yaml-workflow 0.5.0

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files

YAML Workflow

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files. Create modular, reusable workflows by connecting tasks through YAML definitions, with support for parallel processing, batch operations, and state management.

Why yaml-workflow?

Most workflow tools (Airflow, Prefect, Dagster) are designed for distributed cloud infrastructure with complex server setups. yaml-workflow takes a different approach:

	yaml-workflow	Airflow / Prefect / Dagster
Setup	pip install yaml-workflow	Server, database, scheduler, workers
Configuration	Plain YAML files	Python DAGs + infrastructure config
Dependencies	3 (PyYAML, Jinja2, Click)	50+ packages, Docker, PostgreSQL
Use case	Local automation, scripts, CI/CD, data pipelines	Enterprise orchestration at scale
Learning curve	Minutes	Hours to days
State	File-based, resumable	Database-backed

Choose yaml-workflow when you need:

• Simple task automation without infrastructure overhead
• Reproducible pipelines defined in version-controlled YAML
• Batch processing with parallel execution
• State persistence and workflow resume after failures
• A lightweight alternative to shell scripts with better error handling

Features

• YAML-driven workflow definition with Jinja2 templating
• Multiple task types: shell, Python, file, template, HTTP, batch
• Parallel execution with configurable worker pools
• State persistence and resume capability
• Dry-run mode to preview without executing
• Workflow visualization (ASCII and Mermaid)
• Retry mechanisms with configurable strategies
• Namespaced variables (args, env, steps, batch)
• Flow control with custom step sequences and conditions
• Extensible task system via @register_task decorator

Quick Start

# Install
pip install yaml-workflow

# Initialize example workflows
yaml-workflow init

# Run a workflow with parameters
yaml-workflow run workflows/hello_world.yaml name=Alice

Example workflow (hello_world.yaml):

name: Hello World
description: A simple greeting workflow

params:
  name:
    type: string
    default: World

steps:
  - name: create_greeting
    task: template
    inputs:
      template: "Hello, {{ args.name }}!"
      output_file: greeting.txt

  - name: show_greeting
    task: shell
    inputs:
      command: cat greeting.txt

Visualize workflows

yaml-workflow visualize workflows/data_pipeline.yaml

  Workflow: Data Pipeline

  ┌─────────────────┐
  │  detect_format   │
  │   python_code    │
  └─────────────────┘
           │
           ▼
  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
  │ process_json │  │ process_csv  │  │ process_xml  │  │handle_unknown│
  │    shell     │  │    shell     │  │    shell     │  │    shell     │
  └──────────────┘  └──────────────┘  └──────────────┘  └──────────────┘
           │
           ▼
  ┌─────────────────┐
  │ generate_report  │
  │   python_code    │
  └─────────────────┘

Adjacent conditional steps are automatically grouped as branches. Use --format mermaid to export for docs or GitHub rendering.

Dry-run mode

Preview what a workflow would do without executing anything:

yaml-workflow run workflows/hello_world.yaml name=Alice --dry-run

[DRY-RUN] Workflow: Hello World
[DRY-RUN] Steps: 2 to execute

  [DRY-RUN] Step 'create_greeting' — task: template — WOULD EXECUTE
    template: Hello, Alice!
    output_file: greeting.txt
  [DRY-RUN] Step 'show_greeting' — task: shell — WOULD EXECUTE
    command: cat greeting.txt

[DRY-RUN] Complete. 2 step(s) would execute, 0 would be skipped.
[DRY-RUN] No files were written. No tasks were executed.

More commands

# List available workflows
yaml-workflow list

# Validate a workflow
yaml-workflow validate workflows/hello_world.yaml

# Resume a failed workflow
yaml-workflow run workflows/hello_world.yaml --resume

Documentation

Full documentation is available at orieg.github.io/yaml-workflow.

• Getting Started - Installation and first workflow
• Task Types - Shell, Python, file, template, and batch tasks
• Workflow Structure - YAML configuration reference
• Templating - Jinja2 variable substitution
• State Management - Persistence and resume
• Task Development - Creating custom tasks
• API Reference - Full API documentation

Contributing

Contributions are welcome! See the Contributing Guide for development setup and guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.