> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mage.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Integrate Mage into an existing Airflow project
> In this tutorial, we’ll create a DAG in Airflow for scheduling and running a data pipeline; all from the Mage UI.
This tutorial requires that you already have Airflow setup and running
locally.
Open the `requirements.txt` file in the root directory of your Airflow project,
and add the `mage-ai` library:
```
mage-ai
```
You can install and run Mage using Docker or using pip.
### Using Docker
```bash theme={"system"}
docker pull mageai/mageai:latest
```
### Using pip
```bash theme={"system"}
pip install mage-ai
```
Change directory into your Airflow’s DAGs folder. This is typically in the
folder `dags/`.
```bash theme={"system"}
cd dags
```
Then, initialize a new Mage project in the `dags/` folder.
If you’re using Docker, run the following command in the `dags/` folder:
```bash theme={"system"}
docker run -it -p 6789:6789 -v $(pwd):/home/src \
mageai/mageai /app/run_app.sh mage init demo_project
```
If you used pip to install Mage, run the following command in the `dags/`
folder:
```bash theme={"system"}
mage init demo_project
```
Once finished, you should have a folder named `demo_project` inside your `dags/`
folder.
Your current folder structure should look like this:
```
airflow_root_directory/
| -- dags/
| -- | -- demo_project/
```
In the `dags/` folder, create a new file named `create_mage_pipelines.py`.
Then, add the following code:
```python theme={"system"}
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
from mage_ai.orchestration.airflow import create_dags
import os
ABSOLUTE_PATH = os.path.abspath(os.path.dirname(__file__))
project_path = os.path.join(ABSOLUTE_PATH, 'demo_project')
create_dags(
project_path,
DAG,
PythonOperator,
dag_settings=dict(
start_date=datetime(2022, 8, 5), # Change this to any start date you want
),
globals_dict=globals(),
)
```
### Start Mage
In the `dags/` folder, start the Mage tool.
If you’re using Docker, run the following command in the `dags/` folder:
```bash theme={"system"}
docker run -it -p 6789:6789 -v $(pwd):/home/src \
mageai/mageai /app/run_app.sh mage start demo_project
```
If you used pip to install Mage, run the following command in the `dags/`
folder:
```bash theme={"system"}
mage start demo_project
```
Open [http://localhost:6789](http://localhost:6789) in your browser.
Follow steps 1, 2, and 4 in this [tutorial](/guides/load-api-data) to create
a new pipeline, add 1 data loader block, and add 1 transformer block.
1. Open the Airflow webserver UI at
[http://localhost:8080](http://localhost:8080) in your browser.
2. If you named your pipeline `etl demo` based on the tutorial from the previous
step, then find a DAG named `mage_pipeline_etl_demo`. If you named it
something else, find a DAG with the prefix `mage_pipeline_`.
3. Click on the DAG to view the detail page. The URL could typically be this:
[http://localhost:8080/admin/airflow/tree?dag\_id=mage\_pipeline\_etl\_demo](http://localhost:8080/admin/airflow/tree?dag_id=mage_pipeline_etl_demo).
4. Turn that DAG on if its currently off.
5. Trigger a new DAG run.
6. Watch the DAG as it runs each task according to the pipeline you created in
Mage.
