Checkvist Python API Docs | dltHub

Checkvist is an online, outline-oriented task and checklist service that exposes a REST Open API. The REST API base URL is https://checkvist.com and Token‑based or HTTP Basic authentication is supported..

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 Checkvist data in under 10 minutes.

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What data can I load from Checkvist?

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

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How do I authenticate with the Checkvist API?

Use HTTP Basic with your email and remote API key, or obtain a short‑lived token via /auth/login.json?version=2 and include it as the token query parameter or X-Client-Token header.

1. Get your credentials

1. Log in to the Checkvist web UI. 2) Open your profile and navigate to the Remote API Key section; copy the key. 3) To obtain a token, send a GET or POST request to /auth/login.json?version=2 with your email and the remote API key (or password). 4) The response contains a JSON object with a token field. 5) Use that token in subsequent API calls as the token query parameter or X-Client-Token header. 6) When needed, refresh the token via /auth/refresh_token.json?version=2 providing the old token.

2. Add them to .dlt/secrets.toml

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[sources.checkvist_source]
token = "your_short_lived_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 Checkvist 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 checkvist_pipeline.py

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

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Pipeline checkvist_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset checkvist_data
The duckdb destination used duckdb:/checkvist.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 checkvist_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 checklists and tasks from the Checkvist 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 checkvist_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://checkvist.com",
            "auth": {
                "type": "api_key",
                "token": token,
            },
        },
        "resources": [
            {"name": "checklists", "endpoint": {"path": "checklists.json"}},
            {"name": "tasks", "endpoint": {"path": "checklists/{checklist_id}/tasks.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="checkvist_pipeline",
        destination="duckdb",
        dataset_name="checkvist_data",
    )
    load_info = pipeline.run(checkvist_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("checkvist_pipeline").dataset()
sessions_df = data.tasks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM checkvist_data.tasks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("checkvist_pipeline").dataset()
data.tasks.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 Checkvist 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 failures

If credentials are invalid the API returns HTTP 403 Forbidden. Ensure you use either HTTP Basic (username + remote API key or password) or obtain a token via /auth/login.json?version=2 and pass it as the token query parameter or X-Client-Token header. When 2‑step verification is enabled, use the Remote API key and, if required, include the token2fa parameter.

Token expiration and refresh

Tokens are valid for 1 day. Refresh them with /auth/refresh_token.json?version=2 providing the old token. If the refresh call fails (403), re‑authenticate using the login endpoint.

Rate limits and method emulation

The docs do not specify strict rate limits; implement exponential backoff on HTTP 429 or 5xx responses. For APIs that cannot send PUT/DELETE, the service supports POST with an _method parameter to emulate other HTTP verbs.

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