Reaktiv – Simple Reactive Programming for Python

1 month ago 6

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Reactive declarative state management for Python — automatic dependency tracking and reactive updates for your application state.

Links¶

Live Playground: https://reaktiv.bui.app/#playground
Documentation: https://reaktiv.readthedocs.io/
Deep Dive Article: https://bui.app/the-missing-manual-for-signals-state-management-for-python-developers/
GitHub: https://github.com/buiapp/reaktiv

What is reaktiv?¶

reaktiv is a reactive declarative state management library for Python that lets you declare relationships between your data instead of manually wiring updates. When data changes, everything that depends on it updates automatically — eliminating a whole class of bugs where you forget to update dependent state.

Think of it as a spreadsheet for your Python application: change a cell, and all formulas using that cell recalculate instantly.

Why Use reaktiv?¶

reaktiv solves common pain points in state management:

Eliminates manual state synchronization - No more forgetting to update derived values
Reduces bugs - Ensures consistent state throughout your application
Simplifies code - Declare relationships once, not every time data changes
Improves performance - Only recomputes what actually needs to change

Learn more about why you should use reaktiv →

Features¶

Automatic state propagation: Change a value once, and all dependent computations update automatically
Efficient updates: Only the necessary parts are recomputed (fine‑grained reactivity)
Zero external dependencies: Lightweight and easy to incorporate into any project
Type-safe: Fully annotated for clarity and maintainability
Lazy and memoized: Computations run only when needed and cache until dependencies change

Quick Start¶

reaktiv is built around three core primitives:

Signals: Store values and notify dependents when they change
Computed Signals: Derive values that automatically update when dependencies change
Effects: Run side effects when signals or computed signals change

from reaktiv import Signal, Computed, Effect # Reactive data sources name = Signal("Alice") age = Signal(30) # Reactive derived value greeting = Computed(lambda: f"Hello, {name()}! You are {age()} years old.") # Reactive side effect (retain a reference!) greeting_effect = Effect(lambda: print(f"Updated: {greeting()}")) # Update base data — everything reacts automatically name.set("Bob") # Prints: Updated: Hello, Bob! You are 30 years old. age.set(31) # Prints: Updated: Hello, Bob! You are 31 years old.