Installation

System Requirements

AI-Graph requires Python 3.9 or higher. It has been tested on:

  • Python 3.9, 3.10, 3.11, 3.12

  • Windows, macOS, and Linux

Install from PyPI

The easiest way to install AI-Graph is using pip:

pip install ai-graph

If you are using uv, you can install it with:

uv pip install ai-graph

Install from Source

To install the latest development version from GitHub:

git clone https://github.com/msinamsina/ai-graph.git
cd ai-graph
pip install -e .

Development Installation

For development, install with development dependencies:

we use uv as package manager.

uv sync --group dev

This will install additional tools for:

  • Testing: pytest, pytest-cov

  • Code formatting: black, isort

  • Linting: flake8, mypy

  • Documentation: sphinx, sphinx-rtd-theme, sphinx-autodoc-typehints, myst-parser

  • Development: commitizen, twine, pre-commit, plus test, docs, and lint groups

Verification

To verify your installation:

import ai_graph
print(ai_graph.__version__)

Run the test suite to ensure everything is working:

pytest

Docker Installation

You can also run AI-Graph in a Docker container:

docker run -it python:3.12
pip install ai-graph

Troubleshooting

Common installation issues and solutions:

Permission Errors

If you encounter permission errors, try installing with the --user flag:

pip install --user ai-graph

Virtual Environment

It’s recommended to use a virtual environment or uv to avoid conflicts with system packages. Here’s how to set one up:

python -m venv ai-graph-env
source ai-graph-env/bin/activate  # On Windows: ai-graph-env\Scripts\activate
pip install ai-graph

Note

If you are using uv, you can create a virtual environment with:

uv venv ai-graph-env
source ai-graph-env/bin/activate
uv pip install ai-graph

Dependency Conflicts

If you have dependency conflicts, try creating a fresh virtual environment or use conda:

conda create -n ai-graph python=3.12
conda activate ai-graph
pip install ai-graph