Reference · Tools
Simple LLM
Call OpenAI's API
Simple LLM sends a prompt to a language model and returns the generated text, one item per input item, with optional built-in web search when the answer needs current information. A typical build is summarising or rewriting content mid-workflow without configuring a provider-specific node.
- Node type
- Action
- Parameters
- 7
- Outputs
- Output, Error
- Credentials
- None required
Simple LLM
Generate text with AI
Overview
Call OpenAI’s API to process items with AI. Supports system prompts, user prompts with expressions, configurable model parameters, and optional web search.
Category: AI
Tool Name: simple_llm
Version: 1
Appearance: Icon: brain | Color: #667eea
Node Type
Action — processes input items and produces output
Input / Output
| Direction | Port(s) |
|---|---|
| Input | Input |
| Output | Output, Error |
Credentials
This tool does not require any credentials.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| System Prompt | string | No | You are a helpful assistant. | System message that sets the behavior of the assistant. Supports expressions like {{ $json.field }}. |
| User Prompt | string | Yes | {{ $json.text }} | The user message to send to the LLM. Supports expressions like {{ $json.field }}. |
| Model | options | No | gpt-4.1-mini | The OpenAI model to use for completion. |
Options: gpt-5.2, gpt-5, gpt-5-mini, gpt-5-nano, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano | ||||
| Web Search | boolean | No | false | Enable built-in web search. When true, the model can search the web for up-to-date information before responding. |
| Max Concurrency | number | No | 50 | Maximum number of concurrent API calls to OpenAI. |
| Include Input in Output | boolean | No | true | If true, includes the original input data in the output item. |
| Response Field Name | string | No | llmResponse | The field name to store the LLM response in the output item. |
Output Data
One output item per input item. The generated text is written to the field named by Response Field Name (llmResponse by default), alongside an llmMetadata block. With Include Input in Output on — the default — the original item fields are kept underneath; turn it off to return only the response and its metadata. Binary data is forwarded either way.
{
"llmResponse": "The article argues that ...",
"llmMetadata": {
"model": "gpt-4.1-mini",
"usage": { "inputTokens": 812, "outputTokens": 143, "totalTokens": 955 },
"status": "completed",
"webSearchUsed": false,
"processedAt": 1765432100000
}
}
usagereports the token counts for that single call, which is what the run is billed on.webSearchUsedsays whether the model actually ran a web search for this item, not merely whether Web Search was enabled. A model given the tool may still answer without it.processedAtis a millisecond timestamp taken when the item finished.
Reference the result downstream by expression, e.g. {{ $json.llmResponse }}.
Usage Examples
- generate a summary of the article
- ask AI to write a response email
- use LLM to analyze the feedback
- create a product description
- compose a personalized message
Example Configuration
Send each item’s text field to the model and store the reply on llmResponse:
{
"type": "simple_llm",
"parameters": {
"systemPrompt": "You are a helpful assistant.",
"userPrompt": "{{ $json.text }}",
"model": "gpt-4.1-mini",
"webSearch": false,
"maxConcurrency": 50,
"includeInput": true,
"responseFieldName": "llmResponse"
}
}
Error Handling
| Mode | Behavior |
|---|---|
| stop | Halts workflow on first error |
| continue | Skips failed items, passes successful ones through |
| errorPort | Routes failed items to Error output port |
Tips
Sends a prompt to a language model (GPT) and returns the generated text response. Supports optional built-in web search for up-to-date information. Use when you need AI-generated content — summaries, analysis, rewrites, classifications, or any free-text generation task. Produces one item per input item containing the LLM response text.
Frequently asked questions
What is it best used for?
Free-text generation — summaries, analysis, rewrites and classifications. When you need structured fields instead, use Structured Output so the result is parseable rather than prose.
Can it use current information?
Yes, optional built-in web search lets it answer from up-to-date sources rather than training data alone.
How many outputs does it produce?
One item per input item, each carrying the response text, so a batch of records is processed record by record.
Does it need a credential?
No — unlike the provider-specific chat nodes, it runs without you configuring one, which makes it the quickest way to add an AI step.
Build with the Simple LLM node
Drop it into a workflow, wire it to an agent, or call it on a schedule.
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