Onfleet Python API Docs | dltHub

Onfleet is a RESTful delivery management and real‑time logistics API for creating, managing and tracking tasks, workers, destinations, recipients, teams and webhooks. The REST API base URL is https://onfleet.com/api/v2 and All requests require HTTP Basic authentication using the organization API key as the username and an empty password..

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 Onfleet data in under 10 minutes.

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What data can I load from Onfleet?

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

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How do I authenticate with the Onfleet API?

Onfleet uses HTTP Basic authentication: set the Authorization header to Basic with the API key as the username and an empty password. Content‑Type must be application/json for JSON bodies.

1. Get your credentials

1. Log into your Onfleet admin dashboard.
2. Open Settings > API & Webhooks.
3. Click the "+" button to create a new API key.
4. Optionally limit the key scope.
5. Copy the API key (use as Basic auth username, leave password blank).

2. Add them to .dlt/secrets.toml

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[sources.onfleet_source]
api_key = "your_onfleet_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 Onfleet 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 onfleet_pipeline.py

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

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Pipeline onfleet_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset onfleet_data
The duckdb destination used duckdb:/onfleet.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 onfleet_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 tasks and workers from the Onfleet 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 onfleet_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://onfleet.com/api/v2",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "tasks"}},
            {"name": "workers", "endpoint": {"path": "workers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="onfleet_pipeline",
        destination="duckdb",
        dataset_name="onfleet_data",
    )
    load_info = pipeline.run(onfleet_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("onfleet_pipeline").dataset()
sessions_df = data.tasks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM onfleet_data.tasks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("onfleet_pipeline").dataset()
data.tasks.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 Onfleet 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're using HTTP Basic auth with the API key as the username and an empty password. In Postman set Authorization type to Basic and place the API key in the username field.

Rate limits and 429 responses

Onfleet enforces rate limits; a 429 Too Many Requests indicates you should back off and retry after a short delay. Implement exponential backoff when retrying.

Pagination and list endpoints

List endpoints (e.g., GET /tasks, GET /workers) return arrays. Use query parameters described in each endpoint's docs (filters, time ranges, states) to narrow results. Large result sets should be requested with batch filters; some batch endpoints support asynchronous creation with job status webhooks.

Common errors

401 Unauthorized — invalid or missing API key. 403 Forbidden — API key scope prevents action. 404 Not Found — resource id doesn't exist. 429 Too Many Requests — rate limit exceeded. 400 Bad Request — invalid parameters. 500/502/504 — server errors; retry with backoff.

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