6 best open source workflow engines for 2026

<p>A workflow engine platform executes complex processes involving humans, machines and IT systems to improve efficiency and meet compliance demands. The rise of cloud computing, microservices architectures, AI and agents has spurred the creation of open source workflow engines to automate multiple IT processes.</p> <p>These tools embrace different philosophies. Traditional business process management (<a href="https://www.techtarget.com/searchcio/definition/business-process-management">BPM</a>) approaches familiar to business teams can ensure compliance and optimize human processes associated with IT operations.</p> <p>Code-centric approaches familiar to developers and IT engineers can create, configure, manage and version more complex processes for spinning up infrastructure that spans multiple APIs, clouds, networks and databases using infrastructure-as-code (<a href="https://www.techtarget.com/searchitoperations/definition/Infrastructure-as-Code-IAC">IaC</a>) paradigms. Increasingly, open source workflow platforms are adding support for agentic processes.</p> <section class="section main-article-chapter" data-menu-title="What is an open source workflow engine?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What is an open source workflow engine?</h2> <p>The concept of open source workflow engines for IT operations dates to task automation scripts, followed by the cron command-line tool. Open source tools for runbook automation, such as Chef, Puppet and Ansible, later extended this concept for provisioning or rolling back code and infrastructure changes. Workflow engines can support more complex processes across multiple types of infrastructure in a specific sequence, sometimes requiring approvals or input from human experts or managers.</p> <p>They can streamline complex data pipelines for new AI infrastructure, provision services that span multiple cloud platforms or commission multiple services in a specific sequence. They also excel at rolling out new microservices infrastructure that requires provisioning and configuring numerous Docker containers and Kubernetes infrastructure in the appropriate order, and they can augment or <a href="https://www.techtarget.com/searchitoperations/tip/Tasks-to-automate-today-to-streamline-IT-operations">automate many recurring IT operations tasks</a>, including service provisioning, incident response, trouble ticket handling and disaster recovery.</p> <p>The most consequential recent shift in open source workflow automation has been the convergence of workflow orchestration and <a href="https://www.techtarget.com/searchenterpriseai/definition/agentic-AI">agentic AI</a>. Vendors and communities traditionally focused on tools that supported data pipeline schedulers, microservices orchestrators or runbook engines. Increasingly, these tools provide a durable execution layer that enables AI agents to withstand real-world failure modes. Also, they can provide the foundation for an agentic orchestration layer to help enterprises operationalize AI.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/building_an_it_automation_strategy-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/building_an_it_automation_strategy-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/building_an_it_automation_strategy-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/building_an_it_automation_strategy-f.png 1280w" alt="Graphic showing 10 steps in building an IT automation strategy." height="392" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>A critical step in building an automation strategy is identifying and evaluating a suitable tool set for orchestrated workflows. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <p>For example, Apache Airflow 3.0 added a Common AI Provider with large language model (LLM) and AI agent operators across more than 20 model providers, plus event-driven scheduling and directed acyclic graphs (DAG) versioning. Orkes, which maintains Conductor, has released Agentspan, an open source runtime layer that runs on top of Conductor and a model context protocol (MCP) gateway that exposes internal APIs as callable tools.</p> <p>Camunda, traditionally framed as a business process model and notation (BPMN) process engine, has added agentic orchestration capabilities that blend deterministic BPMN process logic with non-deterministic agent behavior. The Dagster asset-aware model is being extended to operationalize machine learning (ML) artifacts, training data sets and feature pipelines. There has also been progress on hybrid models in which the control plane is hosted, but execution stays in the customer's environment to support AI workloads with data residency or compliance constraints.</p> </section> <section class="section main-article-chapter" data-menu-title="Top open source workflow engines: Quick comparison"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Top open source workflow engines: Quick comparison</h2> <p>Several open source workflow engine tools are available. The six detailed here were selected based on their popularity on GitHub and their support for novel IT operations automation capabilities. Some of these platforms <a target="_blank" href="https://github.com/meirwah/awesome-workflow-engines" rel="noopener">received at least 10,000 stars</a> on GitHub. It's also important to note that although most of these projects began life with a permissive open source license, some have adopted less permissive licensing schemes with recent releases.</p> <h3>1. Apache Airflow</h3> <p>Apache Airflow enables teams to programmatically author, schedule and monitor workflows using Python scripts. The platform was developed by Airbnb in 2014 and contributed to the open source community in 2016. Airflow pioneered the use of DAGs to define dependencies in complex automation, offering greater flexibility compared to traditional scripting approaches.