Sendfox Python API Docs | dltHub

Sendfox is an email marketing platform that provides a REST API for managing contacts, lists, and account details. The REST API base URL is https://api.sendfox.com and All requests require a Bearer token passed in the Authorization 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 Sendfox data in under 10 minutes.

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What data can I load from Sendfox?

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

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How do I authenticate with the Sendfox API?

Authentication uses a personal access token. Include the header Authorization: Bearer <TOKEN> with each request.

1. Get your credentials

1. Log in to your Sendfox account.
2. Navigate to Account → Integrations → API (or visit https://sendfox.com/account/oauth).
3. Click "Create personal access token".
4. Give the token a name and set desired scopes.
5. Copy the generated token; store it securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.sendfox_source]
token = "your_api_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 Sendfox 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 sendfox_pipeline.py

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

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Pipeline sendfox_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sendfox_data
The duckdb destination used duckdb:/sendfox.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 sendfox_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 contacts and lists from the Sendfox 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 sendfox_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.sendfox.com",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "data"}},
            {"name": "lists", "endpoint": {"path": "lists", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sendfox_pipeline",
        destination="duckdb",
        dataset_name="sendfox_data",
    )
    load_info = pipeline.run(sendfox_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("sendfox_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sendfox_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sendfox_pipeline").dataset()
data.contacts.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 Sendfox 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 Errors

• 401 Unauthorized – Occurs when the Bearer token is missing, malformed, or expired. Ensure the token is correct and included in the Authorization header.

Validation and Client Errors

• 4xx Codes – Indicate request issues such as missing required parameters or invalid query values. Review the request format and required fields.

Server Errors

• 5xx Codes – Signal problems on Sendfox’s side. Retry after a short delay.

Pagination

• The API uses page, per_page, total, and current_page fields in the response. To retrieve additional pages, increment the page query parameter until current_page equals the last page.

Rate Limits

• The documentation does not specify explicit rate‑limit headers. If you encounter intermittent 429 responses, reduce request frequency.

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