Regulations.gov Python API Docs | dltHub

Regulations.gov API is a REST API that provides access to documents, comments, and dockets related to federal regulations. The REST API base URL is https://api.regulations.gov and All requests require an API key in the X-Api-Key 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 Regulations.gov data in under 10 minutes.

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What data can I load from Regulations.gov?

Here are some of the endpoints you can load from Regulations.gov:

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How do I authenticate with the Regulations.gov API?

Authentication requires an API key, which must be provided in the X-Api-Key HTTP header or as an api_key query parameter for every request.

1. Get your credentials

To obtain API credentials, you need to register for an API key on the Regulations.gov API page (open.gsa.gov/api/regulationsgov/). After registration, the API key will be provided.

2. Add them to .dlt/secrets.toml

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[sources.regulations_gov_source]
api_key = "YOUR_REGS_GOV_API_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 Regulations.gov 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 regulations_gov_pipeline.py

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

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Pipeline regulations_gov_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset regulations_gov_data
The duckdb destination used duckdb:/regulations_gov.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 regulations_gov_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 documents and comments from the Regulations.gov 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 regulations_gov_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.regulations.gov",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "v4/documents", "data_selector": "data"}},
            {"name": "comments", "endpoint": {"path": "v4/comments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="regulations_gov_pipeline",
        destination="duckdb",
        dataset_name="regulations_gov_data",
    )
    load_info = pipeline.run(regulations_gov_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("regulations_gov_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM regulations_gov_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("regulations_gov_pipeline").dataset()
data.documents.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 Regulations.gov 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

If you receive a 401 Unauthorized error, it indicates a missing or invalid API key. Ensure your X-Api-Key header contains a valid key.

Rate Limits

The Regulations.gov API has rate limits. The commenting API is restricted to 50 requests per minute and 500 requests per hour. Exceeding these limits will result in a 429 Too Many Requests error. Refer to api.data.gov/docs/rate-limits/ for general rate limit information.

Pagination and Bulk Retrieval

For bulk retrieval, use the lastModifiedDate parameter and page through data in chunks, as there are strict pagination limits on page size and total pages. While examples mention 5000 per page, the v4 API enforces specific limits.

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