video2flow
Extract video frames as descriptive text flows for LLM consumption.
Claude, GPT-4o, Gemini and other multimodal models can see images but not
video. video2flow bridges the gap: extract frames → generate timestamped
descriptions → feed the text flow to any LLM.
Installation
pip install video2flow
Usage
# Extract frames from a video
video2flow extract video.mp4 -o frames/ --fps 1
# Quick description (without vision API)
video2flow describe video.mp4 --max-frames 10
# Full pipeline
video2flow pipeline video.mp4 -o video_flow/
LLM Integration
Pass the output JSON to any LLM:
import json
flow = json.loads(open("video_flow/flow.json").read())
prompt = flow["usage"]["example_prompt"]
# Then: response = llm.invoke(prompt)
For detailed vision understanding, send image files directly to a multimodal model alongside the flow transcript.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for video2flow 0.3.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| video2flow-0.3.2-cp313-cp313-win_amd64.whl | CPython 3.13 | CPython 3.13 | Windows x86-64 | Details |
Release files / video2flow-0.3.2-cp313-cp313-win_amd64.whl
| Download URL | video2flow-0.3.2-cp313-cp313-win_amd64.whl |
|---|---|
| Size | 209.9 kB |
| Tags | CPython 3.13 Windows x86-64 |
|
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