AI orchestrator: dynamically selects models based on input type
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 11 nodes and runs when a chat message arrives. Along the way it connects Model Selector, Anthropic Chat Model, Google Gemini Chat Model and OpenAI Chat Model and 1 more.
How it runs
Trigger: When a chat message arrives.
- Request Type (Basic LLM Chain): sends a prompt to an LLM, with model: OpenAI Chat Model; output parser: Structured Output Parser
- AI Agent: runs an AI agent, with model: Model Selector; memory: Simple Memory
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (10)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Model Selector
@n8n/n8n-nodes-langchain.modelSelector - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - Anthropic Chat Model
@n8n/n8n-nodes-langchain.lmChatAnthropic - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - OpenRouter Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenRouter
From the author
This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classification and model selection to optimize both performance and…