{ "id": "S2fxW8jbFIetu8Vi", "meta": { "templateCredsSetupCompleted": true }, "name": "AI-Powered n8n Workflow Architecture Decision Engine", "tags": [], "nodes": [ { "id": "d5997a32-7d1a-404a-a6c2-83fc4ff3bdaa", "name": "Receive Problem Description via POST", "type": "n8n-nodes-base.webhook", "position": [ -384, 544 ], "parameters": { "path": "af08511e-f5d9-44e3-8a2f-2482b5c3e4a0", "options": {}, "httpMethod": "POST", "responseMode": "responseNode" }, "typeVersion": 2.1 }, { "id": "b709e130-d9f0-4542-974f-f1c65b43e116", "name": "Extract Request Body", "type": "n8n-nodes-base.code", "position": [ -176, 544 ], "parameters": { "jsCode": "// Get the first incoming item\nconst inputData = $input.first().json;\n\n// Return only the body\nreturn [\n {\n json: inputData.body\n }\n];" }, "typeVersion": 2 }, { "id": "8217efaf-8a7f-4e10-8f31-ca8415405eb6", "name": "Multi-Agent Architecture Decision Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 32, 544 ], "parameters": { "text": "=Analyze the following problem description and decide whether it requires a multi-agent workflow in n8n.\n\nIf it requires multi-agent architecture, design the agents with node types and reasons.\n\nIf it does not require multi-agent architecture, clearly state that and provide a simpler workflow instead.\n\nProblem Description:\n{{ $json.description }}", "options": { "systemMessage": "=You are an n8n Multi-Agent Workflow Architect.\n\nYour job is to analyze a given problem description and decide whether it requires a multi-agent workflow or not.\n\nDecision Rules:\n1. If the problem involves multiple independent reasoning steps (classification, extraction, validation, risk scoring, decision making, routing, follow-ups), then design a multi-agent workflow.\n2. If the problem is simple (single-step logic, direct transformation, or basic automation), DO NOT design a multi-agent workflow.\n\nIf Multi-Agent is Required:\n- Clearly define:\n - Agent Name\n - Purpose\n - n8n Node Type\n - Why it is required\n- Keep explanations short and precise.\n- Provide logical flow order.\n\nIf Multi-Agent is NOT Required:\n- Clearly state: \"This does not require a multi-agent workflow.\"\n- Provide a simpler workflow logic.\n- List minimal required nodes.\n- Keep it concise.\n\nOutput Format:\n\nDecision: (Multi-Agent Required / Not Required)\n\nIf Required:\n1. Agent Name\n - Purpose:\n - Node:\n - Reason:\n\nWorkflow Flow:\nStep 1 \u2192\nStep 2 \u2192\nStep 3 \u2192\n\nIf Not Required:\nSimplified Workflow:\n- Node 1:\n- Node 2:\n- Node 3:\n\nBe precise. No long explanations." }, "promptType": "define" }, "typeVersion": 3 }, { "id": "9b3114b1-6179-45de-9f0c-ac9ced81ed5c", "name": "Azure OpenAI GPT-4o-mini", "type": "@n8n/n8n-nodes-langchain.lmChatAzureOpenAi", "position": [ 32, 976 ], "parameters": { "model": "gpt-4o-mini", "options": {} }, "credentials": { "azureOpenAiApi": { "name": "" } }, "typeVersion": 1 }, { "id": "f368874a-c30a-4a57-85ae-b66ccf877a0a", "name": "Parse Decision, Agents & Steps", "type": "n8n-nodes-base.code", "position": [ 464, 544 ], "parameters": { "jsCode": "const items = $input.all();\n\nreturn items.map(item => {\n\n const rawText = item.json.output || \"\";\n\n // Extract Decision\n const decisionMatch = rawText.match(/Decision:\\s*(.