Cleverreach Python API Docs | dltHub

CleverReach is an email marketing platform that provides a REST API to manage groups, receivers, mailings, reports, attributes and webhooks. The REST API base URL is https://rest.cleverreach.com and All requests require an OAuth2 access_token (Bearer) — tokens obtained via OAuth2 Authorization Code or Client Credentials flows..

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 Cleverreach data in under 10 minutes.

What data can I load from Cleverreach?

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

How do I authenticate with the Cleverreach API?

CleverReach uses OAuth2. Include the access token either as ?access_token=... query parameter or as Authorization: Bearer <access_token> header. v3 requires a v3-scoped token.

1. Get your credentials

1. Log in to CleverReach account.
2. Go to Account > Interfaces > REST API.
3. Create a new OAuth app; record the Client ID and Client Secret.
4. For Authorization Code flow: set redirect_uri, direct users to authorize, receive code, exchange code at https://rest.cleverreach.com/oauth/token.php to get access_token and refresh_token.
5. For Client Credentials flow: POST to https://rest.cleverreach.com/oauth/token.php using client_id:client_secret and grant_type=client_credentials to receive an access_token (scoped to the creating account).

2. Add them to .dlt/secrets.toml

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[sources.cleverreach_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
access_token = "your_oauth_access_token"

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:

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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 Cleverreach 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 cleverreach_pipeline.py

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

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Pipeline cleverreach_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cleverreach_data
The duckdb destination used duckdb:/cleverreach.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 cleverreach_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 groups and receivers from the Cleverreach 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 cleverreach_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://rest.cleverreach.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "groups", "endpoint": {"path": "v2/groups"}},
            {"name": "receivers", "endpoint": {"path": "v2/receivers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cleverreach_pipeline",
        destination="duckdb",
        dataset_name="cleverreach_data",
    )
    load_info = pipeline.run(cleverreach_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):

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import dlt

data = dlt.pipeline("cleverreach_pipeline").dataset()
sessions_df = data.groups.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cleverreach_data.groups LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cleverreach_pipeline").dataset()
data.groups.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 Cleverreach 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 requests return 401/403, verify you supplied a valid OAuth2 access_token. Use Authorization: Bearer or ?access_token=. Ensure token scope matches the API version (v3 endpoints require v3 tokens) and refresh the token using the refresh_token when expired via POST https://rest.cleverreach.com/oauth/token.php.

Rate limits and errors

API returns standard HTTP error codes with JSON error body: {"error": {"code": , "message": ""}}. Handle 4xx/5xx responses by inspecting the error object. 404 returns the error object shown in docs.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install