Open-source alternatives to Kitaru

This project is a containerized machine learning workflow engine and orchestrator designed to automate the end-to-end lifecycle of machine learning models on Kubernetes clusters. It functions as an MLOps pipeline compiler that transforms a domain-specific language into structured specifications for portable and scalable deployment. The platform provides a multi-tenant environment with isolated namespaces and identity provider authentication. It distinguishes itself through a combination of container-based task isolation, strongly typed artifact management for data passing, and content-address

Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It orchestrates complex sequences of containerized tasks by executing them as directed acyclic graphs, allowing for dependency management and parallel processing within a cluster. The system extends the native Kubernetes control plane to manage the full lifecycle of automated processes, from initial triggering to final resource cleanup. The platform distinguishes itself through its controller-pattern reconciliation, which continuously monitors workflow states to align them with desi

Mirror of Apache Oozie

This project is a Python workflow orchestration platform and programmatic data pipeline engine used to author, schedule, and monitor complex data pipelines. It functions as a directed acyclic graph manager and scheduler, allowing users to define data movement and transformation tasks as code to ensure precise execution order and maintainability. The platform distinguishes itself by treating workflows as code, enabling pipelines to be versioned and tested through a standard programming language. It utilizes a system of extensible operators to encapsulate integration logic and employs a templat

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

An open source platform for managing and analyzing biomedical big data

Azkaban is a distributed workflow manager and DAG-based job orchestrator designed as an enterprise batch processor. It serves as a Java-based workflow engine that schedules and executes complex job sequences across a cluster of executor servers, with specific functionality for managing big data workloads on Hadoop clusters. The system distinguishes itself through a distributed executor model that coordinates state via a shared database to ensure high availability. It employs a plugin-based architecture that allows for custom job types and system functionality extensions, including the ability

Event-driven scripting for Kubernetes

Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments

Open-source platform that unifies AI agent orchestration and workflow automation — autonomy and precision in one platform.

COPPER - a high performance Java workflow engine

DolphinScheduler is a distributed workflow orchestrator designed to manage and automate complex data processing pipelines. It functions as a data pipeline scheduler that coordinates multi-step tasks across distributed environments, ensuring reliable execution through defined dependencies and sequences. The platform utilizes a directed acyclic graph model to represent workflows, allowing users to define task relationships via a visual interface. It employs a master-worker architecture supported by a pluggable task plugin system, which enables the dynamic extension of task types without requiri

Conductor is a durable workflow engine designed to orchestrate complex, long-running business processes and autonomous agent loops. It functions as a stateful execution platform that persists the entire history of a process, ensuring that workflows remain reliable and recoverable across infrastructure failures, system restarts, and transient network errors. By managing task lifecycles, worker polling, and state transitions, it provides a centralized coordination layer for distributed systems. The platform distinguishes itself through its specialized support for AI agent orchestration, allowin

CloudSlang Language, CLI and Builder

Argo is a cloud native CI/CD platform and Kubernetes workflow engine. It functions as a container pipeline orchestrator and job scheduler, managing multi-step sequences of containers as jobs using directed acyclic graphs within a cluster. The system acts as a progressive delivery controller, reducing release risk through automated Canary and Blue-Green deployment strategies. It provides declarative GitOps synchronization to mirror the state of a git repository directly into the cluster environment for continuous delivery automation. The platform covers a broad range of capabilities including

【A common used C++ & Python DAG framework】 一个通用的、无三方依赖的、跨平台的、收录于awesome-cpp的、基于流图的并行计算框架。欢迎star & fork & 交流

Camunda is a business process automation platform and orchestration tool used to model, deploy, and monitor automated workflows and human-centric tasks. It features a BPMN workflow engine for executing processes based on the BPMN 2.0 standard and a DMN decision engine for automating business logic and rule evaluation. The platform is designed for flexibility through an embedded workflow library that allows the engine to be integrated directly into custom applications. It supports multi-tenant workflow management, enabling the secure separation of process definitions and instance data for mult

piper - a distributed workflow engine

Cylc: a general purpose workflow engine with a gift for cycling

Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative modeling and version-controlled code. It functions as a workflow engine that treats data assets as first-class primitives, allowing teams to define, schedule, and monitor complex pipelines while maintaining clear visibility into lineage, dependencies, and data quality. The platform distinguishes itself by using a code-as-configuration framework that enables standard software engineering practices, such as unit testing and local mocking, to be applied directly to data workflows.

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

Dapr is a distributed application runtime that provides a sidecar-based infrastructure layer for building resilient microservices and event-driven applications. By utilizing a sidecar proxy pattern, it abstracts complex infrastructure tasks into standardized, network-accessible APIs, allowing developers to focus on application logic while the runtime handles service discovery, state management, and secure communication. The platform distinguishes itself through a pluggable component architecture and language-agnostic design, enabling services written in any programming language to interact wi

The open core .NET job orchestrator that we've been missing

🎲 Dotflow turns an idea into flow! — Lightweight Python library for execution pipelines

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.