{ "workflow": { "id": 4237, "name": "Dynamic AI model router for query optimization with OpenRouter", "views": 4630, "recentViews": 1, "totalViews": 4630, "createdAt": "2025-05-20T07:40:19.495Z", "description": "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.\n\nThis workflow ensures **dynamic, optimized AI responses** by intelligently routing queries to the best-suited model.\n\n---\n\n### **Advantages**\n\n* **🔁 Automatic Model Routing:**\n Automatically selects the best model for the job, improving efficiency and relevance of responses.\n\n* **🎯 Optimized Use of Resources:**\n Avoids overuse of expensive models like GPT-4 by routing simpler queries to lightweight models.\n\n* **📚 Model-Aware Reasoning:**\n Uses detailed metadata about model capabilities (e.g., reasoning, coding, web search) for intelligent selection.\n\n* **📥 Modular and Extendable:**\n Easy to integrate with other tools or expand by adding more models or custom decision logic.\n\n* **👨‍💻 Ideal for RAG and Multi-Agent Systems:**\n Can serve as the brain behind more complex agent frameworks or Retrieval-Augmented Generation pipelines.\n\n---\n\n\n### **How It Works** \n\n1. **Chat Trigger**: The workflow starts when a user sends a message, triggering the **Routing Agent**. \n2. **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). \n3. **Structured Output**: The agent returns a **JSON response** with the user’s prompt and the chosen model. \n4. **Execution**: The selected model processes the query and generates a response, ensuring optimal performance for the task. \n\n### **Set Up Steps** \n\n1. **Configure Nodes**: \n - **Chat Trigger**: Set up the webhook to receive user messages. \n - **Routing Agent (AI Agent)**: Define the system message with model strengths and JSON output rules. \n - **OpenRouter Chat Model**: Connect to OpenRouter for model access. \n - **Structured Output Parser**: Ensure it validates the JSON response format (`prompt` + `model`). \n - **Execution Agent (AI Agent1)**: Configure it to forward the prompt to the selected model. \n\n2. **Connect Nodes**: \n - Link the **Chat Trigger** to the **Routing Agent**. \n - Connect the **OpenRouter Chat Model** and **Output Parser** to the **Routing Agent**. \n - Route the parsed JSON to the **Execution Agent**, which uses the chosen model via **OpenRouter Chat Model1**. \n\n3. **Credentials**: \n - Ensure **OpenRouter API credentials** are correctly set for both chat model nodes. \n\n4. **Test & Deploy**: \n - Activate the workflow and test with sample queries to verify model selection logic. \n - Adjust the routing rules if needed for better accuracy. \n\n---\n\n### **Need help customizing?** \n[Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/). ", "workflow": { "id": "uNLWQ7BSozpoehpU", "meta": { "instanceId": "a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462", "templateCredsSetupCompleted": true }, "name": "Automated AI Routing with OpenRouter", "tags": [], "nodes": [ { "id": "25903a04-24d2-41f9-bf34-5d6234e642e5", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "position": [ -180, -180 ], "webhookId": "a0032740-26d8-491b-93f9-2250906d0e17", "parameters": { "options": {} }, "typeVersion": 1.1 }, { "id": "fabffdee-3c1e-47db-a4e9-f6473a6e9257", "name": "OpenRouter Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "position": [ 0, 40 ], "parameters": { "options": {} }, "credentials": { "openRouterApi": { "id": "credential-id", "name": "openRouterApi Credential" } }, "typeVersion": 1 }, { "id": "c53fe672-92cb-4d88-b4f6-f413fb00ad6a", "name": "Structured Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserStructured", "position": [ 340, 280 ], "parameters": { "schemaType": "manual", "inputSchema": "{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"prompt\": {\n\t\t\t\"type\": \"string\"\n\t\t},\n\t\t\"model\": {\n\t\t\t\"type\": \"string\"\n\t\t}\n\t}\n}" }, "typeVersion": 1.2 }, { "id": "d60a9d61-c611-4813-bf85-e8f8faaa21b6", "name": "OpenRouter Chat Model1", "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter", "position": [ 420, 40 ], "parameters": { "model": "={{ $json.output.model }}", "options": {} }, "credentials": { "openRouterApi": { "id": "credential-id", "name": "openRouterApi Credential" } }, "typeVersion": 1 }, { "id": "ef9ceacb-55e4-4795-aa18-976997ec3717", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -180, -420 ], "parameters": { "width": 840, "height": 180, "content": "## Dynamic Model Selector for Optimal AI Responses\n\nThe **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.\n\nThis workflow ensures **dynamic, optimized AI responses** by intelligently routing queries to the best-suited model." }, "typeVersion": 1 }, { "id": "4d688ad7-b463-4e72-9b79-4b9142f022d2", "name": "Routing Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 40, -180 ], "parameters": { "options": { "systemMessage": "=You are a **Routing Agent**.\n\nYour task is to analyze user queries and determine the most appropriate model to handle each specific use case.\n\n## Available Models\n\nYou have access to the following models:\n\n1. **perplexity/sonar**\n2. **openai/gpt-4o-mini**\n3. **anthropic/claude-3.7-sonnet**\n4. **meta-llama/llama-3-70b-instruct**\n5. **google/gemini-2.5-pro-preview**\n6. **qwen/qwen-qwq-32b**\n7. **openai/codex-mini**\n8. **openai/o1-pro**\n\n## Model Strengths\n\n### 1. perplexity/sonar\n- Built-in web search capability\n- Provides citations and customizable sources\n- Ideal for retrieving live, up-to-date information from the web\n\n### 2. openai/gpt-4o-mini\n- Cost-efficient language model optimized for advanced reasoning tasks\n- Excels in science and mathematics\n- Best suited for problems requiring careful, well-thought-out responses involving multiple variables or connections\n\n### 3. anthropic/claude-3.7-sonnet\n- High proficiency in coding tasks, scoring ~94% on SWE-Bench Verified\n- Enhances data science expertise by navigating unstructured data and utilizing multiple tools for insights\n- Handles very long documents and maintains coherence over extended conversations or analyses\n- Performs well in creative writing tasks such as storytelling, dialogue generation, and summarization\n- Tends to produce responses that are more aligned with safety and ethical guidelines\n\n### 4. meta-llama/llama-3-70b-instruct\n- Strong performance in coding and reasoning tasks\n- Suitable for complex programming and technical problem-solving\n- Supports long context windows, making it ideal for extended analyses\n\n### 5. google/gemini-2.5-pro-preview\n- Advanced multimodal capabilities, handling both text and images\n- Excels in tasks requiring integration of visual and textual information\n- Ideal for complex problem-solving involving diverse data types\n\n### 6. qwen/qwen-qwq-32b\n- Specialized in reasoning and problem-solving tasks\n- Effective in handling logical puzzles and complex analytical queries\n\n### 7. openai/codex-mini\n- Optimized for code generation and completion tasks\n- Suitable for lightweight coding tasks and quick code snippets\n\n### 8. openai/o1-pro\n- Designed for complex reasoning with enhanced computational resources\n- Performs well in STEM-related tasks, including physics, chemistry, and biology\n- Capable of handling large context windows, making it suitable for in-depth analyses\n\n## Output Format\n\nYour output must always be a valid JSON object in the following format:\n\n```json\n{\n \"prompt\": \"user query goes here\",\n \"model\": \"selected-model-name\"\n}\n```\n\n- The **\"prompt\"** field should contain the exact query to be sent to the selected model.\n- The **\"model\"** field should contain the model name (one of: perplexity/sonar, openai/gpt-4o-mini, anthropic/claude-3.7-sonnet, meta-llama/llama-3-70b-instruct, google/gemini-2.5-pro-preview, qwen/qwen-qwq-32b, openai/codex-mini, openai/o1-pro).