Agent Prompter

This node builds and executes an agent that responds to a single user prompt using a language model and a set of user-defined tools.

Each tool is represented as a KNIME workflow with configurations (e.g., strings, numbers, column selection) and can optionally accept input data tables. While the agent does not have access to raw data, it is informed about available tables through metadata — including column names and types — enabling it to select suitable data for each tool as needed.

When the node is executed, the agent reasons step-by-step to fulfill the user’s prompt. It may call one or more tools in sequence, using the output of one tool as input for another. The model autonomously selects tools and input data based on the prompt and the tools’ names and descriptions. Including clear tool descriptions and example use cases significantly improves the agent’s decision-making.

The entire internal reasoning process — including tool calls and decisions — is captured and available via the conversation output table. This node is designed for non-interactive, one-shot execution. For interactive, multi-turn conversations, use the Chat Agent Prompter node.

Options

System message: Instructions provided by the workflow builder that guide the agent's behavior. It typically defines the agent’s role, its tone, boundaries, and behavioral rules.This message is prioritized over the user message and should not contain any information that the user can inject in order to prevent prompt injection attacks.
User message: Message from the end user, prioritized behind the developer message.
Tool column: The column of the tools table holding the tools the agent can use.
Conversation column: The column containing the conversation history if a conversation history table is connected.
Conversation column name: Name of the conversation column if no conversation history table is connected.
Recursion limit: The maximum number of times the agent can repeat its steps to avoid getting stuck in an endless loop. If not provided, defaults to 25.
Debug mode: In debug mode, tool executions are displayed as meta nodes in the agent workflow and the meta node is kept in case of an error in the tool.
Data message prefix: Prefix for the data message shown to the agent. You can use this to customize the instructions about the data repository.

Input Ports

: The chat model to use.
: The tools the agent can use.
…: The table containing the conversation held so far.
…: The data inputs for the agent.

Output Ports

: The conversation between the LLM and the tools reflecting the agent execution.
…: The data outputs of the agent.

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Installation

To use this node in KNIME, install the extension KNIME Python Extension Development (Labs) from the below update site following our NodePit Product and Node Installation Guide:

v5.5

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.5.0.v202506241445

On NodePit since: 2025-07-02

Last update: 2025-07-03

KNIME versions: Since v5.5

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