Drip Python API Docs | dltHub

Drip is an email marketing automation platform providing a REST API to manage accounts, subscribers, campaigns, tags, events and related resources. The REST API base URL is https://api.getdrip.com/v2 and Private integrations use HTTP Basic auth with your API token as the username and an empty password; public integrations use OAuth2/Bearer tokens..

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 Drip data in under 10 minutes.

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What data can I load from Drip?

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

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How do I authenticate with the Drip API?

For private integrations include Authorization via HTTP Basic with your API token as the username and an empty password (curl: -u 'YOUR_API_TOKEN:' or Authorization: Basic base64(YOUR_API_TOKEN:)). For public OAuth use Authorization: Bearer <access_token>. Include User-Agent and Content-Type: application/json headers.

1. Get your credentials

1. Log in to your Drip account. 2) Go to Account Settings / API or User > API tokens (often at https://www.getdrip.com/user/edit or API settings). 3) Create or copy your personal API Token. 4) For private use, use that token as the HTTP Basic username (leave password blank). For public integrations, register an application (Applications), obtain client id/secret and perform OAuth flow to get access tokens.

2. Add them to .dlt/secrets.toml

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[sources.drip_source]
api_key = "your_drip_api_token_here"
account_id = "your_drip_account_id_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 Drip 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 drip_pipeline.py

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

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Pipeline drip_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset drip_data
The duckdb destination used duckdb:/drip.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 drip_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 subscribers and campaigns from the Drip 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 drip_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getdrip.com/v2",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "subscribers", "endpoint": {"path": "<account_id>/subscribers", "data_selector": "subscribers"}},
            {"name": "campaigns", "endpoint": {"path": "<account_id>/campaigns", "data_selector": "campaigns"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="drip_pipeline",
        destination="duckdb",
        dataset_name="drip_data",
    )
    load_info = pipeline.run(drip_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("drip_pipeline").dataset()
sessions_df = data.subscribers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM drip_data.subscribers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("drip_pipeline").dataset()
data.subscribers.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 Drip 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 get 401 Unauthorized: verify you are using your API token correctly. For the classic Drip REST API use HTTP Basic with the token as the username and an empty password (curl -u 'API_TOKEN:'). Ensure you include a User-Agent header. For OAuth flows verify client id/secret and that the access token is supplied as 'Authorization: Bearer '.

Rate limiting

Drip returns X-RateLimit headers on successful responses. Exceeding limits returns HTTP 429 with body e.g. {"message": "API rate limit exceeded. Please try again in an hour.", "documentation": "https://www.getdrip.com/docs/rest-api"}. Respect X-RateLimit-Remaining and back off; batch endpoints have stricter/hourly limits.

Pagination

Many list endpoints are paginated. Check response for pagination links/headers and use query parameters (e.g. page, per_page or since/offset depending on endpoint). If the response returns an array inside a plural key (e.g. "subscribers"), iterate pages until an empty array or no next link.

Common error formats

404 Not Found: {"errors": [{"code": "not_found_error", "message": "The resource you requested was not found"}]} 403 Forbidden: {"errors": [{"code": "authorization_error", "message": "You are not authorized to access this resource"}]} 429 Too Many Requests: {"message": "API rate limit exceeded. Please try again in an hour."}

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