Workflow orchestration engine

Cadence is a distributed workflow orchestration engine designed to execute long-running, asynchronous business logic with built-in durability and resilience across distributed systems. It functions as a stateful process manager that ensures processes resume from their last known state following system crashes or network outages. The platform utilizes a distributed task queue to manage work across independent worker nodes and supports persistence via SQL or Cassandra backend storage. It includes a workflow visualization dashboard for inspecting execution histories and state traces, alongside a

imove is a visual workflow engine and low-code logic designer that allows for the creation of executable business rules via flowcharts. It functions as an event-driven process orchestrator, triggering specific logical sequences based on user interface interactions or external network signals. The platform operates as a node-based automation system where data is processed through a chain of discrete, parameterizable code modules. It converts graphical flowchart representations into executable logic steps for both server and client-side processing. The system supports modular logic engineering

Windmill is an internal developer platform and workflow orchestration engine designed to automate complex business processes and data pipelines. It functions as a distributed serverless runner that executes multi-language scripts within isolated, containerized environments, allowing teams to chain discrete tasks into directed acyclic graphs. The platform distinguishes itself through a Git-centric approach to infrastructure, where system state and workflow definitions are synchronized directly from version control. It features a metadata-driven input system that automatically generates user in

Kestra is a declarative workflow orchestrator designed to manage complex task dependencies and automated processes through versioned configuration files. It functions as a distributed platform that decouples task scheduling from execution by offloading computational workloads to a fleet of worker nodes. The system uses a reactive, event-driven engine to initiate workflows automatically in response to external signals, webhooks, schedules, or file system changes. The platform distinguishes itself through a modular plugin architecture that allows for the integration of custom tasks and external

This project provides a containerized environment for deploying Apache Airflow, enabling the orchestration of complex data pipelines and automated task scheduling. By packaging the orchestration platform into portable images, it ensures consistent execution across diverse infrastructure setups and simplifies the management of runtime dependencies. The platform facilitates distributed task execution by decoupling the scheduler from the execution layer, allowing for horizontal scaling of processing power across multiple worker nodes. It supports dynamic configuration through environment variabl

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

Light Task Scheduler is a distributed job scheduling and workflow orchestration platform designed for managing background processing across scalable computing environments. It functions as a cluster management system that coordinates stateless nodes to execute recurring, cron-based, or one-time tasks with centralized control and high availability. The platform distinguishes itself through a leader-based coordination model that automatically elects a primary controller to manage task distribution and system state. It supports complex workflow dependencies, ensuring that prerequisite tasks comp

Trigger.dev is a platform for building durable, event-driven background workflows. It functions as a workflow engine that allows developers to define complex, long-running processes using standard code rather than proprietary configuration languages. By utilizing a durable execution model, the system checkpoints progress, ensuring that tasks can automatically resume from the exact point of failure after a crash or interruption. The platform distinguishes itself through its focus on stateful, multi-step automation and real-time feedback. It supports the orchestration of AI agents and external

Activepieces is an open-source, self-hosted workflow automation platform designed to connect third-party applications through modular triggers and actions. It provides a low-code integration framework that allows users to build, manage, and execute complex business logic sequences within isolated, sandboxed environments. The platform distinguishes itself through its focus on embeddability and enterprise-grade security. It features an embedded automation builder that can be integrated into external applications via iframes, supported by comprehensive identity and access management tools such a

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

ClaraVerse is a self-hosted orchestration platform for deploying and managing local language models, autonomous agents, and automated workflows on private infrastructure. It functions as a containerized backend manager that orchestrates services, databases, and model providers within local containers to maintain data sovereignty. The platform features a visual workflow builder with a drag-and-drop interface for designing complex parallel task sequences. It utilizes a multi-model abstraction layer to normalize interactions across diverse local and remote AI endpoints and includes a retrieval a

Illa-builder is a low-code internal tool builder and API integration platform used to create business applications and admin panels. It functions as a database GUI dashboard and visual workflow automator, allowing users to connect to databases and external APIs to manage data and automate business processes. The platform provides a self-hosted app framework that can be deployed on private infrastructure via Docker. It enables the creation of custom dashboards and CRMs while maintaining full control over data and hosting. The system includes a visual drag-and-drop canvas for designing user in

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 distributed workflow manager and microservices orchestration engine. It coordinates long-running business processes across a network of services, using a stateful orchestration model to ensure consistent execution order and reliable state management. The system defines task sequences and conditional business logic using structured JSON configuration files. It manages the execution of complex operations across distributed systems with built-in persistence and failure recovery, tracking the progress of processes through a backend database. The platform provides capabilities for

