Stark Bank Python API Docs | dltHub

Stark Bank is a payments and banking API platform offering REST endpoints for creating and managing bank transfers, transactions, webhooks, public keys and related financial resources. The REST API base URL is Production: https://api.starkbank.com Sandbox: https://sandbox.api.starkbank.com and All requests require ECDSA‑signed custom headers (Access-Id, Access-Time, Access-Signature) or SDKs that perform that signing for you..

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 Stark Bank data in under 10 minutes.

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What data can I load from Stark Bank?

Here are some of the endpoints you can load from Stark Bank:

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How do I authenticate with the Stark Bank API?

Authentication uses ECDSA signatures over a message composed of Access-Id + ':' + Access-Time + ':' + bodyString. Requests must include headers Access-Id, Access-Time and Access-Signature (base64 of ECDSA signature). SDKs handle header generation automatically.

1. Get your credentials

1. Create a workspace (digital account) on Stark Bank / Stark Infra dashboard (Sandbox or Production).
2. In the dashboard, create or view your project/organization/workspace id to form Access-Id (project/{id} or organization/{id}/workspace/{id}).
3. Generate or upload your ECDSA private key (PEM) in the dashboard or use SDK instructions to load your private key locally.
4. Use the private key to sign message (Access-Id:Access-Time:body) producing Access-Signature.
5. For webhook verification, GET /v2/public-key returns the provider public key.

2. Add them to .dlt/secrets.toml

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[sources.stark_bank_source]
access_id = "project/your_project_id"
private_key_pem = "-----BEGIN EC PRIVATE KEY-----\n...\n-----END EC PRIVATE KEY-----"

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 Stark Bank 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 stark_bank_pipeline.py

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

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Pipeline stark_bank_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset stark_bank_data
The duckdb destination used duckdb:/stark_bank.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 stark_bank_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 transfers and public_keys from the Stark Bank 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 stark_bank_source(access_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Production: https://api.starkbank.com   Sandbox: https://sandbox.api.starkbank.com",
            "auth": {
                "type": "http_ecdsa_custom",
                "private_key_pem": access_id,
            },
        },
        "resources": [
            {"name": "transfers", "endpoint": {"path": "v2/transfer", "data_selector": "transfers"}},
            {"name": "public_keys", "endpoint": {"path": "v2/public-key", "data_selector": "publicKeys"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="stark_bank_pipeline",
        destination="duckdb",
        dataset_name="stark_bank_data",
    )
    load_info = pipeline.run(stark_bank_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("stark_bank_pipeline").dataset()
sessions_df = data.transfers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM stark_bank_data.transfers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("stark_bank_pipeline").dataset()
data.transfers.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 Stark Bank 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 your Access-Signature or Access-Id is invalid you'll receive 4xx errors. Ensure Access-Time is current unix time and your message format is "Access-Id:Access-Time:bodyString" (empty bodyString for GET). Verify signature using the public key from GET /v2/public-key.

Pagination and cursors

List endpoints return at most 100 objects per page and a cursor string in the response (cursor can be null). Send ?cursor={cursor} to fetch the next page. SDKs manage cursor iteration automatically.

Error format

Errors are returned as JSON { "errors":[{"code":"...","message":"..."}] }. HTTP status codes follow standard semantics (400 for bad input, 500 for server error).

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