The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "AAE Agent Router",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "aae-chat",
"authentication": "headerAuth",
"options": {}
},
"id": "webhook-trigger",
"name": "AAE Chat Webhook",
"type": "n8n-nodes-base.webhook",
"typeVersion": 1,
"position": [
250,
300
],
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"values": {
"string": [
{
"name": "userId",
"value": "={{ $json.userId }}"
},
{
"name": "userMessage",
"value": "={{ $json.message }}"
},
{
"name": "timestamp",
"value": "={{ new Date().toISOString() }}"
},
{
"name": "knowledgeLakeUrl",
"value": "={{ $env.KNOWLEDGE_LAKE_URL }}"
},
{
"name": "dashboardCallbackUrl",
"value": "={{ $env.AAE_DASHBOARD_URL }}/api/chat/callback"
}
],
"array": [
{
"name": "conversationHistory",
"value": "={{ $json.conversationHistory || [] }}"
}
]
},
"options": {}
},
"id": "set-variables",
"name": "Initialize Variables",
"type": "n8n-nodes-base.set",
"typeVersion": 3,
"position": [
450,
300
]
},
{
"parameters": {
"url": "={{ $node['Initialize Variables'].json.knowledgeLakeUrl }}/api/query",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "httpHeaderAuth",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "query",
"value": "={{ $node['Initialize Variables'].json.userMessage }}"
},
{
"name": "userId",
"value": "={{ $node['Initialize Variables'].json.userId }}"
},
{
"name": "limit",
"value": 5
}
]
},
"options": {
"timeout": 10000
}
},
"id": "query-knowledge-lake",
"name": "Query Knowledge Lake",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
650,
300
],
"continueOnFail": true
},
{
"parameters": {
"jsCode": "// Analyze query to determine optimal agent\nconst message = $input.item.json.userMessage.toLowerCase();\nconst knowledgeContext = $input.item.json.results || [];\n\n// Query patterns\nconst patterns = {\n technical: /\\b(code|debug|api|database|deploy|error|fix|build|test|git|docker|railway|typescript|javascript|python|sql|postgres)\\b/gi,\n business: /\\b(strategy|roi|budget|revenue|costs|clients|growth|market|competition|sales|pricing|profit)\\b/gi,\n research: /\\b(what is|how does|explain|research|latest|trends|compare|versus|study|analyze)\\b/gi,\n consulting: /\\b(should i|recommend|advice|best practice|approach|decision|planning|optimize|improve)\\b/gi,\n multiAgent: /\\b(comprehensive|full analysis|multiple perspectives|all aspects|thorough|detailed review)\\b/gi\n};\n\n// Count matches\nconst scores = {\n technical: (message.match(patterns.technical) || []).length,\n business: (message.match(patterns.business) || []).length,\n research: (message.match(patterns.research) || []).length,\n consulting: (message.match(patterns.consulting) || []).length,\n multiAgent: (message.match(patterns.multiAgent) || []).length\n};\n\n// Determine complexity\nconst wordCount = message.split(/\\s+/).length;\nconst hasContext = knowledgeContext.length > 0;\nconst complexity = wordCount > 50 || scores.multiAgent > 0 ? 'complex' : 'simple';\n\n// Select primary agent\nlet selectedAgent = 'Fred'; // Default\nlet agentReason = 'General technical queries';\n\nif (scores.multiAgent > 0 || complexity === 'complex') {\n selectedAgent = 'Multi-Agent';\n agentReason = 'Complex query requiring multiple perspectives';\n} else if (scores.technical >= 2) {\n selectedAgent = 'Fred';\n agentReason = 'Technical implementation focus';\n} else if (scores.consulting >= 2) {\n selectedAgent = 'Claude';\n agentReason = 'Strategic planning and architecture';\n} else if (scores.business >= 2) {\n selectedAgent = 'Colin';\n agentReason = 'Business strategy and ROI analysis';\n} else if (scores.research >= 2) {\n selectedAgent = 'Penny';\n agentReason = 'Research and latest information needed';\n}\n\nreturn {\n selectedAgent,\n agentReason,\n complexity,\n scores,\n requiresMultiAgent: selectedAgent === 'Multi-Agent',\n contextAvailable: hasContext,\n relevantKnowledge: knowledgeContext\n};"
},
"id": "analyze-query",
"name": "Determine Agent & Complexity",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
850,
300
]
},
{
"parameters": {
"rules": {
"rules": [
{
"operation": "equal",
"value1": "={{ $json.selectedAgent }}",
"value2": "Fred"
},
{
"operation": "equal",
"value1": "={{ $json.selectedAgent }}",
"value2": "Claude"
},
{
"operation": "equal",
"value1": "={{ $json.selectedAgent }}",
"value2": "Colin"
},
{
"operation": "equal",
"value1": "={{ $json.selectedAgent }}",
"value2": "Penny"
},
{
"operation": "equal",
"value1": "={{ $json.selectedAgent }}",
"value2": "Multi-Agent"
}
]
}
},
"id": "route-to-agent",
"name": "Route to Agent",
"type": "n8n-nodes-base.switch",
"typeVersion": 3,
"position": [
1050,
300
]
},
{
