# google/visualblocks

Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.

Repository: https://github.com/google/visualblocks
Canonical: https://ross.abutalabs.com/products/visualblocks
Homepage: https://visualblocks.withgoogle.com
Language: TypeScript
License: Apache-2.0
License Family: permissive
Last push: 2026-07-01T01:30:57+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 90, release rhythm 35, longevity 89
- inputs: {"age_days": 1253, "days_push": 64, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1361, forks 176 (observed 2026-08-28T04:04:30.086125+00:00)

## What it is
Visual Blocks for ML is a Google visual programming framework that provides a no-code node graph editor for building ML pipelines from drag-and-drop components like models, inputs, processors, and visualizations. It ships as a JavaScript front-end library for embedding the editing experience, plus a Python package for use in Google Colaboratory notebooks.

## Use cases
- build ML pipelines without writing code
- prototype AI workflows with a drag-and-drop node editor
- embed a visual ML pipeline editor in my web app
- connect ML models, camera inputs, and visualizations in a graph
- register notebook functions as nodes in a visual pipeline UI
- rapidly experiment with multimedia ML application pipelines

## When to choose
- you want no-code/low-code visual prototyping of ML pipelines
- you need to embed a node graph editor in a web platform
- you work in Google Colab and want an interactive pipeline UI over notebook functions
- you are iterating on multimedia ML app ideas like camera or image pipelines

## When to avoid
- you need production-grade, code-first ML orchestration with full programmatic control
- your pipelines require custom node types not supported by the library
- you need a backend-heavy workflow engine rather than a browser-based editor

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, data-visualization, frontend-framework, ui-components, workflow-automation
- domain: machine-learning, artificial-intelligence, developer-tools, data-science, web-development
- platform: browser, python
- tags: no-code, visual-programming, node-graph-editor, ml-pipelines, prototyping, google, angular, colab, web-server, typescript

## Member repositories
- google/visualblocks (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:30.086125+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 2026-08-30T04:41:35.114884+00:00, confidence not recorded.
  - readme: https://github.com/google/visualblocks (fetched 2026-08-28T04:04:30.086125+00:00, sha c5516575e31a)
  - homepage: https://visualblocks.withgoogle.com (fetched 2026-08-29T11:59:36.034555+00:00, sha bcfd918e9726)
- Data as of 2026-08-30T08:39:29.467469+00:00.