7todos Python API Docs | dltHub

GitLab Todos API is a REST interface that lets users list and manage their to‑do items such as merge requests, issue assignments, and other notifications. The REST API base URL is https://gitlab.com/api/v4 and All requests require a Personal Access Token sent in the PRIVATE‑TOKEN header (or an OAuth2 Bearer token)..

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 add "dlt[hub]" and start loading 7todos data in under 10 minutes.

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What data can I load from 7todos?

Here are some of the endpoints you can load from 7todos:

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How do I authenticate with the 7todos API?

Include the header "PRIVATE‑TOKEN: <your_access_token>" with every request (or use an OAuth2 Bearer token in the Authorization header).

1. Get your credentials

1. Log into your GitLab instance.
2. Click your avatar → "Preferences" (or "Settings").
3. Choose "Access Tokens" from the left‑hand menu.
4. Provide a name, expiration date, and select the "api" scope.
5. Click "Create personal access token" and copy the generated token; store it securely for use in the PRIVATE‑TOKEN header.

2. Add them to .dlt/secrets.toml

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[sources._7todos_source]
private_token = "your_private_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 init
uv add "dlt[hub]"

1. Install the dlt AI Workbench:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 7todos 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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uv run python _7todos_pipeline.py

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

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Pipeline _7todos_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset _7todos_data
The duckdb destination used duckdb:/_7todos.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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uv run dlthub 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 todos and todo_mark_done from the 7todos 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 _7todos_source(private_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://gitlab.com/api/v4",
            "auth": {
                "type": "api_key",
                "api_key": private_token,
            },
        },
        "resources": [
            {"name": "todos", "endpoint": {"path": "todos"}},
            {"name": "todo_mark_done", "endpoint": {"path": "todos/:id/mark_as_done"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="_7todos_pipeline",
        destination="duckdb",
        dataset_name="_7todos_data",
    )
    load_info = pipeline.run(_7todos_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("_7todos_pipeline").dataset()
sessions_df = data.todos.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM _7todos_data.todos LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("_7todos_pipeline").dataset()
data.todos.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 7todos 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 PRIVATE‑TOKEN header is missing or the token is invalid. Verify that the token has the api scope and is sent exactly as shown in the docs.

Rate Limiting

• 429 Too Many Requests – GitLab enforces rate limits per user. The response includes RateLimit-Limit, RateLimit-Remaining, and RateLimit-Reset headers. Back‑off and retry after the reset time.

Pagination

• GitLab paginates list endpoints using X-Total, X-Total-Pages, X-Page, X-Per-Page, and Link headers. Use the page query parameter to iterate through pages until X-Page equals X-Total-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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• 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