Load Life360 data to DuckDB

Life360 provides an internal, undocumented REST API used by its web and mobile clients for managing location-based circles and user data. Everything needed to build a working Life360 → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Life360 to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Life360 to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Life360 API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Life360 API at a glance

These values come from the Life360 API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Life360 API?

The API requires a two-step authentication process: first, exchange user credentials for an access token via a Basic Auth header containing a client token; second, use the returned OAuth2 access token as a Bearer token in subsequent requests. Requests generally require the header 'Accept: application/json' and a custom 'User-Agent' string.

1. Get your credentials

Life360 does not provide a public developer dashboard for generating API keys. To obtain the necessary credentials (access token) for API access, follow these steps: 1) Open your web browser and navigate to https://life360.com/login. 2) Open the browser's Developer Tools (usually F12), go to the Network tab, and ensure it is recording. 3) Log in to your Life360 account as usual using your email and the one-time code sent to you. 4) In the Network tab, locate the POST request named 'token'. 5) In the response body of that request, copy the value of 'access_token'. Use this token as a Bearer token for your API requests. Note: This token is long-lived but may need to be refreshed periodically using these same steps if it expires.

2. Add them to .dlt/secrets.toml

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[sources.life360_source]
access_token = "your_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Life360 data can I load into DuckDB?

These are the Life360 endpoints dlt can load into DuckDB:

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How do I load only new Life360 records?

The Life360 API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "circles", "endpoint": {
    "path": "circles",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Life360 pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v3/circles and /v3/circles/{circle}/members from the Life360 API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def life360_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-cloudfront.life360.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "circles", "endpoint": {"path": "circles", "data_selector": "circles"}},
            {"name": "circle_members", "endpoint": {"path": "circles/{circle}/members"}}
        ],
    }
    yield from rest_api_resources(config)


def load_life360_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="life360_pipeline",
        destination="duckdb",
        dataset_name="life360_data",
    )
    load_info = pipeline.run(life360_source())
    print(load_info)


if __name__ == "__main__":
    load_life360_to_duckdb()

Run it with python life360_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Life360 data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("life360_pipeline").dataset()
df = data.circles.df()
print(df.head())

SQL:

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SELECT * FROM life360_data.circles LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Life360 to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Life360 loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Life360 data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack