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Getting Started

Installation

User Guide

Release Notes

Install Extensions

Building Workflows

Components

Flow Control

Expressions

File Handling

Deployment

Integrated Deployment

Workflow Invocation

Best Practices

Advanced Features

AI Extension

Get Started

Build an Agent

Inject context with RAG

LLM Providers

Tutorials

Summarize product reviews

Product FAQ assistant

Restaurant agent

Your first local agent

Task extractor

Reporting

Kerberos

Extensions

Database

Overview

Tutorials

Read and filter data from a database

Write a transformed dataset to a database

Concepts

Ports and Sessions

Query Generation

Type Mapping

Deprecated Framework

Connecting

Connect: Dedicated Node

Connect: File-Based Database

Connect: Generic DB Connector

Register a JDBC Driver

Install Third-Party Driver Plug-in

Drivers on Hub and Server

Kerberos Constrained Delegation

Reading and Transforming

Read Data

Transform Data Visually

Run a Custom SQL Query

Parameterize a Query

Writing and Modifying

Insert, Update, or Merge

Delete Rows

Bulk Load Data

Create and Remove Tables

Use Transactions

Reference

Connector Node Settings

JDBC Driver Reference

Deprecated Node Mapping

Migrate Deprecated Workflows

Python

Overview

Tutorials

Python Scripting Quickstart

Data App Development Using Plotly

Data Processing

K-AI Scripting Assistant

Data Type Conversion

Batch Processing

Jupyter Integration

Environment Management

Pixi Configuration

Dependency Management

Environment Persistence

Channel Mirrors & Air-Gapped Setups

Conda Migration

Alternative Configurations

Conda and Miniforge Setup

Conda Environment Propagation

Manual Python Configuration

Legacy Node Migration

Concepts

Pixi Backend Architecture

Storage Logic

Python Environments on Executors

Reference

Python Scripting API

Bundled Packages

Executor and Hub Configuration

Troubleshooting Guide

API Reference

Inputs and outputs

Classes

Views

Utility functions

R

Deep Learning

Snowflake

Google Cloud

Azure

AWS

Big Data

Big Data Admin

Big Data Secured Cluster

Databricks

Microsoft Fabric

Power BI

Tableau

Report Designer

H2O

Changelogs

Version 5.12

Version 5.11

Version 5.10

Version 5.9

Version 5.8

Version 5.7

Version 5.6

Version 5.5

Version 5.3

Extensions

Bundled Packages ​

The KNIME Python Integration ships with a bundled environment so you can start scripting immediately. This integration works with Python versions 3.10 to 3.14.

Package list ​

The bundled environment contains a curated set of data science and utility packages. For the complete and up to date list of included packages and their versions, refer to the KNIME Anaconda channel.

Metapackages ​

Metapackages simplify the installation of the Python Integration's dependencies. You can find full details, including a version matrix and files, on the KNIME Anaconda channel for knime-python-base.

MetapackageDescription
knime-python-baseMinimal set of packages always required by the Python Integration.
knime-python-scriptingExtends knime-python-base with the full set used in the bundled environment.

Need more packages? ​

The bundled environment cannot be extended. If you need additional packages:

  • Recommended: Use the Python Environment Provider node.
  • Alternative: Create a Conda environment with the metapackage plus your packages — see Set up Conda / Miniforge.

Next steps ​

  • Add packages to an environment
  • Scripting API reference
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