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Loom

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A lightweight visual pipeline runner for research.

Connect your Python scripts into a graph, tweak parameters, run experiments, see results — without setting up Airflow or learning a workflow framework.

  • Try it now

    Open the live demo — no installation required. Browse and run example pipelines in your browser.

    First load may take ~30s to wake up.

  • Install

    pip install loom-pipeline[ui]

    Add to any project's virtualenv and start building pipelines.

Overview

Loom gives you a CLI runner and visual editor for pipelines defined in YAML. Your scripts stay as regular Python with argparse — no framework to learn, no rewrites needed.

It's designed for research workflows. For production orchestration, tools like Airflow or Kubeflow are better suited.

Philosophy

Loom is intentionally minimal:

  • No database — Everything is files: your scripts, YAML configs, and outputs
  • No external services — The visual editor runs a local server that stops when you close it
  • No lock-in — Your scripts work with or without Loom
  • No magic — Loom just builds shell commands and runs them

This makes it easy to adopt incrementally. Start with one experiment, see if it helps, expand from there.

Quick Example

# experiment.yml
variables:
  video: data/raw/recording.mp4
  features: data/processed/features.csv
  model: models/classifier.pt

parameters:
  learning_rate: 0.001
  epochs: 100

pipeline:
  - name: extract
    task: tasks/extract_features.py
    inputs:
      video: $video
    outputs:
      -o: $features

  - name: train
    task: tasks/train_model.py
    inputs:
      data: $features
    outputs:
      -o: $model
    args:
      --lr: $learning_rate
      --epochs: $epochs

# Run the full pipeline
loom experiment.yml

# Or open in the visual editor
loom-ui experiment.yml

Next Steps