Smartsheet Python API Docs | dltHub

Smartsheet is a cloud‑based platform for work management that provides a REST API to programmatically access and manage sheets, rows, columns, users, workspaces, folders, reports, webhooks and other Smartsheet resources. The REST API base URL is https://api.smartsheet.com/2.0 and All requests require a Bearer access 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 Smartsheet data in under 10 minutes.

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What data can I load from Smartsheet?

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

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How do I authenticate with the Smartsheet API?

Smartsheet uses bearer access tokens: include Authorization: Bearer <ACCESS_TOKEN> on every request. For POST/PUT also set Content-Type: application/json. SDKs will read SMARTSHEET_ACCESS_TOKEN from the environment.

1. Get your credentials

1. Sign into the Smartsheet web UI. 2) Navigate to Account → Apps & Integrations (or Help → Personal Settings → Apps & Integrations). 3) Click Generate new access token (or Generate API access token). 4) Provide a name, create the token, copy it once, and store it securely. 5) Use the token as the Bearer token in API calls. Revoke or regenerate tokens from the same page when needed.

2. Add them to .dlt/secrets.toml

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[sources.smartsheet_source]
api_token = "your_smartsheet_access_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 Smartsheet 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 smartsheet_pipeline.py

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

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Pipeline smartsheet_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset smartsheet_data
The duckdb destination used duckdb:/smartsheet.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 smartsheet_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 sheets and rows from the Smartsheet 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 smartsheet_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.smartsheet.com/2.0",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "sheets", "endpoint": {"path": "sheets", "data_selector": "data"}},
            {"name": "rows", "endpoint": {"path": "sheets/{sheetId}/rows", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="smartsheet_pipeline",
        destination="duckdb",
        dataset_name="smartsheet_data",
    )
    load_info = pipeline.run(smartsheet_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("smartsheet_pipeline").dataset()
sessions_df = data.rows.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM smartsheet_data.rows LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("smartsheet_pipeline").dataset()
data.rows.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 Smartsheet 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 that the Authorization header is exactly Bearer <token> and that the token matches the regional host you are calling (e.g., .com, .eu, .gov, .au). Ensure the environment variable SMARTSHEET_ACCESS_TOKEN or the token passed to the client is set correctly.

Rate limits and retrying

Smartsheet enforces rate and concurrency limits. On 429 Too Many Requests or 5xx server errors, implement exponential backoff and retry. Check response headers for rate‑limit information; do not retry 4xx errors that indicate a client‑side problem.

Pagination

List endpoints return a JSON object with a data array and a lastKey token for pagination. Use the lastKey value as the pageToken query parameter to retrieve the next page. Older page‑based fields like pageNumber are deprecated.

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