Workwave-route-manager Python API Docs | dltHub

WorkWave Route Manager (WWRM) is a REST API that provides access to data concerning depots, drivers, vehicles, orders, and time of arrival. The REST API base URL is https://wwrm.workwave.com/api/v1 and All requests require an API key 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 Workwave-route-manager data in under 10 minutes.

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What data can I load from Workwave-route-manager?

Here are some of the endpoints you can load from Workwave-route-manager:

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How do I authenticate with the Workwave-route-manager API?

Authentication is performed using an API key, which can be passed either as a 'key' query-string parameter or as an 'X-WorkWave-Key' HTTP header. If both are provided, the query-string parameter takes precedence.

1. Get your credentials

Obtain your API key directly from WorkWave, as it is provided by them.

2. Add them to .dlt/secrets.toml

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[sources.workwave_route_manager_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 Workwave-route-manager 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 workwave_route_manager_pipeline.py

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

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Pipeline workwave_route_manager_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset workwave_route_manager_data
The duckdb destination used duckdb:/workwave_route_manager.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 workwave_route_manager_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 drivers and toa_routes from the Workwave-route-manager 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 workwave_route_manager_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://wwrm.workwave.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "drivers", "endpoint": {"path": "territories/{territoryId}/drivers", "data_selector": "drivers"}},
            {"name": "toa_routes", "endpoint": {"path": "territories/{territoryId}/toa/routes"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="workwave_route_manager_pipeline",
        destination="duckdb",
        dataset_name="workwave_route_manager_data",
    )
    load_info = pipeline.run(workwave_route_manager_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("workwave_route_manager_pipeline").dataset()
sessions_df = data.drivers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM workwave_route_manager_data.drivers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("workwave_route_manager_pipeline").dataset()
data.drivers.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 Workwave-route-manager 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 encounter a 401 Unauthorized or -200 Unknown Key error, it indicates an issue with your API key. Ensure that the API key is correctly included in the request, either as a key query-string parameter or an X-WorkWave-Key HTTP header, and that it is valid.

Rate Limiting and Queueing

Requests are queued on a per-territory basis and processed in FIFO order. The queue has a maximum size of 10 items. If you attempt to queue more than 10 requests, you will receive a 429 Too Many Requests or -900 Too Many Requests error. This indicates that you have exceeded the rate limit or queue capacity.

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