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Export Airtable data to files on disk

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PyPI Changelog Tests License

Export Airtable data to files on disk


Install this tool using pip:

$ pip install airtable-export


You will need to know the following information:

  • Your Airtable base ID - this is a string starting with app...
  • Your Airtable API key - this is a string starting with key...
  • The names of each of the tables that you wish to export

You can export all of your data to a folder called export/ by running the following:

airtable-export export base_id table1 table2 --key=key

This example would create two files: export/table1.yml and export/table2.yml.

Rather than passing the API key using the --key option you can set it as an environment variable called AIRTABLE_KEY.

Export options

By default the tool exports your data as YAML.

You can also export as JSON or as newline delimited JSON using the --json or --ndjson options:

airtable-export export base_id table1 table2 --key=key --ndjson

You can pass multiple format options at once. This command will create a .json, .yml and .ndjson file for each exported table:

airtable-export export base_id table1 table2 \
    --key=key --ndjson --yaml --json

SQLite database export

You can export tables to a SQLite database file using the --sqlite database.db option:

airtable-export export base_id table1 table2 \
    --key=key --sqlite database.db

This can be combined with other format options. If you only specify --sqlite the export directory argument will be ignored.

The SQLite database will have a table created for each table you export. Those tables will have a primary key column called airtable_id.

If you run this command against an existing SQLite database records with matching primary keys will be over-written by new records from the export.

Request options

By default the tool uses python-httpx's default configurations.

You can override the user-agent using the --user-agent option:

airtable-export export base_id table1 table2 --key=key --user-agent "Airtable Export Robot"

You can override the timeout during a network read operation using the --http-read-timeout option. If not set, this defaults to 5s.

airtable-export export base_id table1 table2 --key=key --http-read-timeout 60

Running this using GitHub Actions

GitHub Actions is GitHub's workflow automation product. You can use it to run airtable-export in order to back up your Airtable data to a GitHub repository. Doing this gives you a visible commit history of changes you make to your Airtable data - like this one.

To run this for your own Airtable database you'll first need to add the following secrets to your GitHub repository:

The base ID, a string beginning `app...`
Your Airtable API key
A space separated list of the Airtable tables that you want to backup. If any of these contain spaces you will need to enclose them in single quotes, e.g. <samp>'My table with spaces in the name' OtherTableWithNoSpaces</samp>

Once you have set those secrets, add the following as a file called .github/workflows/backup-airtable.yml:

name: Backup Airtable

  - cron: '32 0 * * *'

    runs-on: ubuntu-latest
    - name: Check out repo
      uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
        python-version: 3.8
    - uses: actions/cache@v2
      name: Configure pip caching
        path: ~/.cache/pip
        key: ${{ runner.os }}-pip-
        restore-keys: |
          ${{ runner.os }}-pip-
    - name: Install airtable-export
      run: |
        pip install airtable-export
    - name: Backup Airtable to backups/
        AIRTABLE_BASE_ID: ${{ secrets.AIRTABLE_BASE_ID }}
        AIRTABLE_KEY: ${{ secrets.AIRTABLE_KEY }}
      run: |-
        airtable-export backups $AIRTABLE_BASE_ID $AIRTABLE_TABLES -v
    - name: Commit and push if it changed
      run: |-
        git config "Automated"
        git config ""
        git add -A
        timestamp=$(date -u)
        git commit -m "Latest data: ${timestamp}" || exit 0
        git push

This will run once a day (at 32 minutes past midnight UTC) and will also run if you manually click the "Run workflow" button, see GitHub Actions: Manual triggers with workflow_dispatch.


To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd airtable-export
python -mvenv venv
source venv/bin/activate

Or if you are using pipenv:

pipenv shell

Now install the dependencies and tests:

pip install -e '.[test]'

To run the tests:


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