Open Source Workflow Engines
A workflow engine coordinates a sequence of tasks, decisions, and data changes so that processes run consistently and can respond to events or conditions. It can automate business approvals, connect services, schedule jobs, or orchestrate parallel computing tasks. By making dependencies and execution state explicit, workflow engines help teams reduce manual handoffs, track progress, handle failures, and repeat complex processes reliably.
Open source options range from visual business-process automation tools to code-first orchestrators for data, containers, and AI tasks. When choosing one, consider process-modeling support, deployment requirements, scalability, integrations, licensing, documentation, and maintenance activity. Also check how it handles retries, monitoring, version changes, and human review. These tools are useful to developers, operations teams, and organizations seeking to automate processes while retaining control over their infrastructure and workflows.
2 repositories · updated September 25, 2026

Graphon: A Python Graph Execution Engine for Agentic AI Workflows
Graphon is an innovative Python-based graph execution engine designed for building agentic AI workflows. It provides a robust framework for orchestrating complex AI tasks, featuring event-driven execution, graph validation, and shared runtime state. This evolving repository already includes a functional engine, built-in nodes, and end-to-end examples for developers.

Argo Workflows: A Cloud-Native Workflow Engine for Kubernetes
Argo Workflows is an open-source, container-native workflow engine designed for orchestrating parallel jobs on Kubernetes. It allows users to define multi-step workflows where each step is a container, modeling dependencies using directed acyclic graphs (DAGs). This CNCF graduated project is ideal for machine learning pipelines, data processing, and CI/CD.