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Open-source platform layer for AI agents in production
The main features of zenml-io/kitaru are: Workflow Orchestration, General Purpose Orchestration.
Projects with overlapping indexed features include: couler-proj/couler — Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows,… netflix/metaflow — Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of… 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… kubeflow/pipelines — This project is a containerized machine learning workflow engine and orchestrator designed to automate the end-to-end… prefecthq/prefect — Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as…
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 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
This project is a containerized machine learning workflow engine and orchestrator designed to automate the end-to-end lifecycle of machine learning models on Kubernetes clusters. It functions as an MLOps pipeline compiler that transforms a domain-specific language into structured specifications for portable and scalable deployment. The platform provides a multi-tenant environment with isolated namespaces and identity provider authentication. It distinguishes itself through a combination of container-based task isolation, strongly typed artifact management for data passing, and content-address