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The Fireworks Workflow Management Repo.
The main features of materialsproject/fireworks are: General Purpose Orchestration, Workflow Platforms.
Open-source alternatives to materialsproject/fireworks include: inngest/inngest — Inngest is a durable execution framework and event-driven automation engine designed to orchestrate background… lyft/flyte — Flyte is a distributed machine learning pipeline manager and MLOps workflow engine. It functions as a… windmill-labs/windmill — Windmill is an internal developer platform and workflow orchestration engine designed to automate complex business… agnostiqhq/covalent — Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute… apache/incubator-airflow — This project is a Python workflow orchestration platform and programmatic data pipeline engine used to author,… apache/dolphinscheduler — DolphinScheduler is a distributed workflow orchestrator designed to manage and automate complex data processing…
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
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
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
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.