Installation

I'm using Ubuntu 24.04.4 LTS.

uv is a fast Python package manager, written in Rust. Installation instructions in detail are available here. It enables you to fetch dependencies (libraries) from their official distribution sources faster than when using pip alone.

1. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Create a directory for airflow at your desired location:

mkdir airflow

3. Create virtual environment for airflow:

uv venv --python=3.12

4. Activate the newly created venv:

source .venv/bin/activate

5. Install airflow via uv:

uv pip install "apache-airflow[celery]==3.2.2" --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.2.2/constraints-3.12.txt"

6. Install openai provider for airflow:

uv pip install apache-airflow-providers-openai

7. Install common ai module:

uv pip install apache-airflow-providers-common-ai

8. Run airflow:

airflow standalone

Note: On first run, the admin password will be echoed to terminal. After the first run, you can find it in the file simple_auth_manager_passwords.json.generated in the directory

9. Create Pydantic AI connection to your OpenAI model compliant LLM via the Apache Airflow UI at http://localhost:8080 (do not use "standalone" for production), using the following:

• Connection Type: Pydantic AI
• Connection ID: openai_default
• Description: [optional]
• Host: http://127.0.0.1:11434/v1
• API Key: [not required but apparently cannot be left blank]
• Extra JSON: { "extra": { "model": "openai:nemotron-3-nano:4b" } }

Note: "model" in extra JSON must be prefixed with "openai:", but you can use whatever model is desired, as long as it supports "thinking"

10. Create your directed acyclic graph (DAG)

Note: The default path for directed acyclic graphs (DAG) is "./dags/".