Dynamic AI model router for query optimization with OpenRouter
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) Routing Agent (v1.9), AI Agent (v1.9) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (6)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - OpenRouter Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenRouter - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - AI Agent
@n8n/n8n-nodes-langchain.agent - Auto-fixing Output Parser
@n8n/n8n-nodes-langchain.outputParserAutofixing - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
What it does
The Agent Decisioner is a dynamic, AI-powered routing system that automatically selects the most appropriate large language model (LLM) to respond to a user's query based on the query’s content and purpose. This workflow ensures dynamic, optimized AI responses by intelligently routing queries to the best-suited model. --- Advantages 🔁 Automatic Model Routing: Automatically selects the best model for the job, improving efficiency and relevance of responses. 🎯 Optimized Use of Resources: Avoids overuse of expensive models like GPT-4 by routing simpler queries to lightweight models. 📚 Model-Aware Reasoning: Uses detailed metadata about model capabilities (e.g., reasoning, coding, web search) for intelligent selection. 📥 Modular and Extendable: Easy to integrate with other tools or expand by adding more models or custom decision logic. 👨💻 Ideal for RAG and Multi-Agent Systems: Can serve as the brain behind more complex agent frameworks or Retrieval-Augmented Generation pipelines. --- How It Works 1. Chat Trigger: The workflow starts when a user sends a message, triggering the Routing Agent. 2. Model Selection: The AI Agent analyzes the query and selects the best-suited model from the available options (e.g., Claude 3.7 Sonnet for coding, Perplexity/Sonar for web searches, GPT-4o Mini for reasoning). 3. Structured Output: The agent returns a JSON response with the user’s prompt and the chosen model. 4. Execution: The selected model processes the query and generates a response, ensuring optimal performance for the task. Set Up Steps 1. Configure Nodes: - Chat Trigger: Set up the webhook to receive user messages. - Routing Agent (AI Agent): Define the system message with model strengths and JSON output rules. - OpenRouter Chat Model: Connect to OpenRouter for model access. - Structured Output Parser: Ensure it validates the JSON response format (prompt + model). - Execution Agent (AI Agent1): Configure it to forward the prompt to the selected model. 2. Connect Nodes: - Link the Chat Trigger to the Routing Agent. - Connect the OpenRouter Chat Model and Output Parser to the Routing Agent. - Route the parsed JSON to the Execution Agent, which uses the chosen model via OpenRouter Chat Model1. 3. Credentials: - Ensure OpenRouter API credentials are correctly set for both chat model nodes. 4. Test & Deploy: - Activate the workflow and test with sample queries to verify model selection logic. - Adjust the routing rules if needed for better accuracy. --- Need help customizing? Contact me(mailto:[email protected]) for consulting and support or add me on Linkedin.