FDA Python API Docs | dltHub

openFDA is an Elasticsearch-backed public API that serves FDA data (drugs, devices, foods, recalls, adverse events, etc.). The REST API base URL is https://api.fda.gov (openFDA); https://api-datadashboard.fda.gov/v1 (FDA Data Dashboard); https://www.accessdata.fda.gov/rest/pcbapi/v1 (PCB API base path under accessdata); https://www.accessdata.fda.gov/rest/iresapi/v1 (iRES Enforcement API base path under accessdata — documented at ires/apidocs). and openFDA: optional API key passed as api_key parameter (increases rate limits); accessdata (iRES/PCB/Data Dashboard): header-based Authorization-User and Authorization-Key (OII Unified Logon) required..

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 FDA data in under 10 minutes.

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What data can I load from FDA?

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

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How do I authenticate with the FDA API?

openFDA accepts an api_key query parameter (or Basic auth username form) to raise rate limits; Data Dashboard / iRES / PCB require FDA OII Unified Logon credentials and two headers: Authorization-User (email) and Authorization-Key (FDA-generated key).

1. Get your credentials

1. openFDA: request a free API key at https://open.fda.gov/apis/authentication/ (follow "Get your API key" workflow).
2. iRES/PCB/Data Dashboard: request credentials via OII Unified Logon (https://www.accessdata.fda.gov/scripts/oul) and follow the application instructions; FDA will send Authorization-Key and Authorization-User (email).

2. Add them to .dlt/secrets.toml

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[sources.fda_data_source]
api_key = "your_openfda_api_key"
authorization_user = "your_email@example.com"
authorization_key = "your_fda_auth_key"

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 FDA 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 fda_data_pipeline.py

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

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Pipeline fda_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fda_data_data
The duckdb destination used duckdb:/fda_data.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 fda_data_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 drug_label and drug_event from the FDA 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 fda_data_source(api_key (for openFDA) / authorization_user, authorization_key (for accessdata sources)=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fda.gov (openFDA); https://api-datadashboard.fda.gov/v1 (FDA Data Dashboard); https://www.accessdata.fda.gov/rest/pcbapi/v1 (PCB API base path under accessdata); https://www.accessdata.fda.gov/rest/iresapi/v1 (iRES Enforcement API base path under accessdata — documented at ires/apidocs).",
            "auth": {
                "type": "api_key (openFDA) ; http_basic-like header pair (accessdata) — represent as "api_key" for openFDA and "custom_headers" for accessdata in dlt.",
                "api_key (for openFDA) ; authorization_key (for accessdata)": api_key (for openFDA) / authorization_user, authorization_key (for accessdata sources),
            },
        },
        "resources": [
            {"name": "drug_label", "endpoint": {"path": "drug/label.json", "data_selector": "results"}},
            {"name": "drug_event", "endpoint": {"path": "drug/event.json", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fda_data_pipeline",
        destination="duckdb",
        dataset_name="fda_data_data",
    )
    load_info = pipeline.run(fda_data_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("fda_data_pipeline").dataset()
sessions_df = data.drug_label.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fda_data_data.drug_label LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fda_data_pipeline").dataset()
data.drug_label.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 FDA 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 failures

openFDA: If requests exceed anonymous daily limits, obtain an api_key and pass as api_key parameter or Basic auth username. accessdata APIs: 401/Not Authorized occur when Authorization-User or Authorization-Key headers are missing or cached with wrong signature; use unique signatures and ensure TLS1.2.

Rate limits and quotas

openFDA: anonymous limits 240 req/min per IP and 1,000/day; with api_key 240 req/min per key and 120,000/day. Contact openFDA for higher limits.

Pagination and result selectors

openFDA: responses are JSON objects with top-level keys meta and results; the records array is in "results". Data Dashboard/iRES: responses include keys statuscode,message,resultcount,result; the records array is "result". Data Dashboard supports start/rows paging (max rows 5000) and returntotalcount to get totalrecordcount.

common_api_errors:

• openFDA returns HTTP 400 for malformed queries; Data Dashboard returns statuscode values (e.g., 401 Not Authorized, 412 No results found, 415 rows >5000, etc.) as documented.

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