Fleetio Python API Docs | dltHub

Fleetio is a fleet management platform providing a REST API to access vehicles, fuel, maintenance, drivers, and other fleet resources. The REST API base URL is https://secure.fleetio.com/api/v1/ and All requests require an API key in the Authorization header plus an Account-Token header..

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 Fleetio data in under 10 minutes.

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What data can I load from Fleetio?

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

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How do I authenticate with the Fleetio API?

Authentication uses an API key sent in the Authorization header prefixed with the word "Token" (Authorization: Token YOUR_API_KEY) and an Account-Token HTTP header containing your account token. All requests are over HTTPS and use JSON.

1. Get your credentials

1. Log in to your Fleetio account. 2) From the account menu open User Settings and click "Manage API Keys". 3) Click "+ Add API Key" (or "New API Key"), give it a label and create the key. 4) Copy the API key and note the Account Token shown on the Manage API Keys page (also visible in the account URL or bottom of the page). 5) Store both securely; the API key is used in Authorization and the account token in Account-Token header.

2. Add them to .dlt/secrets.toml

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[sources.fleetio_management_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 Fleetio 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 fleetio_management_pipeline.py

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

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Pipeline fleetio_management_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fleetio_management_data
The duckdb destination used duckdb:/fleetio_management.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 fleetio_management_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 vehicles and fuel_entries from the Fleetio 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 fleetio_management_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://secure.fleetio.com/api/v1/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "vehicles", "endpoint": {"path": "vehicles"}},
            {"name": "fuel_entries", "endpoint": {"path": "fuel_entries"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fleetio_management_pipeline",
        destination="duckdb",
        dataset_name="fleetio_management_data",
    )
    load_info = pipeline.run(fleetio_management_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("fleetio_management_pipeline").dataset()
sessions_df = data.vehicles.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fleetio_management_data.vehicles LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fleetio_management_pipeline").dataset()
data.vehicles.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 Fleetio 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 your Authorization header is set to 'Token YOUR_API_KEY' (including the leading word 'Token') and that Account-Token header contains the correct account token. The /accounts endpoint is an exception and does not require Account-Token.

Permission errors (403 Forbidden)

403 responses indicate the API key's associated user lacks permissions for the requested resource—adjust user roles/permissions in Fleetio or use a different API key tied to an appropriate user.

Rate limits and pagination

Index endpoints are paginated. Fleetio returns pagination information via response headers (X-Pagination-Limit, X-Pagination-Current-Page, X-Pagination-Total-Pages, X-Pagination-Total-Count). Current default page size is 100 but may change; follow headers rather than hardcoding. Handle pagination by iterating pages.

Common errors (4xx/5xx)

401 Unauthorized: bad/missing Authorization or Account-Token. 403 Forbidden: insufficient permissions. 404 Not Found: invalid resource id. 422 Unprocessable Entity: validation errors on create/update. 500/502/503: 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