Edrive Auto Python API Docs | dltHub

Build a Edrive Auto-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

Last updated: Mar 13, 2026

The E-Drive Auto REST API documentation is available at https://edriveauto.com/api. It returns vehicle makes in JSON format. E-Drive Auto's primary focus is on increasing on-page conversions. The REST API base URL is https://api.edriveauto.com/api/ and Login (POST /Account/login) returns an accessToken; use it as a Bearer token for subsequent requests..

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 Edrive Auto data in under 10 minutes.

What data can I load from Edrive Auto?

Here are some of the endpoints you can load from Edrive Auto:

Resource	Endpoint	Method	Description
get_makes	TradeVue/get-makes?year={year}	GET	Returns list of vehicle makes for a given year (top‑level array of {id, name}).
get_models	TradeVue/get-models?makeID={makeID}&year={year}	GET	Returns list of vehicle models for a make and year (top‑level array of {id, name}).
get_trim_styles	TradeVue/get-trim-styles?modelID={modelID}&year={year}	GET	Returns list of trim styles for a model and year (top‑level array of {id, name}).
get_body_styles	TradeVue/Get-body-styles?modelID={modelID}&year={year}&trimID={trimID}	GET	Returns list of body styles (top‑level array of {id, name}).
get_engines	TradeVue/get-engines?modelID={modelID}&year={year}&trimID={trimID}&bodyID={bodyID}	GET	Returns list of engines (top‑level array of {id, name}).
get_transmissions	TradeVue/get-transmissions?modelID={modelID}&year={year}&trimID={trimID}&bodyID={bodyID}&engineID={engineID}	GET	Returns list of transmissions (top‑level array of {id, name}).
get_trade_in	TradeVue/get-trade-in?...&mileage={mileage}&zipCode={zipCode}&optionIdCslList={optionIdCslList}	GET	Returns appraisal/pricing object (averagePrice, cleanPrice, etc.).
decode_vin	TradeVue/decode-vin?vin={vin}	GET	VIN decoder returns top‑level array of vehicle objects with detailed fields.

How do I authenticate with the Edrive Auto API?

Obtain credentials (Unique Key and password) from E-Drive Auto and POST JSON to https://api.edriveauto.com/api/Account/login with username and password. The response contains accessToken and refreshToken. Send Authorization: Bearer on other endpoints.

1. Get your credentials

Contact E-Drive Auto (sales/support) to request API access — docs list contacting sanjar@edriveauto.com.
E-Drive Auto will provide your Unique Key (use as username) and a password.
Call POST https://api.edriveauto.com/api/Account/login with JSON {"username": "", "password": "", "role": "Dealer"} to receive accessToken and refreshToken.
Use Authorization: Bearer header on GET calls.
Refresh the token using POST /api/Account/refresh-token with {"refreshToken": ""} when needed.

2. Add them to .dlt/secrets.toml


[sources.edrive_auto_source]
username = "YOUR_UNIQUE_KEY"
password = "YOUR_PASSWORD"
# Or store an access_token after login
access_token = "your_jwt_access_token"

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.

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:


uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:


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:


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 Edrive Auto 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:


python edrive_auto_pipeline.py

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


Pipeline edrive_auto_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset edrive_auto_data
The duckdb destination used duckdb:/edrive_auto.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:


dlt pipeline edrive_auto_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.

Python pipeline example

This example loads get_makes and decode_vin from the Edrive Auto API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:


import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def edrive_auto_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.edriveauto.com/api/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "get_makes", "endpoint": {"path": "TradeVue/get-makes?year={year}"}},
            {"name": "get_models", "endpoint": {"path": "TradeVue/get-models?makeID={makeID}&year={year}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="edrive_auto_pipeline",
        destination="duckdb",
        dataset_name="edrive_auto_data",
    )
    load_info = pipeline.run(edrive_auto_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.

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):


import dlt

data = dlt.pipeline("edrive_auto_pipeline").dataset()
sessions_df = data.get_makes.df()
print(sessions_df.head())

SQL (DuckDB example):


SELECT * FROM edrive_auto_data.get_makes LIMIT 10;

In a marimo or Jupyter notebook:


import dlt

data = dlt.pipeline("edrive_auto_pipeline").dataset()
data.get_makes.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

What destinations can I load Edrive Auto data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Destination	Example value
DuckDB (local, default)	`"duckdb"`
PostgreSQL	`"postgres"`
BigQuery	`"bigquery"`
Snowflake	`"snowflake"`
Redshift	`"redshift"`
Databricks	`"databricks"`
Filesystem (S3, GCS, Azure)	`"filesystem"`

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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.


dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

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