Webpay Python API Docs | dltHub

Webpay is Transbank's payment processing REST API for creating, confirming, querying and managing card transactions (Webpay Plus and Mall). The REST API base URL is https://webpay3g.transbank.cl and all requests require Tbk-Api-Key-Id and Tbk-Api-Key-Secret headers.

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 add "dlt[hub]" and start loading Webpay data in under 10 minutes.

---

What data can I load from Webpay?

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

---

How do I authenticate with the Webpay API?

Authentication uses two mandatory HTTP request headers: Tbk-Api-Key-Id for the commerce code and Tbk-Api-Key-Secret for the API secret key. Communication must be conducted over a secure TLS 1.2 connection, and payloads must use Content-Type: application/json.

1. Get your credentials

To obtain production credentials for the Transbank Webpay REST API, you must complete a validation process. Start by visiting the official Transbank Developers portal and locating the validation form designated for plugin integration. Submit the required business and technical details. Once Transbank support reviews and approves your submission, you will be notified and provided with your production-ready 'Tbk-Api-Key-Secret' and confirmed 'Tbk-Api-Key-Id' (commerce code). During the development phase, you may use publicly available integration (test) credentials provided in the Transbank developers' documentation to perform initial testing.

2. Add them to .dlt/secrets.toml

Copy
[sources.webpay_source]
tbk_api_key_id = "REPLACE_ME"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

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 Webpay 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:

Copy
uv run python webpay_pipeline.py

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

Copy
Pipeline webpay_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset webpay_data
The duckdb destination used duckdb:/webpay.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads /rswebpaytransaction/api/webpay/v1.4/transactions and /rswebpaytransaction/api/webpay/v1.4/transactions/{token} from the Webpay API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def webpay_source(tbk_api_key_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://webpay3g.transbank.cl",
            "auth": {"type": "api_key", "api_key": tbk_api_key_id, "name": "tbk_api_key_id"},
        },
        "resources": [
            {"name": "transactions_status", "endpoint": {"path": "rswebpaytransaction/api/webpay/v1.2/transactions/{token}"}},
            {"name": "transactions_create", "endpoint": {"path": "rswebpaytransaction/api/webpay/v1.2/transactions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="webpay_pipeline",
        destination="duckdb",
        dataset_name="webpay_data",
    )
    load_info = pipeline.run(webpay_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.

---

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):

Copy
import dlt

data = dlt.pipeline("webpay_pipeline").dataset()
sessions_df = data.transactions_status.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM webpay_data.transactions_status LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("webpay_pipeline").dataset()
data.transactions_status.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Webpay 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.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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