Projects sharing features with Argo Workflows

Pipeline is a Kubernetes native CI/CD framework and cloud native pipeline orchestrator. It functions as a custom resource controller that translates declarative pipeline definitions into coordinated pod executions and managed workloads. The system acts as a containerized task runner, allowing for the execution of standalone build steps and reusable tasks that process specific inputs to produce defined outputs. It enables the orchestration of complex workflows by running a sequence of independent containers as modular components within a cloud environment. The platform covers automated softwa

Crossplane is a Kubernetes-based control plane framework that functions as a cloud resource orchestrator and infrastructure-as-code platform. It enables the management of heterogeneous infrastructure by extending the Kubernetes API to provision and maintain external cloud services through declarative configuration. By utilizing custom resource controllers, it continuously reconciles the state of external infrastructure with defined desired states, ensuring consistent deployment and lifecycle management across multiple cloud providers. The platform distinguishes itself through its composition-

Inngest is a durable execution framework and event-driven automation engine designed to orchestrate background workflows. It enables developers to build resilient, stateful processes by memoizing function steps, ensuring that long-running tasks can automatically resume from the last successful operation after failures, timeouts, or infrastructure restarts. The platform distinguishes itself through its event-driven architecture, which uses a schema-validated bus to trigger functions and coordinate complex, multi-step logic. It employs an onion-model middleware approach for cross-cutting concer

Argo CD is a declarative, GitOps-based continuous delivery tool designed for Kubernetes. It functions as a centralized control plane that synchronizes application states from version-controlled repositories directly into target clusters, ensuring that the live environment consistently matches the desired configuration defined in Git. The platform distinguishes itself through its ability to manage multi-cluster deployments from a single interface, providing unified oversight across distinct computing environments. It employs a controller-based reconciliation loop to continuously monitor for co

Elsa Core is a workflow engine framework designed for defining, executing, and managing long-running business processes. It functions as a distributed workflow orchestrator and event-driven trigger system, capable of operating as a multi-tenant platform with secure data isolation. The project distinguishes itself through a flexible approach to workflow definitions, supporting a visual drag-and-drop designer, programmatic C# definitions, and portable JSON specifications. It provides a highly extensible architecture allowing for the development of custom activities and the use of a dynamic expr

Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl

Osmedeus is a security workflow orchestration engine that coordinates AI agents, shell commands, and scanning tools through declarative YAML pipelines. It functions as a distributed security scanner, a declarative workflow automator, and an AI agent framework for security, enabling automated multi-step security analysis with conditional branching, parallel execution, and distributed workers. The engine distinguishes itself through a hybrid runner model that executes workflow steps on the local host, inside Docker containers, or over SSH to remote machines, selected per step or module. It supp

dbt-core is a command-line framework for transforming data within a warehouse using modular SQL and version control. It functions as a data transformation engine that enables users to define data structures and business logic through declarative configuration files, which the system then compiles into executable code. By managing complex data dependencies through a directed acyclic graph, it ensures that transformation tasks execute in the correct order while maintaining a manifest-driven state to track lineage and execution history. The project distinguishes itself through an adapter-based d

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

Airflow is a platform for programmatically authoring, scheduling, and monitoring complex data pipelines. It functions as a workflow automation engine that manages the lifecycle of recurring business processes by executing code-defined task dependencies. By representing workflows as directed acyclic graphs, the system ensures that task execution order and data flow are explicitly defined and reliably maintained across distributed computing environments. The platform distinguishes itself through a highly modular, provider-based architecture that decouples core orchestration logic from external

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

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

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

Flyte is a Kubernetes-based machine learning orchestrator and containerized pipeline manager designed for coordinating AI workflows and data pipelines. It functions as an engine for defining and executing resilient pipelines, utilizing a data lineage tracker to maintain immutable execution states and ensure reproducible outputs. The platform distinguishes itself by packaging individual tasks into separate containers to ensure dependency isolation and environment consistency. It provides specialized capabilities for machine learning, including the transformation of trained models into scalable

Orchest is a data pipeline orchestrator and containerized workflow manager. It provides a platform for designing, scheduling, and executing complex data processing sequences through a combination of a graphical interface and scripting. The platform distinguishes itself by using containers to manage software dependencies, ensuring consistent execution across different environments. It features a polyglot task scheduler capable of triggering jobs written in multiple programming languages and includes a version control system that tracks historical snapshots of project configurations and code.

Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It

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

Temporal is a distributed workflow orchestration engine designed to manage fault-tolerant, stateful, and long-running background processes. It functions as a platform for coordinating complex cross-service operations, ensuring consistency and reliability in distributed environments by decoupling workflow orchestration from task execution. The platform distinguishes itself through a deterministic, event-sourced execution model that reconstructs workflow state by re-executing code from an immutable event log. This approach isolates non-deterministic side effects into managed activities, allowin

This project is a Go language library that provides a programmatic interface for interacting with the Kubernetes API server. It serves as a client for managing cluster resources, offering both typed interfaces for compile-time safety and dynamic interfaces for unstructured data and custom resource management. The library includes a controller framework designed for building event-driven automation. This framework utilizes informers to maintain local resource caches and rate-limited work queues to decouple event detection from state reconciliation. High availability is supported through a lead

Kube-rs is a Rust client library and runtime for interacting with the Kubernetes API server. It provides a type-safe interface for managing cluster resources and state, including a toolkit for defining custom resource definitions and a framework for building admission webhooks to validate or mutate API requests. The project distinguishes itself through a dedicated controller runtime that implements event-driven reconciliation loops to align actual cluster state with desired configurations. It utilizes a streaming system to monitor resource changes and synchronize remote states into a local qu

The Operator SDK is a framework for building, packaging, and managing custom controllers that extend the Kubernetes API. It serves as a toolset for defining new API types and implementing reconcile loops to automate the lifecycles of complex applications. The project provides specialized support for creating operators based on Helm charts or Ansible playbooks, allowing users to maintain a desired cluster state using existing automation tools. It includes a dedicated system for packaging controllers into standardized container image bundles for distribution via the Operator Lifecycle Manager.

Kubebuilder is a framework and set of scaffolding tools used to build Kubernetes APIs and controllers. It functions as an operator framework that provides generators for custom resource definitions, admission webhooks, and RBAC manifests to extend cluster functionality. The project distinguishes itself through marker-based code generation, which parses source code comments to automatically produce Kubernetes manifests and boilerplate logic. It employs a hub-and-spoke versioning model to translate data between multiple API versions and uses a three-way merge strategy to automate project migrat

This repository is a comprehensive educational resource covering the full spectrum of DevOps practices, including continuous integration and delivery, containerization, orchestration, configuration management, and infrastructure as code. It provides structured content that walks through automating the build, test, and deployment lifecycle using multi-stage pipelines with Jenkins and Azure DevOps. The material covers declarative server configuration and state management through automated Ansible playbooks, enabling consistent system environments across machines. It also addresses container lif

release-it is a Git release automation tool designed to coordinate software versioning, changelog generation, and package publishing. It functions as a semantic versioning manager that increments project versions and updates configuration files based on semantic standards or custom schemes. The project distinguishes itself through a plugin-based extension system that allows for custom versioning and publishing logic. It supports complex project structures via monorepo versioning automation to synchronize internal dependencies across multiple workspaces. The tool covers a broad range of capab

Komodo is a remote server orchestrator and container deployment platform. It provides a centralized interface for managing multiple remote hosts through lightweight agents, coordinating Docker Swarm and Kubernetes clusters, and automating software delivery via integrated CI/CD pipelines. The system distinguishes itself with a TypeScript-based automation engine that executes typed scripts against the system API for complex operational workflows. It supports infrastructure-as-code through TOML-based declarative configuration synchronization and provides ephemeral build infrastructure that provi

Sealed Secrets is a Kubernetes secret encryption tool and controller designed for GitOps security. It provides a mechanism to encrypt sensitive data into specialized resources that can be safely stored in public version control systems and decrypted only within a cluster. The system uses an asymmetric encryption manager to seal secrets with a public key, ensuring that only the corresponding private key held within the cluster can unseal them. It includes utilities for security key rotation, secret re-encryption, and offline private key recovery to maintain data access during disaster recovery