PayaConnect Python API Docs | dltHub

Paya Connect API uses HTTPS/TLS1.2, with sandbox host name api.sandbox.payaconnect.com. Version 2 will be non-backwards compatible. Test transaction amounts are provided for error responses. The REST API base URL is https://api.payaconnect.com and all requests require either user-id + user-api-key headers (or query params), or a short-lived access-token obtained from POST /v2/token.

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 PayaConnect data in under 10 minutes.

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What data can I load from PayaConnect?

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

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How do I authenticate with the PayaConnect API?

Requests accept authentication via request headers "developer-id", "user-id" and "user-api-key" (underscores replaced by dashes), or via query parameters; alternatively obtain an access token with POST /v2/token and pass it as the "access-token" query param or header. Tokens expire after 15 minutes of idle time.

1. Get your credentials

1. In Paya Connect dashboard create or view a User and generate a user_api_key (Users endpoint in docs describes how).
2. Note the user_id and developer_id for your account.
3. Use these values as headers: "developer-id", "user-id", "user-api-key" (replace underscores with dashes) or pass as query params.
4. Or POST /v2/token with username/password/domain to receive a token to send as "access-token" header or query param.

2. Add them to .dlt/secrets.toml

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[sources.paya_connect_source]
user_id = "your_user_id"
user_api_key = "your_user_api_key"
developer_id = "your_developer_id"

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 PayaConnect 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 paya_connect_pipeline.py

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

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Pipeline paya_connect_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset paya_connect_data
The duckdb destination used duckdb:/paya_connect.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 paya_connect_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 contacts from the PayaConnect 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 paya_connect_source(user_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.payaconnect.com",
            "auth": {
                "type": "api_key",
                "user_api_key": user_api_key,
            },
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "v2/transactions", "data_selector": "transactions"}},
            {"name": "contacts", "endpoint": {"path": "v2/contacts", "data_selector": "contacts"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM paya_connect_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("paya_connect_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 PayaConnect 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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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