Load Lago data to DuckDB

Lago is a billing and monetization platform that allows applications to manage customers, subscriptions, and usage metrics. Everything needed to build a working Lago → 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 Lago 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 Lago 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 Lago 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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Lago API at a glance

These values come from the Lago API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Lago API?

Authentication is performed by passing an API key in the 'Authorization' header using the 'Bearer' token scheme. Requests must also include 'Content-Type: application/json'.

1. Get your credentials

To obtain your Lago API credentials: 1. Log in to your Lago account. 2. Navigate to the Developer section in the sidebar. 3. Select the API keys tab. 4. If you have an existing key, click the Reveal button to display it or the Copy button to copy it to your clipboard. 5. If you require a new key (Premium feature), click the Add a key button, provide an optional name, and save to generate the key.

2. Add them to .dlt/secrets.toml

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[sources.lago_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 Lago data can I load into DuckDB?

These are the Lago endpoints dlt can load into DuckDB:

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How do I load only new Lago records?

The Lago 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": "customers", "endpoint": {
    "path": "api/v1/customers",
    # 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 Lago pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /customers and /events from the Lago API into DuckDB:

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

@dlt.source
def lago_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getlago.com/api/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "api/v1/customers", "data_selector": "customers"}},
            {"name": "subscriptions", "endpoint": {"path": "api/v1/subscriptions", "data_selector": "subscriptions"}}
        ],
    }
    yield from rest_api_resources(config)


def load_lago_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="lago_pipeline",
        destination="duckdb",
        dataset_name="lago_data",
    )
    load_info = pipeline.run(lago_source())
    print(load_info)


if __name__ == "__main__":
    load_lago_to_duckdb()

Run it with python lago_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 Lago 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("lago_pipeline").dataset()
df = data.customers.df()
print(df.head())

SQL:

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SELECT * FROM lago_data.customers LIMIT 10;

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

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How do I deploy the Lago 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 Lago 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 Lago 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