Artigraph

Artigraph is a library for creating and working with interrelated graphs of data artifacts. It's best used in conjunction with data pipeline tools like Prefect or Dask. Under the hood, Artigraph uses SQLAlchemy and is compatible with most major relational databases including PostgreSQL, MySQL, and SQLite. It also supports popular tools like Pydantic, Numpy, and Pandas.

At a Glance

The script below creates a graph of data artifacts and displays it using NetworkX and Plotly.

Note

Running this script will require you to pip install "artigraph[networkx,plotly]" aiosqlite

import artigraph as ag
from artigraph.extras.networkx import create_graph
from artigraph.extras.plotly import figure_from_networkx

# configure what engine artigraph will use
ag.set_engine("sqlite+aiosqlite:///example.db", create_tables=True)


# define a model of your data
@ag.dataclass
class MyModel(ag.GraphModel, version=1):
    some_value: int
    another_value: dict[str, str]


# create a node, a model, and a link between them
node = ag.Node()
model = MyModel(some_value=42, another_value={"foo": "bar"})
link = ag.Link(source_id=node.graph_id, target_id=model.graph_id, label="my_model")

# write them to the database
ag.write_many([node, model, link])

# create and display the resulting graph
graph = create_graph(node)
fig = figure_from_networkx(graph)
fig.show()