30 open-source projects similar to materialsproject/fireworks, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Fireworks alternative.
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
Flyte is a distributed machine learning pipeline manager and MLOps workflow engine. It functions as a Kubernetes-native orchestrator used to coordinate data, models, and compute resources for executing machine learning pipelines and autonomous agents at scale. The platform provides specialized infrastructure for the full machine learning lifecycle, including a dedicated model serving platform to deploy trained models as scalable production-ready inference services. It also enables the coordination and state management of autonomous AI agents. The system manages scalable pipeline execution th
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
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
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
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
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
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.
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
【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
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
COPPER - a high performance Java workflow engine
The open agent control plane. Govern autonomous AI agents with pre-execution policy enforcement, approval gates, and audit trails. Works with LangChain, CrewAI, MCP, and any framework.
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
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.
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
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
Workflows for Fission: Fast, reliable and lightweight function composition for serverless functions
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