Documentation

Everything you need to build data-science pipelines with oryxflow: declare each step as a task, and the engine runs the DAG in order, skips what's already computed, reruns exactly what a parameter, data, or code change affects, and lets you load any result by name.

New here? Start with Installation then the Quickstart.

• Installation

  Install oryxflow and its optional extras (cloud storage, export, dask).

• Quickstart

  From nothing to a running, self-caching pipeline in a few minutes.

• Transition from scripts

  Turn an existing analysis script into cached tasks.

• Writing & managing tasks

  Dependencies, inputs, outputs, and save formats.

• Workflows & Running workflows

  Wrap tasks in a flow; preview, run, and reset.

• Parameters

  Parameter inheritance and how it drives selective reruns.

• Task I/O formats

  Parquet, pickle, CSV, in-memory cache, and cloud storage.

• Managing complex workflows

  Automatic code invalidation, selective resets, multi-experiment flows.

• Build with Claude Code

  Let an AI coding assistant scaffold the project and wire the DAG.

• API Reference

  Every public symbol, generated from the source docstrings.