Encharge Python API Docs | dltHub

Encharge is a marketing automation platform that offers a REST API for ingesting contacts, managing workflows, and sending transactional emails. The REST API base URL is https://api.encharge.io/v1 and All requests require an Encharge token supplied via the X‑Encharge‑Token header or as a token query parameter..

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 Encharge data in under 10 minutes.

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What data can I load from Encharge?

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

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How do I authenticate with the Encharge API?

Encharge authenticates calls with a write token. For most endpoints the token is provided in the X‑Encharge‑Token header; for the transactional email endpoint the token can be supplied as a "token" query parameter.

1. Get your credentials

1. Log into your Encharge account.
2. Click your profile avatar and select Settings.
3. In the left menu choose API Tokens (or Integrations > API).
4. Click Generate New Token and give it a descriptive name.
5. Copy the generated token – this is the write key used as X‑Encharge‑Token.
6. Store the token securely; it will not be shown again.

2. Add them to .dlt/secrets.toml

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[sources.encharge_data_source]
api_key = "your_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 Encharge 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 encharge_data_pipeline.py

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

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Pipeline encharge_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset encharge_data_data
The duckdb destination used duckdb:/encharge_data.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 encharge_data_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 events from the Encharge 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 encharge_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.encharge.io/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "contacts"}},
            {"name": "events", "endpoint": {"path": "events", "data_selector": "events"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM encharge_data_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("encharge_data_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 Encharge 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 – The X‑Encharge‑Token header is missing, malformed, or the token is invalid. Verify that the correct write key is configured in secrets.toml and that it is being sent as a header.

Rate Limiting

• 429 Too Many Requests – Encharge enforces request limits per minute. Back‑off for a few seconds and retry, or implement exponential back‑off logic.

Pagination

• Encharge uses cursor‑based pagination. The response includes a next_cursor field; include it as ?cursor=... in the next request to retrieve subsequent pages.

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