flyteorg/flyte

Health v2 · maintenance only

Adoption not part of the score

7245 stars · 877 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Flyte is an open-source workflow orchestration platform for coordinating data, ML models, and AI agents at scale, authored in pure Python and running on Kubernetes. It provides durable execution with retry, caching, checkpointing, and recovery from infra or code failures, plus a CLI and web UI.

Use cases

• orchestrate machine learning training pipelines
• run durable AI agents with observability
• schedule and retry data processing workflows on kubernetes
• run high-throughput LLM inference jobs
• fine-tune models with distributed GPU compute
• build reproducible ETL pipelines in python
• recover long-running jobs from infrastructure failures

When to choose

• you need production-grade ML/AI pipeline orchestration on Kubernetes
• you want durable, retryable workflows written in pure Python without YAML DSLs
• you need to coordinate agents, data, and model training in one platform
• you require checkpointing, caching, and failure recovery for long-running jobs

When to avoid

• you only need simple cron-style job scheduling without orchestration
• your workloads are small single-machine scripts with no scaling needs
• you want a lightweight local-only task runner without a control plane
• you are not prepared to operate Kubernetes-based infrastructure

Facets

framework · maturity active

workflow-automation scheduling machine-learning llm-inference agent-framework etl deployment cli sdk machine-learning artificial-intelligence developer-tools cloud-computing python go cli cross-platform cloud self-hosted workflow-orchestration mlops durable-execution pipelines kubernetes-native declarative grpc fine-tuning llm ai-agents lf-ai-foundation data-engineering containers kubernetes docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="flyteorg/flyte")