Fintech Primitives Python API Docs | dltHub

Fintech Primitives is a platform offering financial APIs for payments, KYC, and related banking services. The REST API base URL is https://api.fintechprimitives.com and All requests require a Bearer token obtained via OAuth2 client‑credentials and an x‑tenant‑id header..

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 Fintech Primitives data in under 10 minutes.

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What data can I load from Fintech Primitives?

Here are some of the endpoints you can load from Fintech Primitives:

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How do I authenticate with the Fintech Primitives API?

Obtain a Bearer token via POST /v2/auth/{tenant_name}/token using client_id and client_secret, then include Authorization: Bearer <access_token> and x-tenant-id: <tenant_name> in all API calls.

1. Get your credentials

1. Send an email to customerservice@cybrilla.com requesting API access as an OAuth 2.0 client.
2. The support team replies with a client_id, client_secret, and tenant_name.
3. Store these values securely; they will be used to call the token endpoint /v2/auth/{tenant_name}/token to obtain a Bearer token.
4. Optionally, test the token request with a curl command:

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   curl -X POST "https://api.fintechprimitives.com/v2/auth/<tenant_name>/token" \
        -d "client_id=<client_id>&client_secret=<client_secret>&grant_type=client_credentials"

2. Add them to .dlt/secrets.toml

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[sources.fintech_primitives_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
tenant_name = "your_tenant_name_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 Fintech Primitives 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 fintech_primitives_pipeline.py

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

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Pipeline fintech_primitives_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fintech_primitives_data
The duckdb destination used duckdb:/fintech_primitives.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 fintech_primitives_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 files and payments from the Fintech Primitives 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 fintech_primitives_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fintechprimitives.com",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "resources": [
            {"name": "files", "endpoint": {"path": "v2/files", "data_selector": "data"}},
            {"name": "payments", "endpoint": {"path": "v2/payments", "data_selector": "payments"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fintech_primitives_pipeline",
        destination="duckdb",
        dataset_name="fintech_primitives_data",
    )
    load_info = pipeline.run(fintech_primitives_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("fintech_primitives_pipeline").dataset()
sessions_df = data.files.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fintech_primitives_data.files LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fintech_primitives_pipeline").dataset()
data.files.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 Fintech Primitives 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 the token request returns a 401 or 403, verify that the client_id, client_secret, and tenant_name are correct and that the account has been approved.

Rate limits

The API enforces a rate limiter. Production limits are ~100 read and 100 write operations per second; exceeding this returns HTTP 429. Reduce request frequency or implement exponential back‑off.

Pagination quirks

List endpoints include pagination fields such as last, total_pages, number, and the array of records under keys like data, payments, or amcs. Continue fetching while last is false.

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