*)/);\n const decision = decisionMatch ? decisionMatch[1].trim() : null;\n\n // Extract Agents\n const agentRegex = /Agent Name:\\s*(.*?)\\n\\s*- Purpose:\\s*(.*?)\\n\\s*- Node:\\s*(.*?)\\n\\s*- Reason:\\s*(.*?)(?=\\n\\n|\\n\\d+\\. Agent Name:|$)/gs;\n\n let agents = [];\n let match;\n\n while ((match = agentRegex.exec(rawText)) !== null) {\n agents.push({\n name: match[1]?.trim() || \"\",\n purpose: match[2]?.trim() || \"\",\n node: match[3]?.trim() || \"\",\n reason: match[4]?.trim() || \"\",\n });\n }\n\n // Extract Workflow Steps\n const stepsRegex = /Step \\d+:\\s*(.*?)\\s*\u2192/g;\n let steps = [];\n let stepMatch;\n\n while ((stepMatch = stepsRegex.exec(rawText)) !== null) {\n steps.push(stepMatch[1]?.trim() || \"\");\n }\n\n return {\n json: {\n decision,\n agents,\n workflow_flow: steps\n }\n };\n});" }, "typeVersion": 2 }, { "id": "7344bc04-e81b-47c1-8809-7563dad58a7b", "name": "Build HTML Architecture Report", "type": "n8n-nodes-base.code", "position": [ 672, 544 ], "parameters": { "jsCode": "const items = $input.all();\n\nreturn items.map(item => {\n\n const data = item.json;\n\n const decisionClass =\n data.decision === \"Multi-Agent Required\"\n ? \"multi\"\n : \"simple\";\n\n const agentsHTML = Array.isArray(data.agents)\n ? data.agents.map(agent => `\n
\n
${agent.name || \"\"}
\n\n
Purpose
\n
${agent.purpose || \"\"}
\n\n
Node
\n
${agent.node || \"\"}
\n\n
Reason
\n
${agent.reason || \"\"}
\n
\n `).join(\"\")\n : \"\";\n\n const stepsHTML = Array.isArray(data.workflow_flow)\n ? data.workflow_flow.map(step =>\n `
${step}
`\n ).join(\"\")\n : \"\";\n\n const html = `\n \n \n \n \n\n \n\n
\n
\n ${data.decision || \"No Decision\"}\n
\n
\n\n ${agentsHTML ? `\n
\n
Agents
\n
\n ${agentsHTML}\n
\n
\n ` : \"\"}\n\n ${stepsHTML ? `\n
\n
Workflow Flow
\n
\n ${stepsHTML}\n
\n
\n ` : \"\"}\n\n \n \n `;\n\n return {\n json: {\n html\n }\n };\n});" }, "typeVersion": 2 }, { "id": "5f52eae8-6d9a-4be0-b2a2-6aeac4af1ba0", "name": "Return HTML Report to Caller", "type": "n8n-nodes-base.respondToWebhook", "position": [ 880, 544 ], "parameters": { "options": {}, "respondWith": "text", "responseBody": "={{$node['Build HTML Architecture Report'].json['html']}}" }, "typeVersion": 1.5 }, { "id": "26c1a118-683d-4cbd-a8b1-308b8b28312d", "name": "Sticky Note - Overview", "type": "n8n-nodes-base.stickyNote", "position": [ -1040, 384 ], "parameters": { "width": 540, "height": 384, "content": "## \ud83d\udfe1 Workflow Overview\n\n### How it works\nThis workflow serves as an **AI-powered n8n Multi-Agent Architecture Advisor**. It accepts a plain-text problem description via a POST webhook and uses an Azure OpenAI-backed AI Agent to decide whether the problem warrants a multi-agent workflow or a simpler single-flow automation.\n\nThe AI Agent applies structured decision rules: if the problem involves multiple independent reasoning steps \u2014 such as classification, validation, routing, or risk scoring \u2014 it designs a full multi-agent architecture with named agents, n8n node types, and logical flow. If the problem is simple, it recommends a minimal node list instead.\n\nThe raw AI output is then parsed to extract the decision, agent definitions, and workflow steps. Finally, a styled HTML report is generated and returned directly in the webhook response as a visual card-based dashboard.\n\n### Setup steps\n1. **Activate the workflow** in n8n to register the Webhook URL.