Reactive Signals for Python – inspired by Angular's reactivity model
Hey everyone, I built reaktiv, a small reactive signals library for Python, inspired by Angular’s reactivity model. It lets you define Signals, Computed Values, and Effects that automatically track dependencies and update efficiently. The main focus is async-first reactivity without external dependencies. Here is an example code: ``` import asyncio from reaktiv import Signal, ComputeSignal, Effect async def main(): count = Signal(0) doubled = ComputeSignal(lambda: count.get() * 2) async def log_count(): print(f"Count: {count.get()}, Doubled: {doubled.get()}") Effect(log_count).schedule()…
In plain words
Reaktiv is a Python library that implements reactive programming with Signals, Computed Values, and Effects inspired by Angular's reactivity model. It automatically tracks dependencies and updates values efficiently without external dependencies, with an async-first approach. The library is designed for developers building applications that need reactive state management where changes automatically propagate through dependent computations.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey everyone, I built reaktiv, a small reactive signals library for Python, inspired by Angular’s reactivity model. It lets you define Signals, Computed Values, and Effects that automatically track dependencies and update efficiently. The main focus is async-first reactivity without external dependencies. Here is an example code: ``` import asyncio from reaktiv import Signal, ComputeSignal, Effect async def main(): count = Signal(0) doubled = ComputeSignal(lambda: count.get() * 2) async def log_count(): print(f"Count: {count.get()}, Doubled: {doubled.get()}") Effect(log_count).schedule() count.set(5) # Triggers: "Count: 5, Doubled: 10" await asyncio.sleep(0) # Allow effects to process asyncio.run(main()) ```
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