reaktiv 0.10.0

Reactive signals for Python with async support

reaktiv

Reactive Signals for Python with first-class async support, inspired by Angular's reactivity model.

Why reaktiv?

If you've worked with modern frontend frameworks like React, Vue, or Angular, you're familiar with the power of reactive state management. The idea is simple but transformative: when data changes, everything that depends on it updates automatically. This is the magic behind dynamic UIs and real-time systems.

But why should Python miss out on the benefits of reactivity? reaktiv brings these reactive programming advantages to your Python projects:

• Automatic state propagation: Change a value once, and all dependent computations update automatically.
• Efficient updates: Only the necessary parts are recomputed.
• Async-friendly: Seamlessly integrates with Python's asyncio for managing real-time data flows.
• Zero external dependencies: Lightweight and easy to incorporate into any project.
• Type-safe: Fully annotated for clarity and maintainability.

Real-World Use Cases

Reactive programming isn't just a frontend paradigm. In Python, it can simplify complex backend scenarios such as:

• Real-time data streams: Stock prices, sensor readings, or live updates.
• User session management: Keep track of state changes without heavy manual subscription management.
• Complex API workflows: Automatically cascade changes across related computations.

By combining these features, reaktiv provides a robust foundation for building reactive, real-time systems - whether for data streaming, live monitoring, or even Python-powered UI frameworks.

How it Works

reaktiv provides three core primitives:

1. Signals: Store a value and notify dependents when it changes.
2. Computed Signals: Derive values that automatically update when dependencies change.
3. Effects: Run side effects when signals or computed signals change.

Core Concepts

graph LR
    A[Signal] -->|Value| B[Computed Signal]
    A -->|Change| C[Effect]
    B -->|Value| C
    B -->|Change| C
    C -->|Update| D[External System]
    
    classDef signal fill:#4CAF50,color:white;
    classDef computed fill:#2196F3,color:white;
    classDef effect fill:#FF9800,color:white;
    
    class A,B signal;
    class B computed;
    class C effect;

Installation

pip install reaktiv
# or with uv
uv pip install reaktiv

Quick Start

Basic Reactivity

import asyncio
from reaktiv import Signal, Effect

async def main():
    name = Signal("Alice")

    async def greet():
        print(f"Hello, {name.get()}!")

    # Create and schedule effect
    # IMPORTANT: Assign the Effect to a variable to ensure it is not garbage collected.
    greeter = Effect(greet)
    greeter.schedule()

    name.set("Bob")  # Prints: "Hello, Bob!"
    await asyncio.sleep(0)  # Process effects

asyncio.run(main())

Using update()

Instead of calling set(new_value), update() lets you modify a signal based on its current value.

from reaktiv import Signal

counter = Signal(0)

# Standard way
counter.set(counter.get() + 1)

# Using update() for cleaner syntax
counter.update(lambda x: x + 1)

print(counter.get())  # 2

Computed Values

from reaktiv import Signal, ComputeSignal

# Synchronous context example
price = Signal(100)
tax_rate = Signal(0.2)
total = ComputeSignal(lambda: price.get() * (1 + tax_rate.get()))

print(total.get())  # 120.0
tax_rate.set(0.25)
print(total.get())  # 125.0

Async Effects

import asyncio
from reaktiv import Signal, Effect

async def main():
    counter = Signal(0)

    async def print_counter():
        print(f"Counter value is: {counter.get()}")

    # IMPORTANT: Assign the Effect to a variable to prevent it from being garbage collected.
    counter_effect = Effect(print_counter)
    counter_effect.schedule()

    for i in range(1, 4):
        await asyncio.sleep(1)  # Simulate an asynchronous operation or delay.
        counter.set(i)

    # Wait a bit to allow the last effect to process.
    await asyncio.sleep(1)

asyncio.run(main())

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Advanced Features

Using untracked()

By default, when you access a signal inside a computed function or an effect, it will subscribe to that signal. However, sometimes you may want to access a signal without tracking it as a dependency.

import asyncio
from reaktiv import Signal, Effect, untracked

async def main():
    count = Signal(10)
    message = Signal("Hello")

    async def log_message():
        tracked_count = count.get()
        untracked_msg = untracked(lambda: message.get())  # Not tracked as a dependency
        print(f"Count: {tracked_count}, Message: {untracked_msg}")

    effect = Effect(log_message)
    effect.schedule()

    count.set(20)  # Effect runs (count is tracked)
    await asyncio.sleep(1)
    message.set("New Message")  # Effect does NOT run (message is untracked)

    await asyncio.sleep(1)

asyncio.run(main())

