---
id: reactivex
version: "5.1.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# reactivex — ReactiveX (Rx) for Python

License: permissive · Maintenance: active · Downloads: 10.3M/mo

## What it is and what it does

RxPY is a Python implementation of the ReactiveX pattern for composing asynchronous and event-driven programs. It models asynchronous data flows as observable sequences that can be transformed, filtered, and combined using a rich set of operators. The library supports both fluent (method-chaining) and functional (pipe-based) syntax, giving developers flexibility in how they structure their code. Programs built with RxPY represent data streams as Observables, apply operators to query and transform those streams, and use Schedulers to control concurrency.

The package is production-stable (Development Status 5), actively maintained with recent releases, and has a substantial operator library. It runs on Python 3.10 through 3.14 and depends only on typing-extensions at runtime, keeping installation friction low. RxPY is a fairly complete port of ReactiveX from other languages, adapted to follow Python naming conventions (snake_case instead of camelCase).

Use it for:

- Build real-time data pipelines that react to incoming events, such as sensor data or user interactions, with automatic filtering and transformation.
- Implement responsive UI or API handlers that need to coordinate multiple asynchronous operations and emit results as they complete.
- Compose complex event streams from multiple sources, merging, debouncing, or throttling them based on time or data conditions.
- Handle backpressure and flow control in systems that produce data faster than it can be consumed.
- Simplify concurrent programming by expressing sequential logic over time-varying data without explicit callbacks or futures.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

RxPY is a library for building asynchronous and event-based programs using observable sequences and composable operators, supporting both fluent method-chaining and functional pipe-based syntax.

Yes. RxPY is a mature, actively maintained library with low install friction, permissive licensing, no known vulnerabilities, and strong community adoption. Install it if you need to model asynchronous or event-driven workflows as composable data streams; it is particularly valuable for real-time systems, reactive UIs, and complex event coordination. The dual syntax support (fluent and functional) makes it adaptable to different coding styles.

## Install

pip install reactivex
uv add reactivex
poetry add reactivex

## Installing reactivex

Before you install: Installation is straightforward with low friction; the package has a single lightweight runtime dependency (typing-extensions) and is actively maintained with a recent release (18 days old) and strong community engagement (5019 repository stars).

License in practice: MIT license is permissive, allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.

Quickstart:

pip install reactivex

import reactivex as rx
from reactivex import operators as ops

source = rx.of(1, 2, 3, 4, 5)
result = source.pipe(
    ops.map(lambda x: x * 2),
    ops.filter(lambda x: x > 5)
)
result.subscribe(print)

Requires Python 3.10 or above (v5.x does not support earlier versions).

Verify before relying:

- Whether the 150+ operators mentioned in the description are all documented and production-ready.
- Performance characteristics and overhead compared to other async frameworks for typical workloads.
- Scheduler behavior and thread-safety guarantees in concurrent scenarios.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 10.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

reactive programming python, observable sequences async, event-based data streams, composable operators pipeline, asynchronous event handling, rxpy reactive extensions, functional reactive programming, observable pattern python, async-programming, event-driven, reactive-streams

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