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Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.
The main features of aqueducthq/aqueduct are: Workflow Orchestration.
Projects with overlapping indexed features include: argoproj/argo-workflows — Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It… cordum-io/cordum — The open agent control plane. Govern autonomous AI agents with pre-execution policy enforcement, approval gates, and… couler-proj/couler — Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows,… dagworks-inc/hamilton — Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode… dotflow-io/dotflow — 🎲 Dotflow turns an idea into flow! — Lightweight Python library for execution pipelines. argoproj/argo — Argo is a cloud native CI/CD platform and Kubernetes workflow engine. It functions as a container pipeline…
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
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.
Argo is a cloud native CI/CD platform and Kubernetes workflow engine. It functions as a container pipeline orchestrator and job scheduler, managing multi-step sequences of containers as jobs using directed acyclic graphs within a cluster. The system acts as a progressive delivery controller, reducing release risk through automated Canary and Blue-Green deployment strategies. It provides declarative GitOps synchronization to mirror the state of a git repository directly into the cluster environment for continuous delivery automation. The platform covers a broad range of capabilities including