Engagebay Python API Docs | dltHub

EngageBay is an all-in-one marketing, sales and support CRM platform exposing CRM objects via a REST API. The REST API base URL is https://app.engagebay.com/dev/api/ and All requests require the EngageBay REST API key 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 Engagebay data in under 10 minutes.

---

What data can I load from Engagebay?

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

---

How do I authenticate with the Engagebay API?

Requests are authenticated by including your REST API Key in the Authorization HTTP header (no Bearer prefix) and setting Accept: application/json.

1. Get your credentials

1. Log in to EngageBay. 2) Navigate to Account Settings (Admin) → API & Tracking Code (or API). 3) Locate the REST API Key displayed on the page. 4) Copy the key; it will be used as the value for the Authorization header in API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.engagebay_source]
api_key = "your_rest_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Engagebay 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:

Copy
python engagebay_pipeline.py

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

Copy
Pipeline engagebay_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset engagebay_data
The duckdb destination used duckdb:/engagebay.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline engagebay_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.

---

Python pipeline example

This example loads contacts and companies from the Engagebay API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def engagebay_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.engagebay.com/dev/api/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "dev/api/panel/subscribers"}},
            {"name": "companies", "endpoint": {"path": "dev/api/panel/companies"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="engagebay_pipeline",
        destination="duckdb",
        dataset_name="engagebay_data",
    )
    load_info = pipeline.run(engagebay_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.

---

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):

Copy
import dlt

data = dlt.pipeline("engagebay_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM engagebay_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("engagebay_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.

---

What destinations can I load Engagebay 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.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify that the Authorization header contains the exact REST API Key without a Bearer prefix and that the Accept: application/json header is present. Ensure the key has not been regenerated or revoked.

Rate limits (429)

The API may return 429 Too Many Requests. Reduce request frequency, honour the page_size limit (max 100), and implement exponential backoff. Contact EngageBay support to raise limits if needed.

Pagination and cursors

List endpoints use cursor‑based pagination: include page_size (max 100) and cursor query parameters. The response includes a cursor field in the last record; if absent, the list is complete. Some endpoints return a top‑level JSON array, so no data selector is required.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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