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AI & researchAgentChat TriggerOpenAI Chat ModelMemory Buffer WindowEdit Fields

Use any LLM-Model via OpenRouter

Configured with Agent, Chat Trigger, OpenAI Chat Model, Memory Buffer Window. Review the connections and setup before adapting this template.

Workflow preview
TransformSettingsEdit FieldsTriggerWhen chat messagereceivedChat TriggerAI & modelsAI AgentAgentData storeChat MemoryMemory Buffer WindowAI & modelsLLM ModelOpenAI Chat Model

Node categories in this workflow

  • Transform
  • Trigger
  • AI & models
  • Data store

Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.

Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.

How it works

The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.

Workflow node names, types, and operations
NodeCategoryTypeOperation
SettingsTransformEdit FieldsSee node parameters
When chat message receivedTriggerChat TriggerSee node parameters
AI AgentAI & modelsAgentSee node parameters
Chat MemoryData storeMemory Buffer WindowSee node parameters
LLM ModelAI & modelsOpenAI Chat ModelSee node parameters

Setup

  1. Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
  2. Connect your own credentials through n8n’s credential store. Replace REPLACE_WITH_* values with your resource identifiers.
  3. Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
  4. Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.

Requirements

An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.

Credential types declared in the source

  • openAiApi

Workflow notes

Settings

Settings

Specify the model

Run LLM

Run LLM

Using OpenRouter to make model fully configurable

Model examples

Model examples

  • openai/o3-mini
  • google/gemini-2.0-flash-001
  • deepseek/deepseek-r1-distill-llama-8b
  • mistralai/mistral-small-24b-instruct-2501:free
  • qwen/qwen-turbo

For more see https://openrouter.ai/models

Use any LLM-Model via OpenRouter

Use any LLM-Model via OpenRouter

Purpose and structure

This folder contains the Use any LLM-Model via OpenRouter workflow. Its executable nodes use agent, chatTrigger, lmChatOpenAi, memoryBufferWindow, set. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When chat message received

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Stickynote/1719_Stickynote_Automation_Triggered.json MIT; preserve source notices.

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