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ResinKit SDK (Python) provides a high-level client and Jupyter-friendly UI to interact with ResinKit services.
  • Core features: authenticated client, Variables management, Data Sources management, and a Flink SQL console with live streaming results
  • Best environment: JupyterLab/Notebook with Panel widgets enabled

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Prerequisites

  • Python 3.9+
  • JupyterLab/Notebook
  • panel installed for UI components
Use a dedicated virtual environment to isolate dependencies.
1

Install ResinKit SDK

From source (inside this repository):
git clone https://github.com/resink-ai/resinkit-sdk-python
cd resinkit-sdk-python
uv sync --dev
From PyPI:
pip install resinkit-sdk-python
PyPI distribution may be lagging behind the source code.
Verify installation:
python -c "import resinkit; print('resinkit', resinkit.__version__)"
2

Configure connection settings

You can collect to resinkit instance from python:
from resinkit import Resinkit
rs = Resinkit()
rs.ui_flink_sql().show()
Quick connectivity tests:
# show Flink SQL UI
rs.ui_flink_sql().show()

# show Variables UI
rs.ui_vars().show()

# show Data Sources UI
rs.ui_sources().show()

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Explore the UI tools

Variables UI

Create, list, and delete server-side variables.

Data Sources UI

Manage SQL/Kafka sources, test connections, and crawl schemas.

Flink SQL UI

Run SQL with live streaming results in a notebook-friendly console.

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Programmatic usage

Use ResinkitAPIClient for direct API calls (outside the UI widgets): 
from resinkit.core.resinkit_api_client import ResinkitAPIClient
client = ResinkitAPIClient()
variables = await client.list_variables()
Flink SQL streaming responses are newline-delimited JSON frames. The console UI assembles and renders these for you; see the Flink SQL UI guide for details.