</p> <p>IT operations teams use the platform to track workflow status and troubleshoot issues. Airflow can provision, backup and restore cloud infrastructure; automate system health checks; streamline patch management; conduct security audits; and collate IT compliance documentation. Apache Airflow 3.0, released in April 2025, was the largest release in the project's history with unified orchestration enhancements for data and AI infrastructure.</p> <ul class="default-list"> <li><b>Key features:</b> DAG versioning; event-driven scheduling; a task SDK; and task isolation.</li> <li><b>Best for:</b> Orchestrating data and ML pipeline across cloud data platforms, coordinating IT compliance documentation processes, system health checks and security audits.</li> <li><b>Pros:</b> Large ecosystem, Apache Foundation neutrality and a mature managed services market.</li> <li><b>Cons:</b> Only Python support for most features, and it can be <a href="https://www.techtarget.com/searchitoperations/tip/Scalable-IT-infrastructure-Balancing-speed-with-stability">complex to scale</a>.</li> </ul> <h3>2. Argo Workflows</h3> <p>Argo Workflows is a container-native workflow engine that supports complex provisioning and management processes on Kubernetes for CI/CD, data processing and ML model training processes. Like Airflow, it supports DAGs. IT engineers, developers and data scientists can define workflows using YAML files, making them easy to manage and version-control.</p> <p>The platform is a good fit for deploying applications, setting up data processing pipelines, backing up and restoring processes and monitoring complex infrastructure. It is backed by the Cloud Native Computing Foundation (CNCF), which brings a measure of governance assurance for enterprise projects.</p> <ul class="default-list"> <li><b>Key features:</b> YAML-defined workflows that run on Kubernetes; cloud-native artifacts across AWS, Azure and Google Cloud Platform; SDKs for Java, Go, Python and TypeScript.</li> <li><b>Best for:</b> Kubernetes-based IT automation that <a href="https://www.techtarget.com/searchapparchitecture/tip/Pipeline-as-Code-Managing-CI-CD-complexity-and-sprawl">supports CI/CD pipelines</a>, ML training pipelines, IaC orchestration and batch data processing.</li> <li><b>Pros: </b>Strong Kubernetes integration, CNCF support and a strong enterprise community.</li> <li><b>Cons:</b> Limited usefulness outside of Kubernetes.</li> </ul> <h3>3. Camunda</h3> <p>Camunda was historically a legacy BPM vendor with a thriving open source community around its core offerings and open source tool of the same name. In 2019, the company rolled out Zeebe to orchestrate microservices at scale while also providing visibility for various stakeholders. The platform provides strong support for BPMN tooling, which can facilitate collaboration across business, finance, security and IT operations teams.</p> <p>Zeebe supports event streaming and a distributed architecture that improves scalability and resilience compared to traditional BPM workflow engines, with complementary tools supporting modeling, implementing, connecting, monitoring and optimizing workflows.</p> <p>Zeebe is a good candidate for workflows that span multiple human and IT services, such as trouble ticket response and employee onboarding. Starting in 2024, licensing terms for new components required paid enterprise licenses for production use. Recent updates have focused on <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">agentic orchestration</a> and Camunda Copilot for working with BPMN diagrams.</p> <ul class="default-list"> <li><b>Key features:</b> Strong BPMN support and connectors for AI agents, vector databases and MCP.</li> <li><b>Best for:</b> Scenarios requiring strong compliance and approval processes in telecommunications, banking, insurance and healthcare.</li> <li><b>Pros:</b> Native BPMN 2.0 support, hybrid LLM and deterministic workflows, and extensive tools for business users.</li> <li><b>Cons:</b> Might be less flexible than alternative code-based approaches.</li> </ul> <h3>4. Conductor</h3> <p>Conductor was open sourced by Netflix in 2016 and helped the streaming service achieve rapid growth. Netflix discontinued stewarding the project in 2023, and the original engineering team founded Orkes and took over stewardship. Conductor supports a variety of workflows for IT operations use cases, including cloud infrastructure provisioning, cloud security scanning, CI/CD pipeline orchestration, human-driven processes, BPMN workflows and data protection workflows.</p> <p>The platform is Apache 2.0-licensed and language-agnostic by design, with workflows that can run for months or even years and pause for arbitrary durations. Orkes recently released Agentspan, a durable runtime for AI agents that runs on Conductor and an <a href="https://www.techtarget.com/searchsoftwarequality/news/366634681/MCP-OAuth-update-adds-security-for-personalized-AI">MCP gateway, allowing agents to call tools</a> using an API. It also added a Prompt-to-Workflow capability for writing executable workflows in natural language.</p> <ul class="default-list"> <li><b>Key features:</b> Language- and framework-agnostic; visual workflow design with code editor support; multiple workflow primitives that support cron jobs; event-driven and message queue triggers.</li> <li><b>Best for:</b> Running microservices at scale, media encoding pipelines and managed agentic processes.<b> </b></li> <li><b>Pros:</b> Resilience at scale and growing agentic and AI support.</li> <li><b>Cons:</b> Lacks an external governance structure of a neutral open source organization.</li> </ul> <h3>5. Dagster</h3> <p>Dagster is emblematic of an open source workflow engine designed from the ground up to support complex data pipeline requirements for extract, transform and load (ETL) as well as data science, ML and AI workflows. Released in 2019, the platform defines data applications using a graph of functional computations for producing and consuming data assets. Different roles can use a variety of tools to create and manage pipelines, including Spark, SQL and Python, while collaborating on data infrastructure.