Bringg Python API Docs | dltHub

Bringg is a delivery orchestration platform offering REST APIs for managing orders, deliveries, drivers, and related resources. The REST API base URL is https://us2-api.bringg.com/v2 and all requests require service or OAuth2-based credentials (Bearer token / service URL) 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 Bringg data in under 10 minutes.

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What data can I load from Bringg?

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

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How do I authenticate with the Bringg API?

Bringg supports two main authentication methods: service URLs (unique service tokens/URLs generated in the Bringg admin) and OAuth 2.0. API calls require including the service token as a Bearer token in the Authorization header or calling the service-specific URL for service-style auth. OAuth 2.0 flows use standard token endpoints and Bearer tokens.

1. Get your credentials

1. Sign in to Bringg as an Admin user.2. In Bringg Admin, create an API application (OAuth 2.0 app) or generate a service URL/service credentials under API Access / Integrations.3. For service URLs note the full service URL (format: https://-admin-api.bringg.com/services/<service_name>); for OAuth create client credentials and exchange code for a token per OAuth 2.0 URLs doc.4. Store the returned access token or service URL securely; tokens are used as Bearer tokens in Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.bringg_order_service_quotes_source]
# place under [sources.bringg_source]
token = "your_bringg_bearer_token_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 Bringg 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 bringg_order_service_quotes_pipeline.py

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

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Pipeline bringg_order_service_quotes_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bringg_order_service_quotes_data
The duckdb destination used duckdb:/bringg_order_service_quotes.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 bringg_order_service_quotes_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 orders and drivers from the Bringg 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 bringg_order_service_quotes_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://us2-api.bringg.com/v2",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders", "data_selector": "orders"}},
            {"name": "drivers", "endpoint": {"path": "drivers", "data_selector": "drivers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bringg_order_service_quotes_pipeline",
        destination="duckdb",
        dataset_name="bringg_order_service_quotes_data",
    )
    load_info = pipeline.run(bringg_order_service_quotes_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("bringg_order_service_quotes_pipeline").dataset()
sessions_df = data.orders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bringg_order_service_quotes_data.orders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bringg_order_service_quotes_pipeline").dataset()
data.orders.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 Bringg 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 or 403 Forbidden, verify you are using the correct regional service URL or Bearer token. Ensure token has not expired and your app has the required scopes/permissions. For OAuth flows verify client_id/client_secret and token exchange completed.

Rate limiting

Bringg enforces rate limits per account/region. If you receive 429 Too Many Requests, implement exponential backoff and retry after the Retry-After header. Batch calls where possible and paginate results.

Pagination

Many list endpoints are paginated. Use the provided paging parameters (page, page_size or offset/limit depending on endpoint) and follow response pagination metadata (pages, page, total) returned in the response. Iterate until no more pages.

Quotes and Fulfillment flow

Quotes are requested via the quotes endpoint (POST /v2/orders/quotes) which returns an array in the 'quotes' key. To start fulfillment for an order using a chosen quote, call POST /v2/orders/{order_id}/fulfill/{quote_id} (Fulfill Order by Quote ID).

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