Trello Python API Docs | dltHub

Trello is a collaboration platform for organizing projects into boards, lists, and cards via a REST API. The REST API base URL is https://api.trello.com/1 and All requests require an API key and token (key+token) or OAuth; key and token are passed as query parameters or form fields..

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 Trello data in under 10 minutes.

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What data can I load from Trello?

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

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How do I authenticate with the Trello API?

Trello uses an API key (application key) plus a token for user‑scoped access; include key and token as query params (?key=APIKey&token=APIToken) or in request body. OAuth 1.0 is also supported.

1. Get your credentials

1. Log into Trello. 2) Visit https://trello.com/1/appKey/generate (or the Power‑Ups admin page) to generate your API key. 3) On that page click the Token link to generate an API token (choose scope and duration). 4) Store both the key and token securely for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.trello_source]
api_key = "your_api_key_here"
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 Trello 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 trello_pipeline.py

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

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Pipeline trello_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset trello_data
The duckdb destination used duckdb:/trello.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 trello_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 boards and cards from the Trello 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 trello_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.trello.com/1",
            "auth": {
                "type": "api_key",
                "api_key": api_token,
            },
        },
        "resources": [
            {"name": "boards", "endpoint": {"path": "1/members/me/boards"}},
            {"name": "cards", "endpoint": {"path": "1/boards/{boardId}/cards"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="trello_pipeline",
        destination="duckdb",
        dataset_name="trello_data",
    )
    load_info = pipeline.run(trello_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("trello_pipeline").dataset()
sessions_df = data.boards.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM trello_data.boards LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("trello_pipeline").dataset()
data.boards.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 Trello 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 you supplied both key and token and that the token has not been revoked or expired. Tokens are generated from the appKey page; ensure you used the token for the correct account.

Rate limits (429)

Trello applies rate limits per API key. On exceeding limits the API returns HTTP 429 and an explanatory message. Use webhooks where possible and paginate Actions with before/since parameters to avoid heavy polling.

Pagination and Actions limit

Many list endpoints (notably Actions) are capped at 1000 items per request. Use since/before (ISO 8601 date or resource ID) to page through results. When asking for large historical sets, iterate by passing the last item's ID as before.

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