Lago Python API Docs | dltHub

Lago is a subscription billing and revenue management API. The REST API base URL is https://api.getlago.com/api/v1 and All requests require a Bearer token for authentication..

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 Lago data in under 10 minutes.

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What data can I load from Lago?

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

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How do I authenticate with the Lago API?

Send the API key in the HTTP header Authorization: Bearer <API_KEY>. The request body and responses are JSON, and the Content-Type header should be application/json.

1. Get your credentials

1. Log in to your Lago account at https://app.getlago.com.
2. Navigate to Settings → API Keys.
3. Click Create new API key.
4. Copy the generated key; it will not be shown again.
5. Store the key securely for use in dlt configuration.

2. Add them to .dlt/secrets.toml

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[sources.lago_source]
api_key = "your_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 Lago 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 lago_pipeline.py

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

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Pipeline lago_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset lago_data
The duckdb destination used duckdb:/lago.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 lago_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 billing_entities and invoices from the Lago 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 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": "billing_entities", "endpoint": {"path": "billing_entities", "data_selector": "billing_entities"}},
            {"name": "invoices", "endpoint": {"path": "invoices", "data_selector": "invoices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="lago_pipeline",
        destination="duckdb",
        dataset_name="lago_data",
    )
    load_info = pipeline.run(lago_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("lago_pipeline").dataset()
sessions_df = data.billing_entities.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM lago_data.billing_entities LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("lago_pipeline").dataset()
data.billing_entities.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 Lago 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 Errors

• 401 Unauthorized – Ensure the Authorization: Bearer <API_KEY> header is present and the token is valid. Regenerate the API key from the Lago dashboard if necessary.

Rate Limiting

• 429 Too Many Requests – The API enforces rate limits per account. Implement exponential backoff and respect the Retry-After header returned by the server.

Pagination Issues

• Invalid page or per_page values – The API expects integer values. Verify that pagination parameters are correctly typed and within allowed ranges (e.g., per_page max 100).

Generic Errors

• 400 Bad Request, 404 Not Found, 422 Unprocessable Entity – These are defined in the OpenAPI components/responses. Check the error message body for details and correct request parameters accordingly.

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