Airtable Python API Docs | dltHub

Airtable is a cloud‑based spreadsheet‑database platform that provides a REST API for programmatic access to its bases and tables. The REST API base URL is https://api.airtable.com/v0 and All requests require a Bearer token in the Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Airtable data in under 10 minutes.

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What data can I load from Airtable?

Here are some of the endpoints you can load from Airtable:

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How do I authenticate with the Airtable API?

Authentication uses token‑based Bearer authentication. Include the header Authorization: Bearer <your_token> with each request.

1. Get your credentials

1. Log in to your Airtable account.
2. Click your avatar → Account.
3. In the API section, select Generate personal access token.
4. Choose the desired scopes (e.g., data.records:read) and optionally restrict to specific bases.
5. Click Create token, then copy the generated token.
6. Store the token securely (e.g., in a secrets.toml file) for use with dlt.

2. Add them to .dlt/secrets.toml

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[sources.airtable_source]
api_token = "your_airtable_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Airtable API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python airtable_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline airtable_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset airtable_data
The duckdb destination used duckdb:/airtable.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline airtable_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads list_records and list_bases from the Airtable API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def airtable_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.airtable.com/v0",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "list_records", "endpoint": {"path": "v0/{base_id}/{table_name}", "data_selector": "records"}},
            {"name": "list_bases", "endpoint": {"path": "v0/meta/bases", "data_selector": "bases"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="airtable_pipeline",
        destination="duckdb",
        dataset_name="airtable_data",
    )
    load_info = pipeline.run(airtable_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("airtable_pipeline").dataset()
sessions_df = data.list_records.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM airtable_data.list_records LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("airtable_pipeline").dataset()
data.list_records.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Airtable data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication errors (401 Unauthorized)

A request without a valid Bearer token or with an expired token will return a 401 response. Verify that the Authorization: Bearer <token> header is correctly set and that the token has the required scopes.

Rate limiting (429 Too Many Requests)

Airtable limits API calls to 5 requests per second per base. If you exceed this limit you receive a 429 response with a Retry-After header indicating how long to wait before retrying.

Pagination (offset handling)

Responses that contain more than 100 records include an offset field. Use this value as the offset query parameter in the next request to retrieve the following page.

Validation errors (422 Unprocessable Entity)

If request parameters are malformed or required fields are missing, Airtable returns a 422 response with an error message describing the issue.

Not found (404)

A 404 response indicates that the specified base, table, or record ID does not exist or is not accessible with the provided token.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack