Load WorkOS data to DuckDB

WorkOS is an API platform that provides authentication and enterprise-ready integrations like SSO, directory sync, and audit logs. Everything needed to build a working WorkOS → 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 WorkOS 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 WorkOS 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 WorkOS 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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WorkOS API at a glance

These values come from the WorkOS API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the WorkOS API?

Requests must be authenticated by including the Authorization header with a Bearer token containing your WorkOS API key. API keys are prefixed with 'sk_'.

1. Get your credentials

1. Log in to your WorkOS Dashboard.\n2. Select the relevant Environment (Staging or Production) from the top-level menu.\n3. Navigate to 'API Keys' in the left-hand sidebar.\n4. For Production keys, click 'Create Key', provide a name, and secure the generated key immediately as it will not be displayed again. For Staging, keys are available to view inline.

2. Add them to .dlt/secrets.toml

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[sources.workos_source]
api_key = "REPLACE_ME"

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 WorkOS data can I load into DuckDB?

These are the WorkOS endpoints dlt can load into DuckDB:

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How do I load only new WorkOS records?

WorkOS exposes after on connections, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "connections", "endpoint": {
    "path": "connections",
    "data_selector": "data",
    "incremental": {"cursor_path": "after", "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 WorkOS pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading organizations and authkit/api-keys/validate from the WorkOS API into DuckDB:

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

@dlt.source
def workos_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.workos.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "connections", "endpoint": {"path": "connections", "data_selector": "data"}},
            {"name": "organizations", "endpoint": {"path": "organizations", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_workos_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="workos_pipeline",
        destination="duckdb",
        dataset_name="workos_data",
    )
    load_info = pipeline.run(workos_source())
    print(load_info)


if __name__ == "__main__":
    load_workos_to_duckdb()

Run it with python workos_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 WorkOS 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("workos_pipeline").dataset()
df = data.connections.df()
print(df.head())

SQL:

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SELECT * FROM workos_data.connections LIMIT 10;

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

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How do I deploy the WorkOS 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 WorkOS 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 WorkOS 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