Valor API Python API Docs | dltHub

Valor API is a payment gateway and merchant management REST API used to process transactions and manage merchants, devices, and invoices. The REST API base URL is api.valorpaytech.com and All requests require a Bearer token generated from your App ID/App Key (Authorization: Bearer )..

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 Valor API data in under 10 minutes.

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What data can I load from Valor API?

Here are some of the endpoints you can load from Valor API:

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How do I authenticate with the Valor API API?

Authentication uses a bearer token passed in the Authorization header as "Bearer ".

1. Get your credentials

1. Log into the Valor merchant portal.
2. Navigate to the API Keys section.
3. Create or view your App ID and App Key.
4. Call the Merchant Create Bearer Token endpoint with these credentials to obtain a bearer token.

2. Add them to .dlt/secrets.toml

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[sources.valor_api_source]
api_key = "your_app_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 Valor API 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 valor_api_pipeline.py

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

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Pipeline valor_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset valor_api_data
The duckdb destination used duckdb:/valor_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 valor_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 transactions and merchants from the Valor API 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 valor_api_source(app_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "api.valorpaytech.com",
            "auth": {
                "type": "bearer",
                "token": app_key,
            },
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "transactions", "data_selector": "transactions"}},
            {"name": "merchants", "endpoint": {"path": "merchants", "data_selector": "merchants"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="valor_api_pipeline",
        destination="duckdb",
        dataset_name="valor_api_data",
    )
    load_info = pipeline.run(valor_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("valor_api_pipeline").dataset()
sessions_df = data.transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM valor_api_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("valor_api_pipeline").dataset()
data.transactions.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 Valor API 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 token generation fails, verify App ID and App Key from your portal and that you called the Merchant Create Bearer Token endpoint correctly. Ensure the returned bearer token is included as: Authorization: Bearer . Use the Check Credentials API to validate processor/merchant credentials.

Pagination and large result sets

Many list endpoints support limit/offset pagination. Use limit and offset parameters to page through results. Responses may be versioned; confirm the response body key that contains the record array (e.g., "transactions", "merchants", "products").

Rate limiting and errors

Documentation describes standard API errors for invalid credentials, missing parameters, and processor declines for transaction operations. Handle HTTP 401 for auth issues, 400 for bad requests, 429 for rate limits (throttle and retry), and 5xx for server errors with exponential backoff.

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