Python-based Light-weight workflow engine.

Major BPMN engines (Camunda, jBPM, Activiti, Flowable) are Java-based and typically require substantial system resources for deployment and runtime (CPU, heap, disk), even for modest workloads. This can impact scalability and operational costs, especially in containerized or cloud settings. m8flow is resource-light!

Modern data, AI, and web development ecosystems substantially leverage Python and frameworks like Django, Flask, and FastAPI. Integrating Java BPMN engines with Python-centric data/ML workflows is complicated, often requiring middleware, REST bridging, or less portable solutions.

Java stack licensing from vendors such as Oracle has become more costly and difficult to predict—with recent shifts to employee-based pricing leading to sudden cost spikes even for smaller deployments. m8flow addresses risks like

• High or unpredictable license/maintenance fees
• Lack of transparency in future cost increases (inflation, tier jumps)
• Restrictions in access to source code, limiting customizations by client or partner
• Potential compliance exposure in cases of unclear licensing

• Traditional engines fit regulated environments due to features like audit trails and explainability. However, they are not positioned to take advantage of agentic workflows. Examples – a decision gateway cannot easily be customized to plug in an ML model, and a human task step cannot be easily augmented with AI assistance.
• Traditional BPMN solutions rely on Java/.NET based workflow engines that excel in regulated environments but require significant integration effort to incorporate modern AI capabilities.
• While these engines can integrate with ML models and AI services through APIs and custom extensions, they weren’t architected with agentic workflows as a primary use case, making AI integration more complex compared to newer, AI-native workflow platforms