DMN Engine is a decision engine (rule engine) allowing to execute and evaluate the decisions defined in a DMN model. Its primary target is to evaluate the decision tables that transform the inputs into the output(s) using the decision rules.
Power Flows DMN - Powerful decisions and rules engine
An implementation of DMN (Decision Model Notation) in Python
bkflow-feel 是一款基于 Python 的 FEEL (Friendly Enough Expression Language) 语法解析器，用于对 FEEL 语法表达式进行解析和运算，得到对应的 Python 对象作为计算结果。
A central repository for DMN based decision services
An example of how to use the stx-dmn-eval-js library, the dmn engine for node.js
LogicForge is a simplified system that allows users to define, execute, and manage business rules and complex events in a declarative and efficient way. It provides a rule engine, a DMN engine, and a CEP engine that can handle various types of logic and data.
OrchesT is a scalable, distributed workflow orchestration platform for executing BPMN 2.0 and DMN 1.3 definitions. It provides a Kafka-driven engine with Spring Boot client integration, MongoDB persistence, and a full REST API for managing process lifecycles.
Official Python SDK for QuantumBPM: async BPMN 2.0 workflow orchestration, DMN 1.5 decision evaluation with FEEL, and external job workers.
Official Java SDK and Spring Boot starter for QuantumBPM: BPMN 2.0 workflow orchestration, DMN 1.5 decision evaluation, and virtual-thread job workers.
An Azure implementation of DecisionCentral - a central repository for DMN decision services
Official Go SDK for QuantumBPM: BPMN 2.0 workflow orchestration, DMN 1.5 decision evaluation with FEEL, and external job workers.
Official TypeScript/JavaScript SDK for QuantumBPM: BPMN 2.0 workflow orchestration, DMN 1.5 decision evaluation with FEEL, and job workers.
An implementation of DMN (Decision Model Notation) in Python
Sutra is a Rust-native, message-native workflow engine built on BPMN 2.0.
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