Todoist Python API Docs | dltHub

Todoist is a task management platform that provides REST and Sync APIs for creating, reading, updating, and deleting tasks, projects, labels, sections, comments, reminders and related resources. The REST API base URL is https://api.todoist.com/rest/v2 and all requests require 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 Todoist data in under 10 minutes.

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What data can I load from Todoist?

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

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How do I authenticate with the Todoist API?

Use HTTP header Authorization: Bearer <personal_or_oauth_token> for all REST and Sync requests. Content-Type for POST/PUT as required (application/json for REST; application/x-www-form-urlencoded for Sync).

1. Get your credentials

1. Sign in to Todoist web app. 2) Go to Settings -> Integrations (or Preferences -> Integrations). 3) Locate the API token (Personal API token) and copy it. For OAuth apps, register an app at developer.todoist.com to obtain client_id and client_secret and perform the OAuth flow to obtain access tokens.

2. Add them to .dlt/secrets.toml

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[sources.todoist_source]
api_token = "your_todoist_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 Todoist 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 todoist_pipeline.py

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

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Pipeline todoist_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset todoist_data
The duckdb destination used duckdb:/todoist.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 todoist_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 tasks and projects from the Todoist 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 todoist_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.todoist.com/rest/v2",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "tasks"}},
            {"name": "projects", "endpoint": {"path": "projects"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM todoist_data.tasks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("todoist_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 Todoist 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 you receive 401 Unauthorized, verify the Authorization header is exactly: Authorization: Bearer <token> and that the token is valid (personal token or OAuth access token). For OAuth, ensure token scope includes required read permissions.

Rate limits

Todoist enforces request limits; if you receive 429 Too Many Requests, back off and retry after the time indicated by Retry-After header. Use bulk/sync where possible to reduce request volume.

Pagination and large data retrieval

REST v2 endpoints typically return top-level arrays for resources. For full-account sync or batching, use the Sync API (/sync/v9/sync) which returns multiple resource arrays (e.g., "projects", "items", "notes") and supports incremental sync via sync_token. For legacy activity pagination the API returns results + next_cursor fields.

Common API errors: 401 Unauthorized, 403 Forbidden (insufficient permissions), 404 Not Found (invalid id), 429 Too Many Requests (rate limit), 400 Bad Request (invalid parameters), 500+ server errors.

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