Load Kaspr data to DuckDB

Kaspr is a service that provides programmatic access to a database of business and contact information to enrich data via LinkedIn profile URLs. Everything needed to build a working Kaspr → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Kaspr to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Kaspr to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Kaspr API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Kaspr API at a glance

These values come from the Kaspr API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Kaspr API?

The API uses Bearer authentication. You must include the API key in the Authorization header in the format 'Bearer <API_KEY>'.

1. Get your credentials

1. Log in to your Kaspr account. 2. Navigate to 'Settings & Members' (or 'Settings'). 3. Go to the 'API' tab. 4. Generate a new API key (if prompted) or copy the existing one from the dashboard.

2. Add them to .dlt/secrets.toml

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[sources.kaspr_source]
api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Kaspr data can I load into DuckDB?

These are the Kaspr endpoints dlt can load into DuckDB:

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How do I load only new Kaspr records?

The Kaspr API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "user_me", "endpoint": {
    "path": "v2/user/me",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Kaspr pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/person/enrich and /v2/rich/person/linkedin-url from the Kaspr API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def kaspr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kaspr.io/v2",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "user_me", "endpoint": {"path": "v2/user/me"}},
            {"name": "person_enrich", "endpoint": {"path": "v2/person/enrich"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kaspr_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kaspr_pipeline",
        destination="duckdb",
        dataset_name="kaspr_data",
    )
    load_info = pipeline.run(kaspr_source())
    print(load_info)


if __name__ == "__main__":
    load_kaspr_to_duckdb()

Run it with python kaspr_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Kaspr data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("kaspr_pipeline").dataset()
df = data.user_me.df()
print(df.head())

SQL:

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SELECT * FROM kaspr_data.user_me LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Kaspr to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Kaspr loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Kaspr data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack