Captain Data Python API Docs | dltHub

Captain Data is a developer-first REST API platform for extracting and enriching B2B people and company data (profiles, firmographics, employee lists) in real-time. The REST API base URL is https://api.captaindata.com and All requests require an API key sent as an HTTP header (X-API-Key or x-api-key) or as the 'key' query parameter for older endpoints..

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 Captain Data data in under 10 minutes.

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What data can I load from Captain Data?

Here are some of the endpoints you can load from Captain Data:

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How do I authenticate with the Captain Data API?

Provide your project API key in the X-API-Key (or x-api-key) header for v1/v2; some legacy v1 examples also accept the key query parameter. Never share the key.

1. Get your credentials

1. Sign in to https://app.captaindata.com.
2. Open your project's Settings or Developer Settings page.
3. Locate the Project UID and the API Key displayed there.
4. Copy the API Key and use it in the X-API-Key header for all requests.

2. Add them to .dlt/secrets.toml

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[sources.captain_data_source]
api_key = "YOUR_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 Captain Data 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 captain_data_pipeline.py

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

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Pipeline captain_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset captain_data_data
The duckdb destination used duckdb:/captain_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 captain_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 people_search and companies_search from the Captain Data 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 captain_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.captaindata.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "people_search", "endpoint": {"path": "v1/people/search"}},
            {"name": "companies_search", "endpoint": {"path": "v1/companies/search"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="captain_data_pipeline",
        destination="duckdb",
        dataset_name="captain_data_data",
    )
    load_info = pipeline.run(captain_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("captain_data_pipeline").dataset()
sessions_df = data.people_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM captain_data_data.people_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("captain_data_pipeline").dataset()
data.people_search.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 Captain Data 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 requests return 401/403, verify that the X-API-Key (or x-api-key) header is present and contains a valid project API key. For legacy v1 calls you may also need to supply the key query parameter. Ensure the project UID in the URL matches the one associated with the key. Contact support@captaindata.co if problems persist.

Pagination and large result sets

Some endpoints are paginated. Use page_size and page query parameters as shown in examples (e.g. ?page_size=25). Follow the pagination guide in the documentation for handling offsets or next‑page tokens.

Rate limits and quota errors

Quota and rate information are exposed via /v1/quotas and the project endpoint. Exceeding quotas returns errors (see the Error Reference). Monitor tasks_left and tasks_used from the project metadata endpoint to avoid hitting 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