Cielo E-commerce Python API Docs | dltHub

Cielo E-commerce API offers flexible, secure payment solutions for e-commerce. It provides high conversion rates and no monthly fees. Detailed documentation and free API testing are available. The REST API base URL is https://apisandbox.cieloecommerce.cielo.com.br (transactional sandbox), https://apiquerysandbox.cieloecommerce.cielo.com.br (sandbox queries), https://apiquery.cieloecommerce.cielo.com.br (production queries) and all requests require MerchantId and MerchantKey HTTP headers for authentication.

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 Cielo E-commerce data in under 10 minutes.

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What data can I load from Cielo E-commerce?

Here are some of the endpoints you can load from Cielo E-commerce:

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How do I authenticate with the Cielo E-commerce API?

Cielo uses two API credentials (MerchantId and MerchantKey) that must be sent as HTTP headers on each request. Use the MerchantId and MerchantKey provided in your Cielo account; query endpoints are on the query host and transactional operations on the transactional host.

1. Get your credentials

1. Log in to https://www.cielo.com.br with your merchant account.
2. Go to My Cielo Account (select the establishment if needed) and click E-commerce.
3. In Gerenciamento de Credenciais, open Credencial API and click Visualizar.
4. Authenticate with the token sent to you (via management app or e‑mail) to reveal MerchantId and MerchantKey; copy them within 3 minutes.

2. Add them to .dlt/secrets.toml

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[sources.cielo_ecommerce_source]
merchant_id = "your_merchant_id_here"
merchant_key = "your_merchant_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 Cielo E-commerce 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 cielo_ecommerce_pipeline.py

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

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Pipeline cielo_ecommerce_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cielo_ecommerce_data
The duckdb destination used duckdb:/cielo_ecommerce.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 cielo_ecommerce_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 sales_by_payment_id and sales_by_merchantorderid from the Cielo E-commerce 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 cielo_ecommerce_source(merchant_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apisandbox.cieloecommerce.cielo.com.br (transactional sandbox), https://apiquerysandbox.cieloecommerce.cielo.com.br (sandbox queries), https://apiquery.cieloecommerce.cielo.com.br (production queries)",
            "auth": {
                "type": "api_key",
                "merchant_key": merchant_key,
            },
        },
        "resources": [
            {"name": "sales_by_payment_id", "endpoint": {"path": "1/sales/{paymentId}"}},
            {"name": "sales_by_merchantorderid", "endpoint": {"path": "1/sales?merchantOrderId={merchantOrderId}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cielo_ecommerce_pipeline",
        destination="duckdb",
        dataset_name="cielo_ecommerce_data",
    )
    load_info = pipeline.run(cielo_ecommerce_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("cielo_ecommerce_pipeline").dataset()
sessions_df = data.sales_by_payment_id.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cielo_ecommerce_data.sales_by_payment_id LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cielo_ecommerce_pipeline").dataset()
data.sales_by_payment_id.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 Cielo E-commerce 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