Cliengo Python API Docs | dltHub

Cliengo is a conversational AI platform that provides chat and lead management via a REST API. The REST API base URL is https://api.cliengo.com/1.0 and All requests require an apiKey (or a JWT derived from the apiKey) for authentication..

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 Cliengo data in under 10 minutes.

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What data can I load from Cliengo?

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

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How do I authenticate with the Cliengo API?

Authentication is performed by including the apiKey as a query parameter (api_key=YOUR_KEY) or by exchanging the apiKey for a JWT and sending it in the Authorization header as a Bearer token.

1. Get your credentials

1. Log in to your Cliengo account.
2. Navigate to Account → Integrations → API.
3. Copy the displayed apiKey.
4. (Optional) Exchange the apiKey for a JWT by calling https://api.cliengo.com/1.0/jwt?api_key=YOUR_KEY.

2. Add them to .dlt/secrets.toml

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[sources.cliengo_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 Cliengo 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 cliengo_pipeline.py

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

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Pipeline cliengo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cliengo_data
The duckdb destination used duckdb:/cliengo.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 cliengo_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 conversations from the Cliengo 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 cliengo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cliengo.com/1.0",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts"}},
            {"name": "conversations", "endpoint": {"path": "conversations"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM cliengo_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cliengo_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 Cliengo 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 – Occurs when the api_key query parameter is missing, malformed, or invalid. Ensure you have copied the correct API key from Account → Integrations → API and include it as api_key=YOUR_KEY or exchange it for a JWT and send it as Authorization: Bearer <token>.

Pagination quirks

• The API uses limit and offset query parameters for pagination. limit defines the maximum number of records per page, while offset is zero‑based. To fetch the next page, increase offset by the previous limit value. If the total count from the prior response is less than the new offset, there are no more pages.
• Example: GET /contacts?limit=100&offset=200 retrieves records 201‑300.
• Missing or out‑of‑range offset can result in an empty array without an error.

General HTTP errors

• 400 Bad Request – Invalid query parameters or malformed URLs.
• 429 Too Many Requests – The API may enforce rate limits; back off and retry after a short delay.
• 500/502/503 – Server‑side issues; retry with exponential backoff.

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