openFDA Python API Docs | dltHub

openFDA is an Elasticsearch-based platform providing REST APIs for public FDA data including drugs, medical devices, foods, and other regulated products. The REST API base URL is https://api.fda.gov and authentication is optional; a free API key can be used to increase rate limits via a query parameter or Basic Auth.

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 add "dlt[hub]" and start loading openFDA data in under 10 minutes.

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What data can I load from openFDA?

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

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How do I authenticate with the openFDA API?

The API supports passing the API key as an 'api_key' query parameter or as a username in Basic Auth (the password field can be left empty).

1. Get your credentials

Visit the official openFDA authentication page (https://open.fda.gov/apis/authentication/) to request a free API key. You will need to provide a valid email address. Upon registration, you will receive an API key that you can use to increase your rate limits (up to 120,000 requests per day).

2. Add them to .dlt/secrets.toml

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[sources.openfda_source]
api_key = "your_api_key_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 openFDA 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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uv run python openfda_pipeline.py

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

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Pipeline openfda_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset openfda_data
The duckdb destination used duckdb:/openfda.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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uv run dlthub 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 drug/event and drug/label from the openFDA 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 openfda_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fda.gov",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "drug_event", "endpoint": {"path": "drug/event.json", "data_selector": "results"}},
            {"name": "drug_label", "endpoint": {"path": "drug/label.json", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="openfda_pipeline",
        destination="duckdb",
        dataset_name="openfda_data",
    )
    load_info = pipeline.run(openfda_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("openfda_pipeline").dataset()
sessions_df = data.drug_event.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM openfda_data.drug_event LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("openfda_pipeline").dataset()
data.drug_event.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 openFDA 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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