How do I update a list in reaktiv without creating a new object every time?

You must create a new list instance to trigger updates, such as using items.set([*items(), 4]) or providing a custom equality function for value-based comparison. In-place modifications won't be detected by default.

reaktiv vs RxPy for reactive programming in Python?

reaktiv focuses on fine-grained, Excel-like reactivity with automatic dependency tracking for state management, while RxPy is based on ReactiveX for event-driven streams. reaktiv is better for declarative state relationships; RxPy suits complex event processing.

How to use reaktiv with async functions and avoid race conditions?

Use the Resource class for async data loading with automatic cancellation, or stick to synchronous effects that spawn async tasks. Avoid experimental async effects for predictable behavior, as recommended in the docs.

Can I integrate reaktiv with FastAPI for real-time updates?

Yes, reaktiv includes a FastAPI websocket example showing reactive state pushed to clients. Signals can trigger effects that update websocket connections, making it suitable for real-time backend applications.

What happens if I forget to assign an effect to a variable in reaktiv?

The effect will be garbage collected immediately and won't run, causing silent failures. This design prevents memory leaks but requires explicit retention, as emphasized in the README's warnings.

How does reaktiv handle errors in reactive computations?

Errors in computed values or effects must be manually caught with try-except blocks to prevent cascading failures. The library doesn't provide built-in error recovery, so you need to implement safe wrappers.

Open-Awesome

reaktiv

MITPython0.24.2

A reactive state management library for Python with automatic dependency tracking, inspired by Angular Signals and SolidJS.

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452 stars9 forks0 contributors

What is reaktiv?

reaktiv is a reactive state management library for Python that provides Signals, Computed values, and Effects to manage application state with automatic dependency tracking. It solves the problem of manually updating dependent state by allowing developers to declare data relationships, ensuring updates propagate automatically when source data changes.

Target Audience

Python developers building applications with complex state logic, such as data processing pipelines, configuration management systems, real-time monitoring tools, or any project where manual state synchronization is error-prone.

Value Proposition

Developers choose reaktiv for its Excel-like reactivity model, which guarantees data consistency, reduces bugs from forgotten updates, and offers fine-grained performance optimizations. Its type safety and async support make it a robust choice for modern Python applications.

Overview

Signals for Python - Reactive Declarative State Management Library for Python - automatic dependency tracking and reactive updates for your application state. Inspired by Angular Signals / SolidJS.

Use Cases

Best For

Building reactive data processing pipelines with automatic updates
Managing complex configuration with cascading overrides
Implementing real-time monitoring and alerting systems
Creating type-safe state management in Python applications
Developing applications with async data loading and cancellation
Simplifying state synchronization in UIs or backend services

Not Ideal For

Projects that heavily mutate lists or dictionaries in place without wanting to manage object creation for updates
Teams building highly dynamic, short-lived applications where managing effect retention (assigning to variables) is cumbersome
Production systems requiring battle-tested async reactivity, as async effects are experimental and can lead to race conditions
Simple scripts or prototypes where the overhead of reactive signals outweighs the benefits of automatic updates

Pros & Cons

Pros

Excel-Like Reactivity

Automatically tracks dependencies between signals and computed values, ensuring data consistency without manual updates, as shown in the order calculation example where changing price or quantity recalculates totals automatically.

Fine-Grained Performance

Only recomputes affected values when dependencies change, with lazy evaluation and memoization, optimizing performance for complex state graphs like independent data pipelines.

Robust Async Support

Includes Resource for async data loading with automatic cancellation and status management, preventing race conditions in data-fetching scenarios, as detailed in the async examples.

Full Type Safety

Integrates with static type checkers like mypy and pyright, providing autocompletion and early error detection, demonstrated with type-hinted signals and computed values.

Cons

Effect Lifetime Management

Effects must be explicitly assigned to variables to prevent garbage collection, a common source of bugs that requires careful memory management, as warned in the README's critical notes.

Mutable Object Overhead

Triggering updates with mutable objects like lists requires creating new instances instead of in-place modifications, adding boilerplate code and potential performance overhead.

Experimental Async Features

Async effects are marked experimental and can lead to unpredictable behavior if signal values change during async execution, limiting reliability for production use without careful handling.

Frequently Asked Questions

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