{ "edges": [ { "to": "8217efaf-8a7f-4e10-8f31-ca8415405eb6", "from": "b709e130-d9f0-4542-974f-f1c65b43e116", "toPort": "0", "fromPort": "0" }, { "to": "8217efaf-8a7f-4e10-8f31-ca8415405eb6", "from": "9b3114b1-6179-45de-9f0c-ac9ced81ed5c", "toPort": "0", "fromPort": "ai_languageModel" }, { "to": "5f52eae8-6d9a-4be0-b2a2-6aeac4af1ba0", "from": "7344bc04-e81b-47c1-8809-7563dad58a7b", "toPort": "0", "fromPort": "0" }, { "to": "7344bc04-e81b-47c1-8809-7563dad58a7b", "from": "f368874a-c30a-4a57-85ae-b66ccf877a0a", "toPort": "0", "fromPort": "0" }, { "to": "b709e130-d9f0-4542-974f-f1c65b43e116", "from": "d5997a32-7d1a-404a-a6c2-83fc4ff3bdaa", "toPort": "0", "fromPort": "0" }, { "to": "f368874a-c30a-4a57-85ae-b66ccf877a0a", "from": "8217efaf-8a7f-4e10-8f31-ca8415405eb6", "toPort": "0", "fromPort": "0" } ], "nodes": [ { "id": "d5997a32-7d1a-404a-a6c2-83fc4ff3bdaa", "name": "Receive Problem Description via POST", "type": "trigger", "params": { "kind": "n8n-nodes-base.webhook", "path": "af08511e-f5d9-44e3-8a2f-2482b5c3e4a0", "options": {}, "httpMethod": "POST", "responseMode": "responseNode" }, "position": [ -384, 544 ] }, { "id": "b709e130-d9f0-4542-974f-f1c65b43e116", "name": "Extract Request Body", "type": "code", "params": { "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];" }, "position": [ -176, 544 ] }, { "id": "8217efaf-8a7f-4e10-8f31-ca8415405eb6", "name": "Multi-Agent Architecture Decision Agent", "type": "llm", "params": { "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 }}", "prompt": "=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 →\nStep 2 →\nStep 3 →\n\nIf Not Required:\nSimplified Workflow:\n- Node 1:\n- Node 2:\n- Node 3:\n\nBe precise. No long explanations." }, "provider": "openai", "promptType": "define", "approximated": true, "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 →\nStep 2 →\nStep 3 →\n\nIf Not Required:\nSimplified Workflow:\n- Node 1:\n- Node 2:\n- Node 3:\n\nBe precise. No long explanations." }, "position": [ 32, 544 ] }, { "id": "9b3114b1-6179-45de-9f0c-ac9ced81ed5c", "name": "Azure OpenAI GPT-4o-mini", "type": "llm", "params": { "model": "gpt-4o-mini", "options": {}, "provider": "openai" }, "position": [ 32, 976 ] }, { "id": "f368874a-c30a-4a57-85ae-b66ccf877a0a", "name": "Parse Decision, Agents & Steps", "type": "code", "params": { "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*→/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});" }, "position": [ 464, 544 ] }, { "id": "7344bc04-e81b-47c1-8809-7563dad58a7b", "name": "Build HTML Architecture Report", "type": "code", "params": { "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

${agent.name || \"\"}

\n\n

Purpose

${agent.purpose || \"\"}

\n\n

Node

${agent.node || \"\"}

\n\n

Reason

${agent.reason || \"\"}

\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 ${data.decision || \"No Decision\"}\n

\n\n ${agentsHTML ? `\n

Agents

\n ${agentsHTML}\n

\n ` : \"\"}\n\n ${stepsHTML ? `\n

Workflow Flow

\n ${stepsHTML}\n

\n ` : \"\"}\n\n \n \n `;\n\n return {\n json: {\n html\n }\n };\n});" }, "position": [ 672, 544 ] }, { "id": "5f52eae8-6d9a-4be0-b2a2-6aeac4af1ba0", "name": "Return HTML Report to Caller", "type": "passthrough", "params": { "mode": "noOp", "options": {}, "respondWith": "text", "responseBody": "={{$node['Build HTML Architecture Report'].json['html']}}" }, "position": [ 880, 544 ] } ] }

Decide multi‑agent vs simple workflows using Azure OpenAI GPT‑4o‑mini