"parameters": {
"url": "https://forge.manus.im/v1/chat/completions",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "httpHeaderAuth",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "model",
"value": "gemini-2.5-flash"
},
{
"name": "max_tokens",
"value": 4096
}
]
},
"options": {}
},
"id": "fred-gemini",
"name": "Fred - Gemini",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
1250,
100
],
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"url": "https://api.anthropic.com/v1/messages",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "anthropicApi",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "anthropic-version",
"value": "2023-06-01"
}
]
},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "model",
"value": "claude-sonnet-4-5-20250929"
},
{
"name": "max_tokens",
"value": 8192
}
]
},
"options": {}
},
"id": "claude-anthropic",
"name": "Claude - Anthropic",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
1250,
200
],
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"url": "https://api.openai.com/v1/chat/completions",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "openAiApi",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "model",
"value": "gpt-4-turbo-preview"
},
{
"name": "max_tokens",
"value": 4096
}
]
},
"options": {}
},
"id": "colin-openai",
"name": "Colin - OpenAI",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
1250,
300
],
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"url": "https://api.perplexity.ai/chat/completions",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "httpHeaderAuth",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "model",
"value": "llama-3.1-sonar-large-128k-online"
},
{
"name": "max_tokens",
"value": 4096
}
]
},
"options": {}
},
"id": "penny-perplexity",
"name": "Penny - Perplexity",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
1250,
400
],
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "// Multi-agent orchestration\nconst userMessage = $input.item.json.userMessage;\n\nreturn {\n multiAgentMode: true,\n message: 'Complex query detected - consulting AAE Council. This will require sequential agent consultation.',\n agent: 'Multi-Agent',\n agentType: 'AAE Council',\n note: 'For MVP, route to Claude for comprehensive analysis. Phase 2 will implement true multi-agent orchestration.'\n};"
},
"id": "multi-agent-orchestrator",
"name": "Multi-Agent Orchestrator",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1250,
500
],
"notes": "Phase 2: Implement sequential agent consultation"
},
{
"parameters": {
"jsCode": "// Extract entities from AI response\nconst agentResponse = $input.item.json.message || $input.item.json.content?.[0]?.text || $input.item.json.choices?.[0]?.message?.content || '';\nconst userMessage = $node['Initialize Variables'].json.userMessage;\nconst agent = $input.item.json.agent || 'Unknown';\n\n// Entity extraction patterns\nconst patterns = {\n Agents: /\\b(Claude|Fred|Colin|Penny|Gemini|GPT-4|Perplexity|Manus|n8n|Zapier)\\b/gi,\n Technology: /\\b(Railway|Cloudflare|PostgreSQL|mem0|tRPC|React|Node\\.js|Docker|Git|API|database|webhook|Notion|GitHub)\\b/gi,\n ExecutiveAI: /\\b(AI strategy|AI budget|AI governance|automation strategy|LLM optimization|AI ROI)\\b/gi,\n Consulting: /\\b(workflow design|system architecture|implementation plan|best practices|optimization|strategy)\\b/gi\n};\n\nconst entities = [];\nconst seen = new Set();\nconst fullText = `${userMessage}\\n\\n${agentResponse}`;\n\nfor (const [entityType, pattern] of Object.entries(patterns)) {\n const matches = fullText.match(pattern) || [];\n \n matches.forEach(match => {\n const normalized = match.trim().toLowerCase();\n if (!seen.has(normalized)) {\n seen.add(normalized);\n const matchIndex = fullText.indexOf(match);\n entities.push({\n name: match.trim(),\n entityType,\n confidence: 0.85,\n description: `Discussed in ${agent} conversation`,\n sourceContext: fullText.substring(Math.max(0, matchIndex - 50), Math.min(fullText.length, matchIndex + 50))\n });\n }\n });\n}\n\n// Infer relationships\nconst relationships = [];\nif (entities.length >= 2) {\n const agentEntities = entities.filter(e => e.entityType === 'Agents');\n const techEntities = entities.filter(e => e.entityType === 'Technology');\n \n agentEntities.forEach(agentEntity => {\n techEntities.forEach(techEntity => {\n relationships.push({\n from: agentEntity.name,\n to: techEntity.name,\n relationshipType: 'discusses',\n weight: 5,\n confidence: 0.80\n });\n });\n });\n}\n\nreturn {\n entities,\n relationships,\n extractedCount: entities.length,\n relationshipCount: relationships.length,\n agent: $input.item.json.agent,\n message: agentResponse\n};"
