Wyze Python API Docs | dltHub

Wyze is a smart home platform that provides REST API endpoints for accessing cameras, events, and user data. The REST API base URL is https://api.wyzecam.com and All requests require an access token obtained via the login endpoint; the token is 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 Wyze data in under 10 minutes.

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What data can I load from Wyze?

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

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How do I authenticate with the Wyze API?

Authentication is performed via a POST to the login endpoint with email, MD5‑hashed password, key_id and api_key. The response provides an access_token (and refresh_token) which must be included in the Authorization header for subsequent requests.

1. Get your credentials

1. Visit https://developer-api-console.wyze.com/ and sign in with your Wyze account.
2. Navigate to the "API Keys" section.
3. Click "Create API Key" and optionally name it.
4. Copy the generated key_id and api_key values.
5. Store these values securely; they will be used in the login request to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.wyze_api_source]
api_key = "your_api_key_here"
key_id = "your_key_id_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 Wyze 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 wyze_api_pipeline.py

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

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Pipeline wyze_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wyze_api_data
The duckdb destination used duckdb:/wyze_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 wyze_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 user_info and event_list from the Wyze 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 wyze_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.wyzecam.com",
            "auth": {
                "type": "api_key",
                "access_token": api_key,
            },
        },
        "resources": [
            {"name": "user_info", "endpoint": {"path": "app/user/get_user_info"}},
            {"name": "event_list", "endpoint": {"path": "app/v2/device/get_event_list", "data_selector": "events"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wyze_api_pipeline",
        destination="duckdb",
        dataset_name="wyze_api_data",
    )
    load_info = pipeline.run(wyze_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("wyze_api_pipeline").dataset()
sessions_df = data.event_list.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wyze_api_data.event_list LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wyze_api_pipeline").dataset()
data.event_list.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 Wyze 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

• Invalid token: Returns HTTP 401. Ensure the access_token is up‑to‑date; refresh it via the refresh_token endpoint.
• Expired token: Use the refresh_token call to obtain a new token.

Rate Limiting

• HTTP 429 Too Many Requests: The API applies per‑minute limits. Back‑off for 30‑60 seconds before retrying.

Pagination

• Some list endpoints paginate using page and page_size query parameters. Check the response for a next_page_token or similar field and include it in subsequent requests to retrieve all records.

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