SecurityTrails Python API Docs | dltHub

SecurityTrails is a threat‑intelligence platform providing REST API access to historical and current IP, DNS, WHOIS, domain and company data. The REST API base URL is https://api.securitytrails.com/v1 and all requests require an API key provided in the APIKEY header (or as query param when headers cannot be used).

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 SecurityTrails data in under 10 minutes.

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What data can I load from SecurityTrails?

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

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How do I authenticate with the SecurityTrails API?

Authentication is via an account API key. Include the key in every request using the HTTP header "APIKEY: <your_api_key>". If headers are not possible the API key may be passed as the "apikey" query parameter. HTTPS is mandatory.

1. Get your credentials

1. Sign in to your SecurityTrails account (https://securitytrails.com/). 2) Open Account > API or Dashboard > API (or Contact support/Account settings if not visible). 3) Create or copy an API key and store it securely. 4) Use that key in the APIKEY header for requests.

2. Add them to .dlt/secrets.toml

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[sources.security_trails_source]
api_key = "your_securitytrails_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 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 SecurityTrails 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 security_trails_pipeline.py

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

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Pipeline security_trails_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset security_trails_data
The duckdb destination used duckdb:/security_trails.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 security_trails_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 domain_subdomains and dns_current from the SecurityTrails 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 security_trails_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.securitytrails.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "domain_subdomains", "endpoint": {"path": "domain/{domain}/subdomains", "data_selector": "subdomains"}},
            {"name": "dns_current", "endpoint": {"path": "dns/{domain}", "data_selector": "records"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="security_trails_pipeline",
        destination="duckdb",
        dataset_name="security_trails_data",
    )
    load_info = pipeline.run(security_trails_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("security_trails_pipeline").dataset()
sessions_df = data.domain_subdomains.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM security_trails_data.domain_subdomains LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("security_trails_pipeline").dataset()
data.domain_subdomains.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 SecurityTrails 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 401/403 responses verify the API key. The API requires the header "APIKEY: ". If using query param, use "apikey=". Ensure HTTPS is used and the key is active on your account.

Rate limits and quota

SecurityTrails enforces rate limits and quota per account/tier. If you receive 429 responses, back off and retry after a delay; monitor your quota in the SecurityTrails dashboard (API > Quota).

Pagination and large responses

Many list endpoints paginate results. Use the provided pagination fields in responses (consult the endpoint docs for "next"/"cursor" fields) or request smaller page sizes to avoid timeouts.

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