
This is a major release, bringing significant performance improvements, as well as some key features moving out of experimental phase, plus full support for Spring Boot 2.0. Check the completely revised documentation for more details. The highlights are listed below.
Performance enhancements, read details here: https://www.flowable.com/blog/flowable-6-3-0-performance-benchmark/
Dynamic task and subprocess injection into running process instances is now fully supported
History can be configured on individual process definitions
A new ‘triggerable‘ service task that executes services externally and calls the engine when done
Support for a transaction-lifecycle based event listener
CMMN is now a fully supported engine
REST API support for all CMMN services and operations
Support for asynchronous service tasks, required rule, autocomplete, completion neutral, User event listeners and manual activation rules
Script task type has been added
Support for viewing and managing CMMN data in the Admin app
Support for collection expressions, such as IN and NOT IN
Improved decision table editor user experience
Support for viewing and managing decision executions in the Admin app
All apps have been fully updated to use Spring Boot 2.0. One property file is used for configuration
Support for expressions in the options fields, including dropdown, radio and hyperlink fields
Support for a password fields in the form editor and runtime
Multi–tenancy support in the Modeler by defining the active tenant in the Modeler property file
Available on GitHub.

Tools like ChatGPT can handle a variety of business tasks, automating nearly everything. And it’s true, GenAI really can do a wide range of tasks that humans do currently. Why not let business users work directly with AI then? And what about Agentic AI?

In this post, we continue our exploration of workflow complexity - learn how key metrics like activity count and control flow reveal natural groupings of models, making it easier to identify and improve overly complex designs.

In this post, we continue our exploration of workflow complexity - learn how key metrics like activity count and control flow reveal natural groupings of models, making it easier to identify and improve overly complex designs.