Stardag
Declarative and composable DAGs for Python.
Stardag provides a clean Python API for representing persistently stored assets, the code that produces them, and their dependencies as a declarative Directed Acyclic Graph (DAG). It is a spiritual—but highly modernized—descendant of Luigi, designed for iterative data and ML workflows.
Built on Pydantic, Stardag uses expressive type annotations to reduce boilerplate and make task I/O contracts explicit—enabling composable tasks and pipelines while maintaining a fully declarative specification of every produced asset.
Quick Example
import stardag as sd
@sd.task
def get_range(limit: int) -> list[int]:
return list(range(limit))
@sd.task
def get_sum(integers: sd.Depends[list[int]]) -> int:
return sum(integers)
# Declarative DAG specification - no computation yet
sum_task = get_sum(integers=get_range(limit=4))
# Materialize all tasks' targets
sd.build(sum_task)
# Load results
assert sum_task.load() == 6
assert sum_task.integers.load() == [0, 1, 2, 3]
Installation
pip install stardag
Or with uv:
uv add stardag
Optional extras:
pip install stardag[s3] # S3 storage support
pip install stardag[prefect] # Prefect integration
pip install stardag[modal] # Modal integration
Documentation
Read the docs for tutorials, guides, and API reference.
- Getting Started — Installation and first steps
- Core Concepts — Tasks, targets, dependencies
- How-To Guides — Integrations with Prefect, Modal
- Configuration — Profiles, CLI reference
Stardag Cloud
Stardag Cloud provides optional services for team collaboration and monitoring:
- Web UI — Dashboard for build monitoring and task inspection
- API Service — Task tracking and coordination across distributed builds
The SDK works fully standalone—the platform adds value for teams needing shared visibility and coordination.
Why Stardag?
- Composability — Task instances as first-class parameters enable loose coupling and reusability
- Declarative — Full DAG specification before execution; inspect, serialize, and reason about pipelines
- Deterministic — Parameter hashing gives each task a unique, reproducible ID and output path
- Pydantic-native — Tasks are Pydantic models with full validation and serialization support
- Framework-agnostic — Integrate with Prefect, Modal, or run standalone
Links
1 maintainer
Release files for stardag 0.26.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| stardag-0.26.0.tar.gz | 1.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| stardag-0.26.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.6 MB
Release files / stardag-0.26.0.tar.gz
| Download URL | stardag-0.26.0.tar.gz |
|---|---|
| Size | 1.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / stardag-0.26.0-py3-none-any.whl
| Download URL | stardag-0.26.0-py3-none-any.whl |
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| Size | 527.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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