\n\n**Important:** Only return the JSON object. Do not include any explanations or additional text." }, "hasOutputParser": true }, "typeVersion": 1.9 }, { "id": "94c49c22-9697-4230-ba35-5159041cfdc7", "name": "AI Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 400, -180 ], "parameters": { "text": "={{ $json.output.prompt }}", "options": {}, "promptType": "define" }, "typeVersion": 1.9 }, { "id": "54c4278e-c185-4ab4-b21e-d8e837b14818", "name": "Auto-fixing Output Parser", "type": "@n8n/n8n-nodes-langchain.outputParserAutofixing", "position": [ 140, 60 ], "parameters": { "options": { "prompt": "Instructions:\n--------------\n{instructions}\n--------------\nCompletion:\n--------------\n{completion}\n--------------\n\nAbove, the Completion did not satisfy the constraints given in the Instructions.\nError:\n--------------\n{error}\n--------------\n\nPlease try again. Please only respond with an answer that satisfies the constraints laid out in the Instructions:" } }, "typeVersion": 1 }, { "id": "2813da6b-423b-41d6-8eb4-664f616ca8d5", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 140, 220 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini" }, "options": {} }, "credentials": { "openAiApi": { "id": "credential-id", "name": "openAiApi Credential" } }, "typeVersion": 1.2 } ], "active": false, "pinData": {}, "settings": { "executionOrder": "v1" }, "versionId": "bb8e8d46-ce32-49e0-a843-496c6e026902", "connections": { "Routing Agent": { "main": [ [ { "node": "AI Agent", "type": "main", "index": 0 } ] ] }, "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "Auto-fixing Output Parser", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenRouter Chat Model": { "ai_languageModel": [ [ { "node": "Routing Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenRouter Chat Model1": { "ai_languageModel": [ [ { "node": "AI Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Structured Output Parser": { "ai_outputParser": [ [ { "node": "Auto-fixing Output Parser", "type": "ai_outputParser", "index": 0 } ] ] }, "Auto-fixing Output Parser": { "ai_outputParser": [ [ { "node": "Routing Agent", "type": "ai_outputParser", "index": 0 } ] ] }, "When chat message received": { "main": [ [ { "node": "Routing Agent", "type": "main", "index": 0 } ] ] } } }, "lastUpdatedBy": 29, "workflowInfo": { "nodeCount": 9, "nodeTypes": { "n8n-nodes-base.stickyNote": { "count": 1 }, "@n8n/n8n-nodes-langchain.agent": { "count": 2 }, "@n8n/n8n-nodes-langchain.chatTrigger": { "count": 1 }, "@n8n/n8n-nodes-langchain.lmChatOpenAi": { "count": 1 }, "@n8n/n8n-nodes-langchain.lmChatOpenRouter": { "count": 2 }, "@n8n/n8n-nodes-langchain.outputParserAutofixing": { "count": 1 }, "@n8n/n8n-nodes-langchain.outputParserStructured": { "count": 1 } } }, "status": "published", "user": { "name": "Davide", "username": "n3witalia", "bio": "Full-stack Web Developer based in Italy specialising in Marketing & AI-powered automations. For business enquiries, send me an email at info@n3w.it or add me on Linkedin.com/in/davideboizza", "verified": true, "links": [ "https://n3w.it" ], "avatar": "https://gravatar.com/avatar/d41b8a0aa81139243509c58870f5b4be292824a507ab57d10ed066d8628ed8da?r=pg&d=retro&size=200" }, "nodes": [ { "id": 565, "icon": "fa:sticky-note", "name": "n8n-nodes-base.stickyNote", "codex": { "data": { "alias": [ "Comments", "Notes", "Sticky" ], "categories": [ "Core Nodes" ], "nodeVersion": "1.0", "codexVersion": "1.0", "subcategories": { "Core Nodes": [ "Helpers" ] } } }, "group": "[\"input\"]", "defaults": { "name": "Sticky Note", "color": "#FFD233" }, "iconData": { "icon": "sticky-note", "type": "icon" }, "displayName": "Sticky Note", "typeVersion": 1, "nodeCategories": [ { "id": 9, "name": "Core Nodes" } ] }, { "id": 1119, "icon": "fa:robot", "name": "@n8n/n8n-nodes-langchain.agent", "codex": { "data": { "alias": [ "LangChain", "Chat", "Conversational", "Plan and Execute", "ReAct", "Tools" ], 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