Reactive Signals for Python with first-class async support, inspired by Angular's reactivity model.
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
asynciofor managing real-time data flows. - Zero external dependencies: Lightweight and easy to incorporate into any project.
- Type-safe: Fully annotated for clarity and maintainability.
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.
reaktiv provides three core primitives:
- Signals: Store a value and notify dependents when it changes.
- Computed Signals: Derive values that automatically update when dependencies change.
- Effects: Run side effects when signals or computed signals change.
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;
pip install reaktiv
# or with uv
uv pip install reaktivimport 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())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()) # 2from 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()) # 125.0import 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())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())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 disposalimport 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!
You can find example scripts in the examples folder to help you get started with using this project.
Inspired by Angular Signals • Built for Python's async-first world • Made in Hamburg
