SmartThings Python API Docs | dltHub

SmartThings is a cloud platform and REST API for controlling and integrating smart home devices, locations, rooms, scenes and automations. The REST API base URL is https://api.smartthings.com and All requests require a Bearer token (Personal Access Token) sent 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 SmartThings data in under 10 minutes.

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What data can I load from SmartThings?

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

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How do I authenticate with the SmartThings API?

Use Authorization: Bearer <PERSONAL_ACCESS_TOKEN> on every request. PATs can be created and used directly or supplied to the SmartThings CLI as a token.

1. Get your credentials

1. Sign in to the SmartThings Developer Workspace (developer.smartthings.com).
2. Navigate to your account settings and open the Personal Access Tokens (PAT) section.
3. Click “Create New Token”, select the required scopes, and generate the token.
4. Copy the token value (shown only once) and store it securely.
5. Use the token as a Bearer token in API requests or set it in the CLI configuration under the token key.

2. Add them to .dlt/secrets.toml

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[sources.smartthings_home_api_source]
token = "your_personal_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 SmartThings 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 smartthings_home_api_pipeline.py

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

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Pipeline smartthings_home_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset smartthings_home_api_data
The duckdb destination used duckdb:/smartthings_home_api.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 smartthings_home_api_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 devices and locations from the SmartThings 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 smartthings_home_api_source(personal_access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.smartthings.com",
            "auth": {
                "type": "bearer",
                "token": personal_access_token,
            },
        },
        "resources": [
            {"name": "devices", "endpoint": {"path": "devices", "data_selector": "items"}},
            {"name": "locations", "endpoint": {"path": "locations", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="smartthings_home_api_pipeline",
        destination="duckdb",
        dataset_name="smartthings_home_api_data",
    )
    load_info = pipeline.run(smartthings_home_api_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("smartthings_home_api_pipeline").dataset()
sessions_df = data.devices.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM smartthings_home_api_data.devices LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("smartthings_home_api_pipeline").dataset()
data.devices.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 SmartThings 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 or 403 Forbidden: ensure your Authorization header is exactly: Authorization: Bearer <PAT>. Verify the PAT has not expired and has required scopes. PATs created via the CLI or Console may have limited lifetimes.

Rate limits

The API may return 429 Too Many Requests when throttled. Implement exponential backoff and respect the Retry-After header if present.

Pagination and list responses

List endpoints return paginated responses with an items array containing records; follow the provided pagination links (or next page tokens) in the response to iterate through results.

Device/Resource not found

404 Not Found indicates the referenced device/location/scene id is invalid or not accessible to the token's account/organization. Confirm IDs and token scope.

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