\n2. **Configure Azure OpenAI credentials** \u2014 ensure the `Azure Open AI account` credential is valid and the `gpt-4o-mini` deployment is active in your Azure resource.\n3. **Send a POST request** to the webhook URL with body: `{ \"description\": \"Your problem description here\" }`\n4. The response will be a styled HTML page with the architecture decision and agent breakdown.\n\n### Customization\n- Replace `gpt-4o-mini` with `gpt-4o` for higher reasoning accuracy on complex problems.\n- Modify the AI Agent system prompt to add new decision rules or output formats.\n- Update card styles in the **Build HTML Architecture Report** node to match your brand." }, "typeVersion": 1 }, { "id": "8fef77e7-2fd2-4ae8-9455-083f6bfe0588", "name": "Sticky Note - Input Section", "type": "n8n-nodes-base.stickyNote", "position": [ -448, 384 ], "parameters": { "color": 7, "width": 388, "height": 326, "content": "## \ud83d\udce5 Input & Extraction\nReceives a POST request with a `description` field and extracts the raw request body for downstream processing." }, "typeVersion": 1 }, { "id": "25685a95-fea8-49f1-8ccf-ff9d4130e7b1", "name": "Sticky Note - AI Section", "type": "n8n-nodes-base.stickyNote", "position": [ -32, 384 ], "parameters": { "color": 7, "width": 432, "height": 374, "content": "## \ud83e\udd16 AI Architecture Decision\nThe AI Agent evaluates the problem description and decides if a multi-agent workflow is needed. It returns structured agent definitions, node types, and a logical workflow flow." }, "typeVersion": 1 }, { "id": "6e185fad-3128-41e4-9342-ba1a9dde7a87", "name": "Sticky Note - Output Section", "type": "n8n-nodes-base.stickyNote", "position": [ 448, 384 ], "parameters": { "color": 7, "width": 592, "height": 374, "content": "## \ud83d\udcca Parse, Build & Respond\nParses the AI output into structured decision, agents, and steps. Renders a styled HTML dashboard and returns it as the final webhook response." }, "typeVersion": 1 }, { "id": "6e67700a-9b33-4794-891a-1df42143db3b", "name": "Sticky Note - Azure Warning", "type": "n8n-nodes-base.stickyNote", "position": [ -192, 816 ], "parameters": { "color": 7, "width": 360, "height": 300, "content": "\u26a0\ufe0f **Azure OpenAI Credentials Required**\nThis node requires a valid `azureOpenAiApi` credential with an active `gpt-4o-mini` deployment. Missing or misconfigured credentials will cause all AI analysis to fail." }, "typeVersion": 1 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "8f430c84-ba4f-4cc6-a871-ded693d19507", "connections": { "Extract Request Body": { "main": [ [ { "node": "Multi-Agent Architecture Decision Agent", "type": "main", "index": 0 } ] ] }, "Azure OpenAI GPT-4o-mini": { "ai_languageModel": [ [ { "node": "Multi-Agent Architecture Decision Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Build HTML Architecture Report": { "main": [ [ { "node": "Return HTML Report to Caller", "type": "main", "index": 0 } ] ] }, "Parse Decision, Agents & Steps": { "main": [ [ { "node": "Build HTML Architecture Report", "type": "main", "index": 0 } ] ] }, "Receive Problem Description via POST": { "main": [ [ { "node": "Extract Request Body", "type": "main", "index": 0 } ] ] }, "Multi-Agent Architecture Decision Agent": { "main": [ [ { "node": "Parse Decision, Agents & Steps", "type": "main", "index": 0 } ] ] } } }