---
id: flyte
version: "2.6.0"
license: unclear
license_treatment: unclear
maintenance: active
---

# flyte — Add your description here

License: unclear · Maintenance: active · Downloads: 173.4K/mo

## What it is and what it does

Flyte is a Python-native orchestration framework for building and running ML pipelines, models, and agents. It lets you define tasks as decorated async or synchronous Python functions, compose them into workflows, and execute them locally during development or remotely on Kubernetes at scale. The SDK handles task parallelism, retries, caching, and resource management automatically.

The framework supports multiple execution modes: local development with a CLI and optional TUI, a Devbox environment for testing against a local Kubernetes cluster, and remote execution on production infrastructure. It also includes app serving capabilities for deploying models via FastAPI. With 23 runtime dependencies including async-lru, pydantic, protobuf, and cloud storage support, it integrates deeply with modern Python async patterns and cloud ecosystems.

Use it for:

- Define and run multi-step ML training pipelines with automatic task parallelism and fault tolerance.
- Serve trained models as HTTP endpoints using FastAPI integration within the same framework.
- Develop workflows locally with the CLI and TUI, then deploy to Kubernetes without code changes.
- Build data processing workflows that combine async task execution with built-in caching and retries.
- Orchestrate agent workflows that require fine-grained control over concurrency and resource allocation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Flyte is a Python SDK for defining, orchestrating, and executing ML pipelines and services at scale, supporting both async and synchronous task definitions with built-in retries, caching, and local or remote execution.

Yes, with conditions. Flyte 2 is actively maintained (released 2 days ago) and offers a cohesive Python-first approach to ML orchestration with low install friction. However, the license treatment is unclear in the metadata despite Apache 2.0 being mentioned in the description—verify the actual license before production use. The Rust controller is marked experimental and under rapid development with known gaps. Best suited for teams building ML pipelines who can tolerate a large dependency footprint and want async-native workflows.

## Install

pip install flyte
uv add flyte
poetry add flyte

## Installing flyte

Before you install: Low friction installation as a pure Python wheel. Active maintenance with a release 2 days ago. Requires Python 3.10 or later and brings in 23 runtime dependencies including async utilities, protobuf, pydantic, and cloud storage support.

Quickstart:

pip install flyte

import flyte

env = flyte.TaskEnvironment(
    name="hello_world",
    image=flyte.Image.from_debian_base(python_version=(3, 12)),
)

@env.task(retries=3, cache="auto")
async def predict(x: int) -> int:
    return 2 * x + 5

Requires Python 3.10 or later

Verify before relying:

- License classification is marked unclear in the fact sheet; Apache 2.0 is mentioned in the description excerpt but not confirmed in metadata
- Whether the Rust controller (flyte_controller_base) is required for typical use or truly optional as described
- Production readiness status beyond 'generally available' announcement

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 173.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ml pipeline orchestration python, workflow scheduling framework, distributed task execution, async python workflows, ml model serving framework, data pipeline automation, kubernetes workflow engine, ml-orchestration, async-workflows, kubernetes-native

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