# videoflow/videoflow

Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.

Repository: https://github.com/videoflow/videoflow
Canonical: https://ross.abutalabs.com/products/videoflow
Homepage: https://docs.videoflow.dev
Language: Python
License: MIT
License Family: permissive
Topics: video-processing, video-analytics, video-pipeline, object-detection, object-tracking
Last push: 2026-07-29T17:46:39+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 8, longevity 100
- inputs: {"age_days": 2697, "days_push": 35, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1034, forks 91 (observed 2026-08-28T04:03:18.566485+00:00)

## What it is
Videoflow is a Python framework for building distributed video and stream processing pipelines as directed acyclic graphs of producers, processors, and consumers. Pipelines run locally as OS subprocesses or on Kubernetes as containers, communicating over NATS JetStream, with built-in components for object detection, tracking, pose estimation, segmentation, and video I/O.

## Use cases
- build a real-time video analytics pipeline
- run object detection and tracking on video streams
- deploy video processing pipelines on Kubernetes
- process camera streams with multiple ML models
- build a distributed stream processing DAG in Python
- analyze surveillance video with pose estimation and segmentation

## When to choose
- you need distributed or scalable video/stream processing with per-node scaling
- you want a DAG-based pipeline that runs locally and on Kubernetes without code changes
- you need off-the-shelf vision components like detection, tracking, or pose estimation
- you want language-agnostic pipeline components shipped as containers

## When to avoid
- you only need simple single-script video file processing without distribution
- you cannot run a NATS JetStream broker in your environment
- you need a mature, battle-tested framework with a large community
- your use case is not stream or series processing

## Facets
- artifact type: framework
- maturity: active
- function: video-processing, streaming, message-queue, machine-learning, computer-vision, workflow-automation
- domain: computer-vision, machine-learning, microservices
- platform: python, cross-platform
- tags: video-analytics, dag-pipeline, nats-jetstream, object-detection, object-tracking, multiprocessing, distributed-computing, video, data-engineering, containers, kubernetes, docker, linux, macos

## Member repositories
- videoflow/videoflow (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.566485+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 2026-08-30T07:06:36.493163+00:00, confidence not recorded.
  - readme: https://github.com/videoflow/videoflow (fetched 2026-08-28T04:03:18.566485+00:00, sha 2caa16000f53)
  - registry_pypi: https://pypi.org/pypi/videoflow/json (fetched 2026-08-29T13:06:28.166879+00:00, sha 66745dace34f)
- Data as of 2026-08-30T08:39:29.467469+00:00.