Load Renault API data to DuckDB

The Renault API (often referred to as the Kamereon API) enables interaction with Renault vehicle services including status monitoring, data retrieval, and remote actions for the MyRenault application ecosystem. Everything needed to build a working Renault API → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Renault API to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Renault API to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Renault API API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Renault API API at a glance

These values come from the Renault API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Renault API API?

Authentication requires a Gigya JWT passed in headers. Access tokens are obtained by using a login token (refresh token) to authenticate against the Gigya service.

1. Get your credentials

The Renault API utilized by the MyRenault application is a private, undocumented interface. Official credentials (e.g., Client ID/Secret) are not provided to individual developers through a public dashboard. Authentication is typically handled by the renault-api Python package, which manages login via a username/password exchange for a Gigya refresh token. To obtain credentials for automation, use the renault-api CLI: 1. Install via pip install renault-api[cli]. 2. Run renault-api login --user <your_email> --password <your_password>. This creates a session stored at ~/.credentials/renault-api.json. Alternatively, you can authenticate programmatically in your pipeline using client.session.login('email', 'password') and persist the client.session.login_token securely to bypass future password prompts.

2. Add them to .dlt/secrets.toml

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[sources.renault_api_source]
renault_username = "your_email_address"
renault_password = "your_password"
kamereon_api_key = "VAX7XYKGfa92yMvXculCkEFyfZbuM7Ss"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Renault API data can I load into DuckDB?

These are the Renault API endpoints dlt can load into DuckDB:

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How do I load only new Renault API records?

The Renault API API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "cockpit", "endpoint": {
    "path": "commerce/v1/accounts/{account_id}/kamereon/kca/car-adapter/v1/cars/{vin}/cockpit",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Renault API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading get_account_vehicles and get_vehicle_data from the Renault API API into DuckDB:

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

@dlt.source
def renault_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-wired-prod-1-euw1.kamereon.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "cockpit", "endpoint": {"path": "commerce/v1/accounts/{account_id}/kamereon/kca/car-adapter/v1/cars/{vin}/cockpit"}},
            {"name": "battery_status", "endpoint": {"path": "commerce/v1/accounts/{account_id}/kamereon/kca/car-adapter/v1/cars/{vin}/battery-status"}}
        ],
    }
    yield from rest_api_resources(config)


def load_renault_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="renault_api_pipeline",
        destination="duckdb",
        dataset_name="renault_api_data",
    )
    load_info = pipeline.run(renault_api_source())
    print(load_info)


if __name__ == "__main__":
    load_renault_api_to_duckdb()

Run it with python renault_api_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Renault API data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("renault_api_pipeline").dataset()
df = data.cockpit.df()
print(df.head())

SQL:

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SELECT * FROM renault_api_data.cockpit LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Renault API to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Renault API loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Renault API data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack