# pyper-dev/pyper

Concurrent Python made simple

Repository: https://github.com/pyper-dev/pyper
Canonical: https://ross.abutalabs.com/products/pyper
Language: Python
License: MIT
License Family: permissive
Topics: asyncio, concurrency, python, threading, data-pipelines, data-processing, multiprocessing, parallel-computing, data, data-collection, data-engineering
Last push: 2025-02-04T12:20:06+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 5, release rhythm 40, longevity 59
- inputs: {"age_days": 833, "days_push": 575, "days_rel": 591, "gap_med": 0, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1518, forks 32 (observed 2026-08-28T04:04:57.443162+00:00)

## What it is
Pyper is a pure-Python library for building concurrent and parallel data pipelines using functional programming patterns. It unifies threading, multiprocessing, and asyncio behind a simple task decorator and pipe-based composition with zero dependencies.

## Use cases
- build concurrent data pipelines in python
- combine asyncio and threading in one pipeline
- parallelize cpu-bound and io-bound steps
- process streaming data with worker pools
- write etl pipelines without race conditions
- compose functions into lazy data-processing pipelines

## When to choose
- you want a lightweight, zero-dependency way to mix threads, processes, and async in one pipeline
- you prefer functional, composable pipeline definitions over framework boilerplate
- you need lazy streaming execution with configurable worker counts

## When to avoid
- you need a full-featured distributed pipeline orchestrator like Airflow or Dagster
- your workload is trivially sequential and gains nothing from concurrency
- you need strict typing or validation of pipeline data schemas

## Facets
- artifact type: library
- maturity: active
- function: concurrency, etl, streaming, workflow-automation
- domain: developer-tools
- platform: python, cross-platform
- tags: asyncio, threading, multiprocessing, data-pipelines, functional-programming, parallel-computing, data-engineering, automation

## Member repositories
- pyper-dev/pyper (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:57.443162+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:32:02.044718+00:00, confidence not recorded.
  - readme: https://github.com/pyper-dev/pyper (fetched 2026-08-28T04:04:57.443162+00:00, sha 0a730e527636)
- Data as of 2026-08-30T08:39:29.467469+00:00.