Monday.com Python API Docs | dltHub

Monday.com is a work operating system that provides a GraphQL API for managing boards, items, columns, users and other platform resources. The REST API base URL is https://api.monday.com/v2 and All requests require a Bearer token passed 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 Monday.com data in under 10 minutes.

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What data can I load from Monday.com?

Here are some of the endpoints you can load from Monday.com:

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How do I authenticate with the Monday.com API?

Authentication uses a personal API token (Bearer). Include the header Authorization: <your_token> and Content-Type: application/json on every POST request to the endpoint.

1. Get your credentials

1. Log in to your Monday.com account.
2. Click your avatar in the top right corner and select Admin (or My Profile → API).
3. In the API tab, click Generate new token.
4. Copy the generated token and keep it secure; you will use it as the Bearer token for API calls.

2. Add them to .dlt/secrets.toml

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[sources.mondaycom_source]
api_key = "your_monday_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 Monday.com 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 mondaycom_pipeline.py

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

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Pipeline mondaycom_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mondaycom_data
The duckdb destination used duckdb:/mondaycom.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 mondaycom_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 items from the Monday.com 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 mondaycom_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.monday.com/v2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "boards", "endpoint": {"path": "", "data_selector": "boards"}},
            {"name": "items", "endpoint": {"path": "", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM mondaycom_data.boards LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mondaycom_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 Monday.com 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 token is missing, malformed, or revoked. Ensure the Authorization: <token> header is correct and the token is still active.

Rate Limiting

• 429 Too Many Requests – Monday.com enforces a limit of 60 requests per minute per token. Implement exponential back‑off and respect the Retry-After header if present.

Pagination

• The GraphQL API uses limit and page arguments in queries to paginate results. If omitted, only a default number of items is returned. Example: { boards(limit: 25, page: 2) { id name } }.

Common Payload Errors

• 400 Bad Request – Typically caused by malformed GraphQL queries or missing required fields. Validate the query syntax and required variables before sending.

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