Pandablue Python API Docs | dltHub

Pandablue is a payments platform providing REST APIs for deposits, cashouts, subscriptions, KYC and quickpay workflows. The REST API base URL is https://api-stg.directa24.com and Authentication uses Bearer read‑only keys for some endpoints and HTTP Basic (API key as username) for others..

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 Pandablue data in under 10 minutes.

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What data can I load from Pandablue?

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

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How do I authenticate with the Pandablue API?

Most Deposits endpoints require an Authorization header with a Bearer read‑only API key (Authorization: Bearer <read_only_key>). Some product APIs require HTTP Basic where the API Key is provided as the basic auth username.

1. Get your credentials

1. Sign in to your Pandablue merchant dashboard. 2) Navigate to the API Keys / Developers or Security section. 3) Create or copy the Read‑Only API Key for listing endpoints (Deposits payment_methods) or the API Key for Cashouts/Subscriptions as instructed. 4) Store the key securely; use it as a Bearer token for Deposits endpoints or as the HTTP Basic username when required.

2. Add them to .dlt/secrets.toml

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[sources.pandablue_kyc_api_source]
api_key = "your_pandablue_api_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 Pandablue 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 pandablue_kyc_api_pipeline.py

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

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Pipeline pandablue_kyc_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pandablue_kyc_api_data
The duckdb destination used duckdb:/pandablue_kyc_api.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 pandablue_kyc_api_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 payment_methods and cashout_creation from the Pandablue 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 pandablue_kyc_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-stg.directa24.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "payment_methods", "endpoint": {"path": "v3/payment_methods?country={country}"}},
            {"name": "cashout_creation", "endpoint": {"path": "v3/cashouts", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pandablue_kyc_api_pipeline",
        destination="duckdb",
        dataset_name="pandablue_kyc_api_data",
    )
    load_info = pipeline.run(pandablue_kyc_api_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("pandablue_kyc_api_pipeline").dataset()
sessions_df = data.payment_methods.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pandablue_kyc_api_data.payment_methods LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pandablue_kyc_api_pipeline").dataset()
data.payment_methods.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 Pandablue 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 Invalid credentials or code 100 / "Invalid credentials", verify the API key used in the Authorization header. Deposits payment_methods requires: Authorization: Bearer <read_only_key>. Some product APIs require HTTP Basic with API key as username—use the correct credential type for the endpoint.

Rate limits and request errors

The docs indicate standard HTTP error codes. For 400/422 verify request query parameters (e.g., country param). For 4xx/5xx check the API Codes page for Cashouts‑specific status codes and descriptions.

Pagination and response shapes

Payment methods return a top‑level array of method objects (no wrapper key). Other endpoints (cashouts, subscriptions, quickpay) may return objects with a data field or nested structures—consult each endpoint page for the exact JSON key when implementing selectors.

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