Cargoboard Python API Docs | dltHub

Cargoboard is a freight forwarding and shipment management platform providing REST APIs to place, retrieve and manage orders, print shipment labels and confirmations. The REST API base URL is https://api.cargoboard.com/v1 and All requests require either an X-Api-Key (customer token) or a Bearer user token..

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 Cargoboard data in under 10 minutes.

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What data can I load from Cargoboard?

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

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How do I authenticate with the Cargoboard API?

Authentication can use a customer token in the X-Api-Key header or an OAuth/user token as a Bearer token in the Authorization header.

1. Get your credentials

1. Sign in to the Cargoboard dashboard. 2) Navigate to API / Integrations or developer settings. 3) Create or copy your customer API key (X-Api-Key) for customer-level access. 4) For user-specific tokens, generate an access token via the dashboard or OAuth flow as documented. 5) Use sandbox keys for testing against api-sandbox.cargoboard.com.

2. Add them to .dlt/secrets.toml

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[sources.cargoboard_source]
api_key = "your_customer_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 Cargoboard 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 cargoboard_pipeline.py

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

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Pipeline cargoboard_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cargoboard_data
The duckdb destination used duckdb:/cargoboard.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 cargoboard_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 orders and order from the Cargoboard 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 cargoboard_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cargoboard.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "data"}},
            {"name": "order", "endpoint": {"path": "orders/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cargoboard_pipeline",
        destination="duckdb",
        dataset_name="cargoboard_data",
    )
    load_info = pipeline.run(cargoboard_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("cargoboard_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cargoboard_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cargoboard_pipeline").dataset()
data.orders.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 Cargoboard 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 you receive 401/403 responses, verify you are sending either X-Api-Key header with the customer token or Authorization: Bearer <token>. Use sandbox keys against api-sandbox.cargoboard.com.

Rate limits and errors

The API returns standard 4xx client errors (400/401/403/404) and 5xx server errors. Implement retry logic for transient 5xx responses and inspect the error body for details.

Pagination and filtering

The GET /orders endpoint supports pagination via query parameters (page, pageSize etc.). The JSON response key containing the list of orders is data. Iterate through pages until no further records are returned.

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