Refersion Python API Docs | dltHub

Refersion is an affiliate marketing platform and API for managing affiliates, conversions and commissions. The REST API base URL is https://api.refersion.com/v2 and All requests require an API key (Secret Key) from your Refersion account..

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 Refersion data in under 10 minutes.

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What data can I load from Refersion?

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

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How do I authenticate with the Refersion API?

Refersion uses API keys (Refersion Secret Key) that you generate in your account settings. Include the key in requests per the Refersion docs (see dashboard instructions to obtain the key).

1. Get your credentials

1. Log in to your Refersion account.
2. From the top menu go to Account > Settings.
3. Open the Refersion API / Integrations > Refersion API section.
4. If no API key exists click "Generate New Key" and then click "Show" to reveal the Secret Key.
5. Copy the Secret Key to use in your requests.

2. Add them to .dlt/secrets.toml

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[sources.refersion_source]
api_key = "your_refersion_secret_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 Refersion 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 refersion_pipeline.py

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

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Pipeline refersion_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset refersion_data
The duckdb destination used duckdb:/refersion.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 refersion_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 affiliate_list and affiliate_get from the Refersion 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 refersion_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.refersion.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "affiliate_list", "endpoint": {"path": "affiliate/list"}},
            {"name": "affiliate_get", "endpoint": {"path": "affiliate/get"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="refersion_pipeline",
        destination="duckdb",
        dataset_name="refersion_data",
    )
    load_info = pipeline.run(refersion_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("refersion_pipeline").dataset()
sessions_df = data.affiliate_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM refersion_data.affiliate_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("refersion_pipeline").dataset()
data.affiliate_list.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 Refersion 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 you receive 401/403 errors, confirm you copied the correct Secret Key from Account > Settings > Refersion API, and that you include it in requests according to the Refersion docs. Regenerate the key from the dashboard if needed.

Rate limits and HTTP error responses

The REST API uses standard HTTP status codes. If you receive 429, implement exponential backoff and retry. For 4xx and 5xx errors inspect the response body for error details.

Missing or unexpected data selectors

Documentation pages do not always list the exact JSON top-level key for arrays of records. When building your pipeline, run a test call (or use the docs "Try It" feature) to capture a canonical response and confirm the data selector (e.g., top-level array or a field such as "affiliates" or "data").

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