adag

Another DAG: a cached, content-addressed DAG engine for Python data pipelines.

Install

pip install adag

Requires Python 3.13+.

Quickstart

Each step is a frozen dataclass. Call a step to wire it into the graph, then run the graph with a Pipeline.

from dataclasses import dataclass
from pathlib import Path

from pydantic import BaseModel

from adag import InputParam, Pipeline, Step


class Number(BaseModel):
    value: int


@dataclass(frozen=True)
class Square(Step[Number]):
    def run(self, x: int) -> Number:
        print("computing square")
        return Number(value=x * x)


@dataclass(frozen=True)
class AddOne(Step[Number]):
    def run(self, n: Number) -> Number:
        return Number(value=n.value + 1)


x = InputParam[int]("x")
total = AddOne()(n=Square()(x=x))
pipeline = Pipeline(total, cache_dir=Path("cache"))

print(pipeline.execute(x=4))  # computes both steps
print(pipeline.execute(x=4))  # served from the cache

The second run reads both results from cache/ and does not call Square.run again:

computing square
value=17
value=17

(output trimmed: structlog lines removed)

Why adag

A step's cache key is a hash of its fields, its inputs, and its source code. Change any of them and that step and everything downstream recompute; nothing else does. Use it for data and ML pipelines where steps are slow and you rerun the graph often. How it compares to Luigi, Airflow, Prefect, Dagster, Hamilton and redun: Why adag.

Key features

• Content-addressed cache: a code edit invalidates only the steps it touches.
• Config fields stay out of the cache key (worker counts, clients, timeouts).
• Sync and async steps (Step, AsyncStep), with a parallel executor.
• BatchStep caches each item on its own, so a rerun resumes after a failure.
• FanOutStep runs a sub-graph per key.
• Cache stores for step results and BatchStep items: filesystem, memory, SQLite, SQLAlchemy (adag[sql]), Redis (adag[redis]). Pass one with Pipeline(..., store=).
• Save a pipeline to disk and load it back with identical hashes.
• SVG rendering of the graph, and an adag run pipeline.py CLI.
• Run fan-out branches in worker processes or on a job scheduler (AWS Batch, SLURM, Kubernetes): write submit, status and cancel; adag does retries, timeouts and cancel.

Links

• Documentation: https://cardona-oss.gitlab.io/adag/
• Source: https://gitlab.com/cardona-oss/adag
• Issues: https://gitlab.com/cardona-oss/adag/-/issues
• PyPI: https://pypi.org/project/adag/
• Changelog: CHANGELOG.md

Contributing

Developer setup, the dev loop, and the release process live in CONTRIBUTING.md. In short: uv sync && make install-hooks, then make check runs the same gate CI runs.

License

MIT © 2026 Jorge Cardona (see LICENSE).