Graphinate 0.14.0

Graphinate. Data to Graphs.

Graphinate. Data to Graphs.

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Table of Contents

• Introduction
  • Meet Graphinate
  • Library at a Glance
  • Links
• Quick Start
  • Install
  • Example
• CLI
• Gallery
• Development
• Acknowledgements

Introduction

Meet Graphinate

Graphinate turns your data into Graphs. Effortlessly.

It is a Python library designed to streamline the pipeline from raw data to structured Graph representations. With Graphinate, you can easily map complex data hierarchies and payloads directly to nodes and edges, creating an efficient retrieval system.

Whether you need to visualize relationships, analyze structure, or serve data via an API, Graphinate provides the tools to bring your graph to life.

Graphinate uses and builds upon the excellent NetworkX.

Library at a Glance

📐 The Architect (graphinate.modeling)

Define the blueprint of your graph by decorating simple functions that yield your data as nodes and edges.

🏗️ The Construction Crew (graphinate.builders)

Takes your graph blueprint and constructs various outputs, whether it's a queryable NetworkX object, a Mermaid diagram, or a GraphQL schema.

🎨 The Artists (graphinate.renderers)

A suite of tools to visualize your graphs, from matplotlib plots to interactive Mermaid diagrams.

📡 The Broadcaster (graphinate.server)

Instantly serve your graph data as an interactive GraphQL API, ready for consumption by web applications.

⌨️ The Command Center (graphinate.cli)

A handy command-line tool to manage and interact with your graph definitions without writing boilerplate code.

Links

• Website (including documentation): https://erivlis.github.io/graphinate
• Source: https://github.com/erivlis/graphinate
• Package: https://pypi.org/project/graphinate

Quick Start

Install

Graphinate is available on PyPI:

pip install graphinate

or

uv add graphinate

To install with server support

pip install graphinate[server]

or

uv add graphinate[server] 

Graphinate officially supports Python >= 3.10.

Example

import graphinate

N: int = 8

# First Define a GraphModel instance.
# It will be used to hold the graph definitions
graph_model: graphinate.GraphModel = graphinate.model(name="Octagonal Graph")


# Register in the Graph Model the edges' supplier generator function
@graph_model.edge()
def edge():
    for i in range(N):
        yield {'source': i, 'target': i + 1}
    yield {'source': N, 'target': 0}


# Use the NetworkX Builder
builder = graphinate.builders.NetworkxBuilder(graph_model)

# build the NetworkX GraphRepresentation
# the output in this case is a nx.Graph instance
graph = builder.build()

# this supplied plot method uses matplotlib to display the graph
graphinate.matplotlib.plot(graph, with_edge_labels=True)

# or use the Mermaid Builder
builder = graphinate.builders.MermaidBuilder(graph_model)

# to create a Mermaid diagram
diagram: str = builder.build()

# and get Markdown or single page HTML to display it
mermaid_markdown: str = graphinate.mermaid.markdown(diagram)
mermaid_html: str = graphinate.mermaid.html(diagram, title=graph_model.name)

# or use the GraphQL Builder
builder = graphinate.builders.GraphQLBuilder(.graph_model)

# to create a Strawberry GraphQL schema
schema = builder.build()

# and serve it using Uvicorn web server
graphinate.graphql.server(schema)

CLI

For detailed information on the command-line interface, please see the CLI Usage Guide in the official documentation.

Gallery

Python Class AST

matplotlib

3D Force-Directed Animation

GitHub Repository

Web Links

Development

For instructions on how to set up your development environment, run tests, and contribute to the project, please see the Development Guide in the official documentation.

Acknowledgements

For a list of the dependencies and tools that make Graphinate possible, please see the Acknowledgements page in the official documentation.

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Copyright © 2023-2026 Eran Rivlis