</p> <p>These tools can help multiple roles -- such as infrastructure teams, developers, <a href="https://www.techtarget.com/searchenterpriseai/definition/data-scientist">data scientists</a> and data engineers -- visualize, configure, develop, test and monitor complex data pipelines in production. Dagster supports capabilities similar to those of Apache Airflow, but it also improves visibility into data lineage, understands dependencies and facilitates more granular data compliance-related workflows. Dagster Components, introduced in late 2025, simplifies the development and deployment of workflow artifacts.</p> <ul class="default-list"> <li><b>Key features:</b> Orchestration using software-defined assets; strong lineage and metadata tracking.</li> <li><b>Best for:</b> Organizations looking to simplify data workflows in a governed and integrated platform.</li> <li><b>Pros: </b>Asset-centric model simplifies visibility for data workflows.</li> <li><b>Cons:</b> The asset model might also complicate some ETL workflows.</li> </ul> <h3>6. Kestra</h3> <p>Kestra is a declarative open source orchestration platform started in 2021 by Emmanuel Darras and Ludovic Dehon in Paris. The platform takes a YAML-first approach to workflow definition. This facilitates collaboration across code editors and version-controlled Git artifacts to streamline reviews, audits and version control.</p> <p>Tasks can be specified in <a href="https://www.techtarget.com/searchapparchitecture/tip/A-beginners-guide-to-learning-new-programming-languages">various languages</a> -- including Python, Node.js, R, Go and Shell -- and extended using over 1,300 plugins that support common workflow patterns across cloud platforms, databases, SaaS systems and IT operations tools. It provides a unified orchestration control plane across data pipelines, AI workflows, infrastructure automation and business processes.</p> <ul class="default-list"> <li><b>Key features: </b>Declarative YAML workflows through graphical tools; AI copilot for writing workflows in natural language; support for AI agents with components for calling LLMs, managing memory and developing MCP clients.</li> <li><b>Best for:</b> Providing a single control plane across data, AI, infrastructure and business processes.</li> <li><b>Pros:</b> A good fit for standardizing IT processes across domains.</li> <li><b>Cons:</b> Younger than some of the others on the list.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="How to choose the right tool for your organization"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How to choose the right tool for your organization</h2> <p>Open source workflow engines embrace various paradigms for thinking about and managing workflows that must be considered from the start. For example, programmers might prefer the declarative approach adopted by tools such as Airflow and Dagster, which simplify the creation and modification of workflows at high granularity, but these tools can be harder for security and compliance teams to manage.</p> <figure class="main-article-image half-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/key_factors_when_selecting_open_source_workflow_engines-h.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/key_factors_when_selecting_open_source_workflow_engines-h_half_column_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/key_factors_when_selecting_open_source_workflow_engines-h_half_column_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/key_factors_when_selecting_open_source_workflow_engines-h.png 1280w" alt="Graphic listing the many features of open source workflow engines."> <figcaption> <i class="icon pictures" data-icon="z"></i>Several features determine the selection of an open source workflow engine. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <p>Compliance and operations teams might prefer the durable execution approach embraced by platforms such as Conductor and Camunda's Zeebe, which can simplify troubleshooting and enhance auditability and controls. Organizations with extensive Kubernetes experience might prioritize platforms such as Argo that bring a <a href="https://www.techtarget.com/searchitoperations/tip/Kubernetes-automation-Use-cases-and-tools-to-know">Kubernetes-native approach</a> to workflow design and management.</p> <p>Also, the open source ethos lets teams shortlist workflow engines for specific processes. Conductor, for example, was designed from the outset to improve media-processing workflows and has gradually expanded to support other use cases. Data management teams are increasingly turning to these tools as ML and data science processes are integrated into more workflows running in production, and many of these engines can help extend governance, management and resilience as they scale.</p> </section> <section class="section main-article-chapter" data-menu-title="Getting started: Implementation considerations"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Getting started: Implementation considerations</h2> <p>The open source nature of these tools means that it's easy to get started implementing or improving a new workflow. A pitfall is that this can introduce new challenges as the workflow scales or must be maintained over time. A workflow that starts life as a one-off project can have long-term maintenance and governance implications.</p> <p>Thus, it doesn't hurt to at least consider what might be required to bring operational discipline to consistently maintain the workflow at scale over time. A best practice is to consider how to extend IaC processes to support workflows and tasks that can be versioned, tested and rolled back when problems arise.</p> <p>It's also early days for some of the new agentic workflows. These tools can certainly fill in the cracks left by traditional deterministic workflows, but they can also introduce new problems. This might require thinking about new approaches to monitoring them in production to detect problems early and exploring how they might break on simple workflows. It can also be helpful to learn from others' experiences as they <a href="https://www.techtarget.com/searchcio/feature/Agentic-ai-in-practice-lessons-from-real-deployments">explore how these new agentic workflows</a> can safely scale in production.</p> <p><i>George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.</i></p> </section>