Whatconverts Python API Docs | dltHub

WhatConverts is a call- and lead-tracking platform exposing account, profile, lead, recording and related data via a REST API. The REST API base URL is https://app.whatconverts.com/api/v1/ and All requests require HTTP Basic authentication using an API token and secret..

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 Whatconverts data in under 10 minutes.

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What data can I load from Whatconverts?

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

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How do I authenticate with the Whatconverts API?

The API uses HTTP Basic auth: supply your API token as the username and API secret as the password (e.g. curl -u token:secret https://app.whatconverts.com/api/v1/leads).

1. Get your credentials

1. Log in to WhatConverts and navigate to the desired account (required for agency plans).
2. Select a profile (or the account‑level Integrations → API Keys for agency/master keys).
3. From the Tracking dropdown open Integrations → API Keys (or Help Center → Generate a Profile API Key).
4. Click Generate API Key / Add API Key; the page will display the token and secret. Save both; token is used as HTTP Basic username and secret as password.

2. Add them to .dlt/secrets.toml

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[sources.whatconverts_source]
token = "your_api_token_here"
secret = "your_api_secret_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 Whatconverts 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 whatconverts_pipeline.py

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

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Pipeline whatconverts_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset whatconverts_data
The duckdb destination used duckdb:/whatconverts.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 whatconverts_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 leads and accounts from the Whatconverts 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 whatconverts_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.whatconverts.com/api/v1/",
            "auth": {
                "type": "http_basic",
                "token": token,
            },
        },
        "resources": [
            {"name": "leads", "endpoint": {"path": "leads", "data_selector": "leads"}},
            {"name": "accounts", "endpoint": {"path": "accounts", "data_selector": "accounts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="whatconverts_pipeline",
        destination="duckdb",
        dataset_name="whatconverts_data",
    )
    load_info = pipeline.run(whatconverts_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("whatconverts_pipeline").dataset()
sessions_df = data.leads.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM whatconverts_data.leads LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("whatconverts_pipeline").dataset()
data.leads.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 Whatconverts 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 Unauthorized, verify you are using HTTP Basic auth (token as username and secret as password). Ensure you use the correct Profile vs Agency/Master key for the endpoint you are calling (account‑level endpoints require an Agency/Master key).

Rate limits and quotas

Profile keys: up to 1,000 requests/day. Master/Agency keys: up to 10,000 requests/day. The API also permits 1 request/ms and up to 20 concurrent requests by default; contact WhatConverts to request higher limits.

Pagination and large pages

List endpoints are paginated. Use leads_per_page (default 25, max 2500) and page_number to iterate. Responses include page_number, total_pages and total_leads (or total_accounts). For large datasets set leads_per_page to the maximum and paginate until page_number == total_pages.

Recording download quirks

Recordings endpoint (/recording) returns binary MP3 data; do not attempt to parse as JSON — use streaming/binary download.

Common API errors

• 400 Bad Request: invalid parameters (date format, invalid IDs)
• 401 Unauthorized: invalid token/secret or insufficient key scope
• 403 Forbidden: trying to access account‑level resources with a profile key
• 404 Not Found: resource id does not exist
• 429 Too Many Requests: excessive concurrent or total request rate (observe per‑key quotas)

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