},
"id": "extract-entities",
"name": "Extract Entities",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1450,
300
]
},
{
"parameters": {
"url": "={{ $node['Initialize Variables'].json.knowledgeLakeUrl }}/api/conversations/ingest",
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "userId",
"value": "={{ $node['Initialize Variables'].json.userId }}"
},
{
"name": "agent",
"value": "={{ $json.agent }}"
},
{
"name": "date",
"value": "={{ new Date().toISOString().split('T')[0] }}"
},
{
"name": "topic",
"value": "=Dashboard Chat: {{ $node['Initialize Variables'].json.userMessage.substring(0, 100) }}"
},
{
"name": "content",
"value": "=User: {{ $node['Initialize Variables'].json.userMessage }}\\n\\n{{ $json.agent }}: {{ $json.message }}"
}
]
},
"options": {
"retry": {
"maxRetries": 3,
"waitBetweenTries": 1000
}
}
},
"id": "ingest-to-knowledge-lake",
"name": "Ingest to Knowledge Lake",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [
1650,
300
],
"continueOnFail": true
},
{
"parameters": {
"values": {
"boolean": [
{
"name": "success",
"value": true
},
{
"name": "ingested",
"value": "={{ $node['Ingest to Knowledge Lake'].json.success || false }}"
}
],
"string": [
{
"name": "agent",
"value": "={{ $node['Extract Entities'].json.agent }}"
},
{
"name": "agentReason",
"value": "={{ $node['Determine Agent & Complexity'].json.agentReason }}"
},
{
"name": "message",
"value": "={{ $node['Extract Entities'].json.message }}"
},
{
"name": "timestamp",
"value": "={{ $node['Initialize Variables'].json.timestamp }}"
}
],
"number": [
{
"name": "entitiesCount",
"value": "={{ $node['Extract Entities'].json.extractedCount }}"
},
{
"name": "conversationId",
"value": "={{ $node['Ingest to Knowledge Lake'].json.conversation?.id || 0 }}"
}
],
"array": [
{
"name": "entities",
"value": "={{ $node['Extract Entities'].json.entities }}"
}
]
},
"options": {}
},
"id": "format-response",
"name": "Format Response",
"type": "n8n-nodes-base.set",
"typeVersion": 3,
"position": [
1850,
300
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ $json }}"
},
"id": "webhook-response",
"name": "Webhook Response",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1,
"position": [
2050,
300
]
}
],
"connections": {
"AAE Chat Webhook": {
"main": [
[
{
"node": "Initialize Variables",
"type": "main",
"index": 0
}
]
]
},
"Initialize Variables": {
"main": [
[
{
"node": "Query Knowledge Lake",
"type": "main",
"index": 0
}
]
]
},
"Query Knowledge Lake": {
"main": [
[
{
"node": "Determine Agent & Complexity",
"type": "main",
"index": 0
}
]
]
},
"Determine Agent & Complexity": {
"main": [
[
{
"node": "Route to Agent",
"type": "main",
"index": 0
}
]
]
},
"Route to Agent": {
"main": [
[
{
"node": "Fred - Gemini",
"type": "main",
"index": 0
}
],
[
{
"node": "Claude - Anthropic",
"type": "main",
"index": 0
}
],
[
{
"node": "Colin - OpenAI",
"type": "main",
"index": 0
}
],
[
{
"node": "Penny - Perplexity",
"type": "main",
"index": 0
}
],
[
{
"node": "Multi-Agent Orchestrator",
"type": "main",
"index": 0
}
]
]
},
"Fred - Gemini": {
"main": [
[
{
"node": "Extract Entities",
"type": "main",
"index": 0
}
]
]
},
"Claude - Anthropic": {
"main": [
[
{
"node": "Extract Entities",
"type": "main",
"index": 0
}
]
]
},
"Colin - OpenAI": {
"main": [
[
{
"node": "Extract Entities",
"type": "main",
"index": 0
}
]
]
},
"Penny - Perplexity": {
"main": [
[
{
"node": "Extract Entities",
"type": "main",
"index": 0
}
]
]
},
"Multi-Agent Orchestrator": {
"main": [
[
{
"node": "Extract Entities",
"type": "main",
"index": 0
}
]
]
},
"Extract Entities": {
"main": [
[
{
"node": "Ingest to Knowledge Lake",
"type": "main",
"index": 0
}
]
]
},
"Ingest to Knowledge Lake": {
"main": [
[
{
"node": "Format Response",
"type": "main",
"index": 0
}
]
]
},
"Format Response": {
"main": [
[
{
"node": "Webhook Response",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
},
"staticData": null,
"tags": [
{
"createdAt": "2025-12-24T00:00:00.000Z",
"updatedAt": "2025-12-24T00:00:00.000Z",
"id": "1",
"name": "AAE"
},
{
"createdAt": "2025-12-24T00:00:00.000Z",
"updatedAt": "2025-12-24T00:00:00.000Z",
"id": "2",
"name": "AI Agents"
}
],
"triggerCount": 1,
"updatedAt": "2025-12-24T00:00:00.000Z",
"versionId": "1"
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
anthropicApihttpHeaderAuthopenAiApi
Pro
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About this workflow
AAE Agent Router. Uses httpRequest. Webhook trigger; 14 nodes.
Source: https://github.com/carlorbiz/aae-dashboard-archive-2026q1/blob/b9e0c98ab1fc8ce05e21351b2a92cc7b22856562/n8n-workflows/AAE_Agent_Router.json — original creator credit. Request a take-down →
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