# SpiffWorkflow

**Repository Path**: ccpwufan/SpiffWorkflow

## Basic Information

- **Project Name**: SpiffWorkflow
- **Description**: A powerful workflow engine implemented in pure Python
- **Primary Language**: Unknown
- **License**: LGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Spiff Workflow

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## Summary

Spiff Workflow is a workflow engine implemented in pure Python. It is based
on the excellent work of the
[Workflow Patterns initiative](http://www.workflowpatterns.com/).

## Do you need commercial support?

Spiff Workflow is supported by [Procedure 8](https://procedure8.com). Get in touch if you need anything!

## Main design goals

- Spiff Workflow aims to directly support as many of the patterns of
  workflowpatterns.com as possible.
- Spiff Workflow uses unit testing as much as possible.
- Spiff Workflow provides a **clean Python API**.
- Spiff Workflow allows for mapping patterns into workflow elements that
  are **easy to understand for non-technical users** in a workflow GUI editor.
- Spiff Workflow implements the best possible **path prediction** for
  workflows.

Spiff Workflow also provides a parser and workflow emulation
layer that can be used to create executable Spiff Workflow specifications
from Business Process Model and Notation (BPMN) documents.

## Quick Intro

The process of using Spiff Workflow involves the following steps:

1. Write a workflow specification. A specification may be written using
   XML ([example](https://github.com/knipknap/SpiffWorkflow/blob/master/tests/SpiffWorkflow/data/spiff/workflow1.xml)),
   JSON, or
   Python ([example](https://github.com/knipknap/SpiffWorkflow/blob/master/tests/SpiffWorkflow/data/spiff/workflow1.py)).
2. Run the workflow using the Python API. Example code for running the workflow:

```python
from SpiffWorkflow.specs import WorkflowSpec
from SpiffWorkflow.serializer.prettyxml import XmlSerializer
from SpiffWorkflow import Workflow

# Load the workflow specification:
with open('my_workflow.xml') as fp:
    serializer = XmlSerializer()
    spec = WorkflowSpec.deserialize(serializer, fp.read())

# Create an instance of the workflow, according to the specification.
wf = Workflow(spec)

# Complete tasks as desired. It is the job of the workflow engine to
# guarantee a consistent state of the workflow.
wf.complete_task_from_id(...)

# Of course, you can also persist the workflow instance:
xml = wf.serialize(XmlSerializer, 'workflow_state.xml')
```

## Documentation

Full documentation is here:

  http://spiffworkflow.readthedocs.io/en/latest/