Flyte is a workflow orchestration engine that coordinates data, models, and compute for building AI pipelines in pure Python.
Flyte addresses the challenge of reliably orchestrating machine learning pipelines and agents at scale. It allows developers to define workflows declaratively in Python, handling the complexity of coordinating data processing, model training, and inference across distributed infrastructure. The tool abstracts away the operational details of scheduling, resource management, and fault tolerance, enabling teams to focus on the logic of their AI systems rather than infrastructure concerns.
Flyte suits teams building production machine learning systems who want to express workflows in Python without managing Kubernetes directly. It is particularly valuable for organizations running complex multi-stage pipelines involving data preparation, model training, and serving. The project emphasizes local development experience through a terminal user interface, allowing developers to test workflows locally before deployment. For those needing a production-ready backend today, an enterprise offering is available; the open source Kubernetes-native backend infrastructure is documented as coming soon.
The project maintains active engagement across multiple channels including Slack and GitHub Discussions, with a structured contribution process documented in the backend development guide. Development is organized around clear separation between the Python SDK and backend infrastructure, with the SDK repository maintained as a distinct component. The project is a graduated initiative of the LF AI & Data Foundation, indicating established governance and community oversight.