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Using on_cleanup()

Sometimes, you need to clean up resources (e.g., cancel timers, close files, reset state) when an effect re-runs or is disposed.

import asyncio
from reaktiv import Signal, Effect

async def main():
    active = Signal(False)

    async def monitor_status(on_cleanup):
        print("Monitoring started")
        active.get()

        def cleanup():
            print("Cleaning up before next run or disposal")

        on_cleanup(cleanup)

    effect = Effect(monitor_status)
    effect.schedule()

    await asyncio.sleep(1)
    active.set(True)  # Cleanup runs before the effect runs again

    await asyncio.sleep(1)
    effect.dispose()  # Cleanup runs before the effect is disposed

asyncio.run(main())

# Output:
# Monitoring started
# Cleaning up before next run or disposal
# Monitoring started
# Cleaning up before next run or disposal

Custom Equality

When creating a Signal or ComputeSignal, you can provide a custom equality function to control when updates are triggered. This is useful for comparing objects by value rather than identity.

from reaktiv import Signal

# Simple example: compare numbers with tolerance
num = Signal(10.0, equal=lambda a, b: abs(a - b) < 0.5)

# This won't trigger updates since 10.0 and 10.3 are within 0.5 of each other
num.set(10.3)  

# This will trigger updates since 10.0 and 10.6 differ by more than 0.5
num.set(10.6)  

# Custom class comparison example
class User:
    def __init__(self, name, role):
        self.name = name
        self.role = role
        
# By default, different User instances are considered different even with same data
# Let's create a signal with custom equality that only cares about the role
user = Signal(User("Alice", "admin"), 
              equal=lambda a, b: a.role == b.role)

# This won't trigger updates because the role is still "admin"
# even though it's a different User instance with a different name
user.set(User("Bob", "admin"))

# This will trigger updates because the role changed
user.set(User("Charlie", "user"))

# For deep equality with nested structures, a simple JSON-based approach works well:
import json

def json_equal(a, b):
    return json.dumps(a, sort_keys=True) == json.dumps(b, sort_keys=True)

# This signal will only update when the content actually changes, 
# even for complex nested structures
user_profile = Signal({
    'name': 'Alice',
    'preferences': {'theme': 'dark', 'notifications': True}
}, equal=json_equal)

# This won't trigger updates (same content in a new object)
user_profile.set({
    'name': 'Alice',
    'preferences': {'theme': 'dark', 'notifications': True}
})

# This will trigger updates (content changed)
user_profile.set({
    'name': 'Alice',
    'preferences': {'theme': 'light', 'notifications': True}
})

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Real-Time Example: Polling System

import asyncio
from reaktiv import Signal, ComputeSignal, Effect

async def main():
    candidate_a = Signal(100)
    candidate_b = Signal(100)
    
    total_votes = ComputeSignal(lambda: candidate_a.get() + candidate_b.get())
    percent_a = ComputeSignal(lambda: (candidate_a.get() / total_votes.get()) * 100)
    percent_b = ComputeSignal(lambda: (candidate_b.get() / total_votes.get()) * 100)

    async def display_results():
        print(f"Total: {total_votes.get()} | A: {candidate_a.get()} ({percent_a.get():.1f}%) | B: {candidate_b.get()} ({percent_b.get():.1f}%)")

    async def check_dominance():
        if percent_a.get() > 60:
            print("Alert: Candidate A is dominating!")
        elif percent_b.get() > 60:
            print("Alert: Candidate B is dominating!")

    # Assign effects to variables to ensure they are retained
    display_effect = Effect(display_results)
    alert_effect = Effect(check_dominance)
    
    display_effect.schedule()
    alert_effect.schedule()

    for _ in range(3):
        await asyncio.sleep(1)
        candidate_a.set(candidate_a.get() + 40)
        candidate_b.set(candidate_b.get() + 10)
    
    await asyncio.sleep(1)

asyncio.run(main())

Sample Output:

Total: 200 | A: 100 (50.0%) | B: 100 (50.0%)
Total: 250 | A: 140 (56.0%) | B: 110 (44.0%)
Total: 300 | A: 180 (60.0%) | B: 120 (40.0%)
Total: 350 | A: 220 (62.9%) | B: 130 (37.1%)
Alert: Candidate A is dominating!

Examples

You can find example scripts in the examples folder to help you get started with using this project.

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Inspired by Angular Signals • Built for Python's async-first world • Made in Hamburg