Flic Python API Docs | dltHub

Flic Hub Studio is a JavaScript-based SDK/runtime (FlicScript) for Flic Hubs that exposes modules to manage buttons, Matter devices, networking and to perform HTTP requests from the hub. The REST API base URL is `` and No centralized REST auth — Flic Hub Studio provides a local JavaScript API; external services must handle their own authentication..

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 Flic data in under 10 minutes.

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What data can I load from Flic?

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

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How do I authenticate with the Flic API?

No centralized REST authentication is documented; access to the hub runtime is through the Flic Hub Studio IDE and package deployment. Outbound HTTP requests use http.makeRequest() where any required headers must be supplied by the developer.

1. Get your credentials

There is no API key or token to retrieve. Developers obtain access by:

1. Logging into the Flic Hub Studio web interface (https://studio.flic.io).
2. Pairing their physical Flic Hub with the Studio account.
3. Deploying packages to the hub via the Studio UI. No separate credential download or generation step exists in the documentation.

2. Add them to .dlt/secrets.toml

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[sources.flic_source]

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 Flic 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 flic_pipeline.py

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

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Pipeline flic_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset flic_data
The duckdb destination used duckdb:/flic.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 flic_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 buttons and http from the Flic 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 flic_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            {"name": "buttons", "endpoint": {"path": "buttonManager.getButtons()"}},
            {"name": "http", "endpoint": {"path": "http.makeRequest(options)"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="flic_pipeline",
        destination="duckdb",
        dataset_name="flic_data",
    )
    load_info = pipeline.run(flic_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("flic_pipeline").dataset()
sessions_df = data.buttons.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM flic_data.buttons LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("flic_pipeline").dataset()
data.buttons.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 Flic 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 / Access Errors

• Since there is no REST authentication, the main failure mode is inability to pair or deploy to the hub. Ensure the hub is paired in the Studio UI and that the user account has permission to deploy packages.

Network / Outbound Request Errors

• The http.makeRequest callback returns a statusCode and content. Non‑2xx codes indicate that the external service rejected the request (e.g., auth failure, bad URL).
• Verify that the hub has internet access and that any required headers (API keys, bearer tokens) are supplied in the request options.

Missing or Incorrect Module Calls

• If a module method such as buttonManager.getButtons() throws an exception, check that the module is correctly imported and that the hub firmware supports the method.
• Consult the SDK documentation for version‑specific method availability.

No REST Endpoint Available

• Attempting to treat Flic Hub Studio as a traditional REST API will result in 404 or connection errors because the platform only provides a local JavaScript API. Use the SDK approach or forward data to an external collector.

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