Everflow Python API Docs | dltHub

Everflow is an affiliate marketing and ad tracking platform that exposes REST APIs for managing networks, advertisers, affiliates, offers and reporting. The REST API base URL is https://api.eflow.team/v1 and All requests require an API key passed in the X-Eflow-API-Key header..

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 Everflow data in under 10 minutes.

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What data can I load from Everflow?

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

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How do I authenticate with the Everflow API?

Authentication uses per‑portal API Keys. Add header "X-Eflow-API-Key: <YOUR_API_KEY>" to every request. Responses (including errors) are returned as JSON and requests must use HTTPS.

1. Get your credentials

1. Log in to your Everflow account.
2. For Network API keys: open Control Center > Security > API Keys (or Security) and create a new Network API key. Copy the key shown once at creation.
3. For Affiliate/Advertiser scoped keys: open the corresponding Manage Affiliate / Managed Advertiser > API / API Keys tab and request/create the key; the key is shown only at creation.
4. Store the key securely and revoke immediately if compromised. If you are unsure whether your account is EU‑hosted, contact your account manager.

2. Add them to .dlt/secrets.toml

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[sources.everflow_source]
api_key = "your_eflow_api_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 Everflow 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 everflow_pipeline.py

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

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Pipeline everflow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset everflow_data
The duckdb destination used duckdb:/everflow.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 everflow_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 advertisers and offers from the Everflow 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 everflow_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.eflow.team/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "advertisers", "endpoint": {"path": "networks/advertisers", "data_selector": "advertisers"}},
            {"name": "offers", "endpoint": {"path": "networks/offers", "data_selector": "offers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="everflow_pipeline",
        destination="duckdb",
        dataset_name="everflow_data",
    )
    load_info = pipeline.run(everflow_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("everflow_pipeline").dataset()
sessions_df = data.advertisers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM everflow_data.advertisers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("everflow_pipeline").dataset()
data.advertisers.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 Everflow 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

Requests missing X-Eflow-API-Key or using a key from the wrong portal will return 403 Forbidden with the message Out of realm. Ensure the correct scoped API key is supplied in the header.

Pagination

Collection endpoints return a paging object with page, page_size, and total_count. Iterate pages by adjusting the page query parameter until the total count is reached.

Rate limits and large reporting exports

Reporting endpoints (e.g., conversions, firehose) can return large payloads. Respect documented rate limits; if limits are hit, implement exponential backoff or contact Everflow support.

Common error responses

• 401 Unauthorized – missing or invalid API key.
• 403 Forbidden – wrong portal key or insufficient permissions.
• 404 Not Found – requested resource does not exist.
• 400 / 422 – invalid request parameters. All errors are returned as JSON objects containing an error field and a descriptive message.

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