Gravity Payments Python API Docs | dltHub

Gravity Payments is a payments platform providing EmergePay (cloud transaction API), Cloud Services (terminal REST interface), and Account API for merchant onboarding. The REST API base URL is https://api.emergepay.chargeitpro.com (production) | https://api.emergepay-sandbox.chargeitpro.com (sandbox) and All API requests require an Authorization header with a non‑expiring partner 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 Gravity Payments data in under 10 minutes.

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What data can I load from Gravity Payments?

Here are some of the endpoints you can load from Gravity Payments:

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How do I authenticate with the Gravity Payments API?

Create a non‑expiring partner authorization token in the Gravity Payments Dashboard (separate tokens for sandbox and production). Supply it in the HTTP Authorization header for requests.

1. Get your credentials

1. Request Dashboard access from Developer Support if you don't have one.
2. Log into the Gravity Payments Dashboard.
3. Create a partner authorization token for sandbox and/or production.
4. Copy the token and store it securely; use it in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.gravity_payments_source]
api_token = "your_partner_token_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 Gravity Payments 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 gravity_payments_pipeline.py

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

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Pipeline gravity_payments_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset gravity_payments_data
The duckdb destination used duckdb:/gravity_payments.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 gravity_payments_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 retrieve_transaction and device_transactions from the Gravity Payments 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 gravity_payments_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.emergepay.chargeitpro.com (production) | https://api.emergepay-sandbox.chargeitpro.com (sandbox)",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "retrieve_transaction", "endpoint": {"path": "virtualterminal/v1/orgs/{oid}/transactions/{externalTransactionId}", "data_selector": "transactionResponse"}},
            {"name": "device_transactions", "endpoint": {"path": "device/v1/deviceTransactions/{transactionToken}", "data_selector": "transactionResponse"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="gravity_payments_pipeline",
        destination="duckdb",
        dataset_name="gravity_payments_data",
    )
    load_info = pipeline.run(gravity_payments_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("gravity_payments_pipeline").dataset()
sessions_df = data.device_transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM gravity_payments_data.device_transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("gravity_payments_pipeline").dataset()
data.device_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 Gravity Payments 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 (401)

When the Authorization header is missing, malformed, or contains an invalid token the API returns 401 Unauthorized. Ensure you are using the correct non‑expiring partner token and that it is included exactly as Authorization: Bearer <token>.

Invalid input (400)

A 400 Bad Request indicates missing required path parameters or malformed request payloads. Verify that placeholders like {oid}, {transactionToken}, etc., are correctly substituted.

Not found (404)

404 Not Found is returned when the requested resource – for example a transaction ID or device token – does not exist. Check the identifiers for typos.

Rate limiting (429)

If you exceed the allowed request rate, the service responds with 429 Too Many Requests. Implement exponential back‑off and respect any Retry-After header.

Server errors (500‑504)

Transient server‑side problems yield 5xx responses. Retry the request after a short delay; persistent failures should be reported to Gravity Payments support.

EmergePay specific conflicts (409 / 424)

409 Conflict can occur when attempting to process a transaction that has already been finalized. 424 Failed Dependency indicates a dependent operation (e.g., device initialization) failed, often due to prior validation errors.

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