3PL Central Python API Docs | dltHub

3PL Central (3PL Warehouse Manager / Extensiv) is a SaaS warehouse management platform providing REST APIs to manage customers, inventory, orders, carriers, and related warehouse resources. The REST API base URL is https://{your_instance_domain} and all requests require HTTP Basic (token) / Bearer depending on product — obtain an auth token via POST /auth/token (Basic -> bearer) or use Basic with Base64 credentials for some endpoints..

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 3PL Central data in under 10 minutes.

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What data can I load from 3PL Central?

Here are some of the endpoints you can load from 3PL Central:

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How do I authenticate with the 3PL Central API?

The 3PL Warehouse Manager REST APIs require obtaining an authentication token by POSTing credentials (client id/secret, tpl guid, user login id) to the token endpoint using Basic Authorization (Base64 encoded client credentials). The returned access token must be provided as Authorization: Bearer {token} for subsequent requests. Some legacy endpoints accept HTTP Basic (Base64 of userid:password) in Authorization header.

1. Get your credentials

1. Request REST API access from your 3PL Central / Extensiv customer success manager. 2) Obtain API keys: Client ID, Client Secret, tpl GUID and UserLogin ID (and account-specific base URL). 3) Use those values to call the token endpoint (POST /auth/token) with Basic Authorization (Base64 of ClientID:ClientSecret) to receive a short-lived bearer token. 4) Include Authorization: Bearer {access_token} in all API requests; refresh hourly.

2. Add them to .dlt/secrets.toml

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[sources.three_pl_central_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
tpl_guid = "your_tpl_guid"
user_login_id = "your_user_login_id"
base_url = "https://secure-wms.com"

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 3PL Central 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 three_pl_central_pipeline.py

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

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Pipeline three_pl_central_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset three_pl_central_data
The duckdb destination used duckdb:/three_pl_central.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 three_pl_central_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 inventory_stockdetails from the 3PL Central 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 three_pl_central_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_instance_domain}",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders"}},
            {"name": "inventory_stockdetails", "endpoint": {"path": "inventory/stockdetails"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM three_pl_central_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("three_pl_central_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 3PL Central 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 Unauthorized: ensure you requested REST API access and have the correct Client ID, Client Secret, tpl GUID and UserLogin ID. Generate the token via POST /auth/token using Basic auth (Base64 ClientID:ClientSecret) and include Authorization: Bearer {token} in requests. Tokens expire hourly — refresh before expiry.

Pagination and RQL

Many list endpoints default to 100 records per page (max 500). Use query params pgsiz (limit), pgnum (page), and follow next/prev links in HAL responses. RQL (3PL Central RQL) can filter results (use rql parameter).

Rate limits and errors

API returns standard HTTP errors: 400 for bad requests (payload/headers), 401 for missing/invalid auth, 403 for forbidden, 404 for not found, 429 may be returned for throttling; 500-range for server errors. 400 responses include detailed JSON error payloads for diagnostics. Some endpoints require If-Match header with ETag for state-changing calls.

Caveats: API is customer-instance-specific; obtain your base URL from 3PL Central account manager. Many endpoints use HAL+JSON; records may appear at top-level arrays or under HAL _embedded structures depending on endpoint.

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