Ifttt Python API Docs | dltHub

IFTTT is a platform that connects services and devices to automate tasks by exposing a Connect API and a Service API for building and running Applets. The REST API base URL is https://connect.ifttt.com and service-authenticated requests use an IFTTT-Service-Key header; unauthenticated requests can read public info.

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 Ifttt data in under 10 minutes.

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What data can I load from Ifttt?

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

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How do I authenticate with the Ifttt API?

The Connect API supports unauthenticated requests for public data and service-authenticated requests by adding the header IFTTT-Service-Key: <service_key> to requests. Some server-to-server calls (and calls acting on behalf of a user) require the service key and optionally a user_id parameter.

1. Get your credentials

1. Sign into the IFTTT Platform (https://ifttt.com/mkt). 2) Open your service in the Platform dashboard. 3) Go to the API / Details (API) tab (sometimes labeled "API" or "Details"). 4) Copy the Service Key labeled "Service Key" (use this as IFTTT-Service-Key). 5) Store it in secrets.toml as ifttt_service_key = "...".

2. Add them to .dlt/secrets.toml

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[sources.ifttt_source]
ifttt_service_key = "your_service_key_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 Ifttt 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 ifttt_pipeline.py

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

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Pipeline ifttt_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ifttt_data
The duckdb destination used duckdb:/ifttt.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 ifttt_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 me and connections from the Ifttt 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 ifttt_source(service_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://connect.ifttt.com",
            "auth": {
                "type": "api_key",
                "ifttt_service_key": service_key,
            },
        },
        "resources": [
            {"name": "me", "endpoint": {"path": "v2/me"}},
            {"name": "connections", "endpoint": {"path": "v2/connections/{connection_id}", "data_selector": "services"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ifttt_pipeline",
        destination="duckdb",
        dataset_name="ifttt_data",
    )
    load_info = pipeline.run(ifttt_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("ifttt_pipeline").dataset()
sessions_df = data.me.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ifttt_data.me LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ifttt_pipeline").dataset()
data.me.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 Ifttt 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 requests return 401, the IFTTT-Service-Key header is missing, malformed, or invalid. Verify the Service Key from your Platform dashboard and send it as IFTTT-Service-Key: <key>. Use GET https://connect.ifttt.com/v2/me to validate the key.

Error response format and handling

Errors are returned as JSON objects with type: "error", code (machine‑readable), message (human), and details (array). Example: {"type":"error","code":"not_found","message":"Unknown connection id","details":[] }.

Pagination for queries

Queries return paginated results in responses with type: "list", data: [ ... ] and a next object when there is another page. Use the cursor value from next in subsequent requests (or include cursor in POST body) until next is absent.

Rate limits and retries

Connect API docs do not publish strict rate‑limit headers in public docs; treat 429/5xx as transient, implement exponential backoff and retries. Use X-Request-ID for debugging logs.

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