SpiffWorkflow
A powerful workflow engine implemented in pure Python
Install / Use
/learn @sartography/SpiffWorkflowREADME
SpiffWorkflow

Spiff Workflow is a workflow engine implemented in pure Python. It is based on the excellent work of the Workflow Patterns initiative. In 2020 and 2021, extensive support was added for BPMN / DMN processing.
Motivation
We created SpiffWorkflow to support the development of low-code business applications in Python. Using BPMN will allow non-developers to describe complex workflow processes in a visual diagram, coupled with a powerful python script engine that works seamlessly within the diagrams. SpiffWorkflow can parse these diagrams and execute them. The ability for businesses to create clear, coherent diagrams that drive an application has far reaching potential. While multiple tools exist for doing this in Java, we believe that wide adoption of the Python Language, and it's ease of use, create a winning strategy for building Low-Code applications.
Build status
Code style
Dependencies
We've worked to minimize external dependencies. We rely on lxml for parsing XML Documents, and that's it!
Features
- BPMN - support for parsing BPMN diagrams, including the more complex components, like pools and lanes, multi-instance tasks, sub-workflows, timer events, signals, messages, boudary events and looping.
- DMN - We have a baseline implementation of DMN that is well integrated with our Python Execution Engine.
- Python Workflows - We've retained support for building workflows directly in code, or running workflows based on a internal json data structure.
A complete list of the latest features is available with our release notes for version 1.0.
Code Examples and Documentation
Detailed documentation is available on ReadTheDocs Also, checkout our example application, which we reference extensively from the Documentation.
Installation
pip install spiffworkflow
Tests
pip install spiffworkflow[dev]
cd tests/SpiffWorkflow
coverage run --source=SpiffWorkflow -m unittest discover -v . "*Test.py"
Support
You can find us on Discord at https://discord.gg/BYHcc7PpUC
Commercial support for SpiffWorkflow is available from Sartography
Contribute
Pull Requests are and always will be welcome!
Please check your formatting, assure that all tests are passing, and include any additional tests that can demonstrate the new code you created is working as expected. If applicable, please reference the issue number in your pull request.
Credits and Thanks
Samuel Abels (@knipknap) for creating SpiffWorkflow and maintaining it for over a decade.
Matthew Hampton (@matthewhampton) for his initial contributions around BPMN parsing and execution.
The University of Virginia for allowing us to take on the mammoth task of building a general-purpose workflow system for BPMN, and allowing us to contribute that back to the open source community. In particular, we would like to thank Ron Hutchins, for his trust and support. Without him our efforts would not be possible.
Bruce Silver, the author of BPMN Quick and Easy Using Method and Style, whose work we referenced extensively as we made implementation decisions and educated ourselves on the BPMN and DMN standards.
The BPMN.js library, without which we would not have the tools to effectively build out our models, embed an editor in our application, and pull this mad mess together.
Kelly McDonald (@w4kpm) who dove deeper into the core of SpiffWorkflow than anyone else, and was instrumental in helping us get some of these major enhancements working correctly.
Thanks also to the many contributions from our community. Large and small. From Ziad (@ziadsawalha) in the early days to Elizabeth (@essweine) more recently. It is good to be a part of this long lived and strong community.
License
GNU LESSER GENERAL PUBLIC LICENSE
Related Skills
claude-opus-4-5-migration
107.8kMigrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5
model-usage
347.0kUse CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
TrendRadar
50.8k⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载,你的 AI 舆情监控助手与热点筛选工具!聚合多平台热点 + RSS 订阅,支持关键词精准筛选。AI 智能筛选新闻 + AI 翻译 + AI 分析简报直推手机,也支持接入 MCP 架构,赋能 AI 自然语言对话分析、情感洞察与趋势预测等。支持 Docker ,数据本地/云端自持。集成微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 等渠道智能推送。
mcp-for-beginners
15.8kThis open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
