Clustdoc Python API Docs | dltHub

Clustdoc is an API that provides powerful REST APIs for building custom integrations and Clustdoc-based applications. The REST API base URL is https://app.clustdoc.com/api and All requests require an API token for authentication, which can be passed as a query string or a Bearer token 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 Clustdoc data in under 10 minutes.

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What data can I load from Clustdoc?

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

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How do I authenticate with the Clustdoc API?

Authentication is done by providing an API token. This token can be passed either as an api_token query string parameter or as a Bearer token in the Authorization header of the request.

1. Get your credentials

To obtain API credentials, go to your Clustdoc team settings page in either the Sandbox or production environment. On the configuration page, click on the 'developer' menu on the left, then click on the 'API' menu. You can then enter a token name and select the abilities you want to assign to your token. Finally, click on the 'validate' button to get your API token.

2. Add them to .dlt/secrets.toml

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[sources.clustdoc_source]
api_token = "your_api_token_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 Clustdoc 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 clustdoc_pipeline.py

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

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Pipeline clustdoc_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset clustdoc_data
The duckdb destination used duckdb:/clustdoc.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 clustdoc_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 templates and dossiers from the Clustdoc 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 clustdoc_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.clustdoc.com/api",
            "auth": {
                "type": "bearer",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "templates", "endpoint": {"path": "templates", "data_selector": "data"}},
            {"name": "dossiers", "endpoint": {"path": "dossiers", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="clustdoc_pipeline",
        destination="duckdb",
        dataset_name="clustdoc_data",
    )
    load_info = pipeline.run(clustdoc_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("clustdoc_pipeline").dataset()
sessions_df = data.templates.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM clustdoc_data.templates LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("clustdoc_pipeline").dataset()
data.templates.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 Clustdoc 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

Rate Limit Exceeded

If you exceed the rate limit, you will receive a 429 HTTP status code. The rate limit is 100 requests per minute per token and is subject to change.

Error Responses

Error responses will return a non-200 status code and a JSON error message and error code.

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