videoflow/videoflow

Health v2 · maintenance only

Adoption not part of the score

1034 stars · 91 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

framework · maturity active

video-processing streaming message-queue machine-learning computer-vision workflow-automation computer-vision machine-learning microservices python cross-platform video-analytics dag-pipeline nats-jetstream object-detection object-tracking multiprocessing distributed-computing video data-engineering containers kubernetes docker linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="videoflow/videoflow")