monday.com Python API Docs | dltHub

monday.com is a work OS platform exposing a GraphQL API to read and modify boards, items, users, updates and related workspace data. The REST API base URL is https://api.monday.com/v2 and All requests require an API token supplied 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?

monday.com uses personal or app API tokens. Include your token in the HTTP Authorization header for every request. Requests should be POST with a JSON body containing a 'query' (and optional 'variables'). Use Content-Type: application/json and optionally API-Version header to pin API version.

1. Get your credentials

1. Sign into your monday.com account and open the Developers section (Profile → Developers) or the Admin/Developers area. 2) Create an app (if you need an app token) or generate a personal API v2 token from the API or Developers settings. 3) Copy the token; treat it as a secret and use it in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.monday_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 monday_pipeline.py

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

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Pipeline monday_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset monday_data
The duckdb destination used duckdb:/monday.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 monday_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 monday_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.monday.com/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "boards", "endpoint": {"path": "v2 (GraphQL queries for boards)", "data_selector": "data.boards"}},
            {"name": "items", "endpoint": {"path": "v2 (GraphQL queries for items)", "data_selector": "data.items"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM monday_data.boards LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("monday_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 failures

If you get 401 or 403 responses, verify your API token and ensure it is sent in the Authorization header. Tokens must be copied exactly and not exposed.

Rate limits

The platform enforces rate limits; excessive requests may return 429. Honor Retry-After header and back off. Use batched GraphQL queries when possible.

Pagination

Many list fields use cursor-based pagination; include 'page' or 'limit'/'cursor' parameters according to the queried field and follow the pageInfo/nextCursor pattern returned inside the data for that field.

GraphQL specifics

All operations are POST to a single endpoint; the JSON response wraps results under 'data'. Query exactly the fields you need; nested queries return nested JSON.

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