questions, suggestions, or contributions. The source code is hosted both on GitHub (https://github.com/mxAlexZ/dibox) and GitLab (https://gitlab.com/AlexZee/dibox). The actual development happens on GitLab, while GitHub is used for better visibility.