Apache NiFi is a flow-based programming platform that enables the visual design, monitoring, and management of data pipelines. At its core, it provides a web-based visual dataflow designer where users build directed graphs of processors to route, transform, and mediate data movement between any source and destination without writing custom code. The system records fine-grained data provenance for every data item from ingestion to delivery, supporting audit, debugging, and replay of data lineage. The platform distinguishes itself through a zero-master cluster architecture that distributes proc

OpenWhisk is a serverless cloud platform designed for deploying and executing stateless functions in response to API calls or events. It serves as a complete serverless stack, providing an API gateway for functions, a function-as-a-service runtime manager, and an event-driven workflow engine. The platform distinguishes itself through a polyglot execution model that supports multiple language runtimes and allows for the creation of custom runtimes using Docker containers. It enables complex logic through function orchestration and composition, allowing multiple functions to be chained into seq

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

Hatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it

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

Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer for streaming data via WebSockets, SSE, gRPC, and WebTransport, alongside a multi-agent orchestration framework for managing agent fleets through sequential, parallel, and graph-based execution workflows. The project features a durable workflow engine that persists agent state as snapshots, allowing long-running tasks to survive system restarts and incorporate human-in-the-loop approvals. It also implements Model Context Protocol servers to expose tools, resources, and prompt

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo

Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep

OpenManus is an autonomous agent framework designed to build intelligent software entities capable of executing complex, multi-step tasks through independent decision-making. It functions as a workflow orchestration engine that uses a central language model to interpret user goals, break them down into actionable steps, and manage the execution flow of agents. The system maintains coherence across tasks through a stateful execution context that tracks progress and intermediate data. The platform distinguishes itself through a dynamic capability discovery mechanism that inspects tool definitio

This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into structured dialogue sequences between specialized agents. The system distinguishes itself through its message-passing orchestration, which manages state transitions and task delegation between independent participants. It supports dynamic conversation state management to provid

LangGraph is a framework for building stateful, multi-step agentic workflows by modeling application logic as a directed graph. It provides a runtime environment where complex tasks are orchestrated through interconnected nodes and edges, allowing developers to manage state transitions, persistent memory, and control flow across long-running automated processes. The platform distinguishes itself through its native support for human-in-the-loop automation, enabling developers to define breakpoints that pause execution for manual review, modification, or approval. It also features checkpoint-ba

MetaGPT is an agentic workflow engine and multi-agent orchestration framework designed to automate complex software engineering and data analysis tasks. It functions as an automated software factory that transforms high-level natural language requirements into functional web applications, technical documentation, and production-ready code. By utilizing a runtime environment that manages the lifecycle of specialized agents, the platform bridges the gap between user intent and finished software components. The system distinguishes itself through role-based agent orchestration and dynamic task d

Semantic Kernel is an artificial intelligence orchestration framework designed to integrate large language models with existing codebases. It functions as an agentic workflow engine, providing a standardized interface that connects generative models to traditional application logic, data sources, and external tools to automate complex, multi-step business tasks. The platform distinguishes itself through a modular plugin architecture and a planner-based reasoning engine that decomposes high-level goals into executable sequences of functions. By utilizing a connector-based abstraction layer, it

Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t

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

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.

Kedro is a data science pipeline framework and orchestration tool designed to build reproducible and modular data engineering workflows. It functions as an MLOps project template and Python data workflow tool that enforces software engineering best practices to move projects from prototype to production. The system distinguishes itself through a centralized data catalog manager that abstracts data access and versioning across various file formats and cloud storage systems. It further separates processing logic from data access via a lazy-loading data registry and provides a standardized proje

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

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

Mirror of Apache Oozie

This project is a JavaScript-based workflow engine designed to execute business process models defined in standard XML. It functions as a stateful orchestrator that manages the lifecycle of complex processes by moving virtual tokens through tasks, gateways, and events. The engine provides a runtime environment for automating business logic, integrating external services, and coordinating human tasks within Node.js or browser-based applications. The engine distinguishes itself through its high degree of extensibility and state management capabilities. It allows developers to inject custom beha

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r

Keep is an open-source AIOps alert management platform that aggregates, deduplicates, and orchestrates the lifecycle of alerts from multiple monitoring tools. It functions as a multi-provider integration hub to centralize the flow of data between observability, ticketing, and communication tools. The platform distinguishes itself through incident workflow automation and AI-powered enrichment. It uses a declarative workflow engine to execute multi-step operational sequences and integrates large language models to summarize event data and correlate technical logs for faster incident resolution.

This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents. It functions as a development framework that manages the end-to-end lifecycle of complex, multi-step tasks, including persona definition, persistent memory management, and the execution of automated coding workflows. By acting as a Model Context Protocol server, it enables standardized communication between development tools and external AI models. The platform distinguishes itself through an event-driven architecture that routes typed messages between agent personas, allowin

The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit