Bridge Python API Docs | dltHub

Bridge is an Open Banking and payments orchestration API that enables account aggregation, initiating payments, and KYC/customer management. The REST API base URL is https://api.sandbox.bridge.xyz and All requests require an Api-Key header with your API key..

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 Bridge data in under 10 minutes.

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What data can I load from Bridge?

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

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How do I authenticate with the Bridge API?

Bridge uses static API keys passed in the Api-Key HTTP header. For write requests you may also include an Idempotency-Key header. Content-Type: application/json for JSON payloads.

1. Get your credentials

1. Create or log in to an account at https://dashboard.bridge.xyz/.
2. Request developer (sandbox) access if needed.
3. Enable the Sandbox toggle in the dashboard.
4. Generate a sandbox API key (prefixed with sk-test).
5. Copy the key and use it in the Api-Key header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.bridge_source]
api_key = "sk-test_your_sandbox_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 Bridge 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 bridge_pipeline.py

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

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Pipeline bridge_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bridge_data
The duckdb destination used duckdb:/bridge.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 bridge_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 customers and accounts from the Bridge 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 bridge_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.sandbox.bridge.xyz",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "v0/customers", "data_selector": "customers"}},
            {"name": "accounts", "endpoint": {"path": "v0/accounts", "data_selector": "accounts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bridge_pipeline",
        destination="duckdb",
        dataset_name="bridge_data",
    )
    load_info = pipeline.run(bridge_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("bridge_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bridge_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bridge_pipeline").dataset()
data.customers.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 Bridge 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 401/403 responses, verify you are sending the Api-Key header with a valid sandbox or production key. Sandbox keys are prefixed with sk-test. Ensure you have selected Sandbox in the dashboard when using sandbox keys.

Rate limits and throttling

Bridge enforces rate limits; slow down retries, respect the Retry-After header when present, and implement exponential backoff for 429 responses.

Pagination

List endpoints return arrays under keys like customers, accounts, transactions and may include pagination metadata. Follow the response's pagination fields (e.g., page or next_cursor) where provided.

Idempotency and write operations

Use the Idempotency-Key header for POST requests that create resources to avoid duplicate side effects.

Common API errors

• 400 Bad Request
• 401 Unauthorized (invalid or missing Api-Key)
• 403 Forbidden (insufficient permissions)
• 404 Not Found
• 429 Too Many Requests
• 500+ Server Errors

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