多AI代理路由器：通过Webhook比较OpenAI、Anthropic和Groq的响应
高级
这是一个Engineering、AI Summarization领域的自动化工作流，包含 18 个节点。主要使用 Set、Code、Merge、Switch、Webhook 等节点。 多AI代理路由器：通过Webhook比较OpenAI、Anthropic和Groq的响应
前置要求
- •HTTP Webhook 端点（n8n 会自动生成）
- •OpenAI API Key
- •Anthropic API Key
使用的节点 (18 个)
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "8inUrLV1lxLoNc7e",
"meta": {
"instanceId": "b91e510ebae4127f953fd2f5f8d40d58ca1e71c746d4500c12ae86aad04c1502",
"templateCredsSetupCompleted": true
},
"name": "多AI代理路由器：通过Webhook比较OpenAI、Anthropic和Groq的响应",
"tags": [],
"nodes": [
{
"id": "21041871-2233-477d-b2d3-2c63919050b5",
"name": "输出解析器",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-1008,
-656
],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "80b943cb-94b6-45fa-8f97-a3fd2adb4c1c",
"name": "输出解析器2",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-992,
-368
],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "b4ae807f-c736-4987-9cf9-32a75d90b3b1",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
-1808,
-800
],
"webhookId": "d842ef53-fd5a-41e7-8371-12d5d6dbac30",
"parameters": {
"path": "ai-pipeline",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 2
},
{
"id": "4236a8f9-1acf-4031-a82b-502dd80831cd",
"name": "提取输入参数",
"type": "n8n-nodes-base.set",
"position": [
-1648,
-800
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "input_data",
"name": "input_data",
"type": "string",
"value": "={{ $json.body.data }}"
},
{
"id": "task_type",
"name": "task_type",
"type": "string",
"value": "={{ $json.body.task_type || 'general' }}"
},
{
"id": "priority",
"name": "priority",
"type": "string",
"value": "={{ $json.body.priority || 'balanced' }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "5b6eeb1c-415c-462b-8d71-5c3b20989ed3",
"name": "动态LLM路由器",
"type": "n8n-nodes-base.code",
"position": [
-1456,
-800
],
"parameters": {
"jsCode": "// Dynamic LLM routing logic based on cost/performance\nconst inputData = $input.item.json.input_data;\nconst taskType = $input.item.json.task_type;\nconst priority = $input.item.json.priority;\n\n// Calculate complexity score\nconst dataLength = inputData.length;\nlet complexityScore = 0;\n\nif (dataLength < 500) complexityScore = 1;\nelse if (dataLength < 2000) complexityScore = 2;\nelse complexityScore = 3;\n\n// Routing decision matrix\nlet selectedProvider = '';\nlet modelName = '';\nlet estimatedCost = 0;\nlet expectedQuality = 0;\n\nif (priority === 'cost') {\n // Prioritize low-cost providers\n if (complexityScore === 1) {\n selectedProvider = 'groq';\n modelName = 'llama-3.1-8b-instant';\n estimatedCost = 0.0001;\n expectedQuality = 7;\n } else if (complexityScore === 2) {\n selectedProvider = 'ollama';\n modelName = 'llama3';\n estimatedCost = 0;\n expectedQuality = 6;\n } else {\n selectedProvider = 'groq';\n modelName = 'llama-3.1-70b-versatile';\n estimatedCost = 0.0005;\n expectedQuality = 8;\n }\n} else if (priority === 'performance') {\n // Prioritize high-quality providers\n if (complexityScore === 1) {\n selectedProvider = 'openai';\n modelName = 'gpt-4o-mini';\n estimatedCost = 0.002;\n expectedQuality = 9;\n } else if (complexityScore === 2) {\n selectedProvider = 'anthropic';\n modelName = 'claude-3-5-sonnet-20241022';\n estimatedCost = 0.015;\n expectedQuality = 10;\n } else {\n selectedProvider = 'openai';\n modelName = 'gpt-4o';\n estimatedCost = 0.025;\n expectedQuality = 10;\n }\n} else {\n // Balanced approach\n if (complexityScore === 1) {\n selectedProvider = 'groq';\n modelName = 'llama-3.1-8b-instant';\n estimatedCost = 0.0001;\n expectedQuality = 7;\n } else if (complexityScore === 2) {\n selectedProvider = 'openai';\n modelName = 'gpt-4o-mini';\n estimatedCost = 0.002;\n expectedQuality = 9;\n } else {\n selectedProvider = 'anthropic';\n modelName = 'claude-3-5-sonnet-20241022';\n estimatedCost = 0.015;\n expectedQuality = 10;\n }\n}\n\nreturn {\n input_data: inputData,\n task_type: taskType,\n priority: priority,\n routing_decision: {\n provider: selectedProvider,\n model: modelName,\n complexity_score: complexityScore,\n estimated_cost: estimatedCost,\n expected_quality: expectedQuality\n },\n timestamp: new Date().toISOString()\n};"
},
"typeVersion": 2
},
{
"id": "c1042d78-0f4b-4dab-97f1-c080df3ab8bc",
"name": "路由到提供商",
"type": "n8n-nodes-base.switch",
"position": [
-1328,
-688
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "openai",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "1584bb13-fed5-457e-a871-99a0d3a81fbf",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.routing_decision.provider }}",
"rightValue": "openai"
}
]
},
"renameOutput": true
},
{
"outputKey": "anthropic",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "10edb531-ce74-4bf2-b795-a80fb0f80049",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.routing_decision.provider }}",
"rightValue": "anthropic"
}
]
},
"renameOutput": true
},
{
"outputKey": "groq",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "961d531e-d1e4-46ba-bd76-44a4999b0653",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.routing_decision.provider }}",
"rightValue": "groq"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "6c88c121-40c7-4313-8697-300dde7e5664",
"name": "OpenAI代理",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-1152,
-1056
],
"parameters": {
"text": "={{ \"You are a data enrichment AI assistant. Analyze and enrich the following data with insights, structure it properly, and provide actionable recommendations.\\n\\nTask Type: \" + $json.task_type + \"\\n\\nInput Data:\\n\" + $json.input_data + \"\\n\\nProvide:\\n1. Structured analysis\\n2. Key insights\\n3. Data enrichment\\n4. Actionable recommendations\\n5. Quality score (1-10)\\n\\nFormat as JSON.\" }}",
"options": {
"systemMessage": "=You are processing data with {{ $json.routing_decision.provider }} ({{ $json.routing_decision.model }}). Quality level: {{ $json.routing_decision.expected_quality }}/10."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.7
},
{
"id": "278920ea-7712-4f58-9274-1b9869daa368",
"name": "Anthropic代理",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-1152,
-800
],
"parameters": {
"text": "={{ \"You are a data enrichment AI assistant. Analyze and enrich the following data with insights, structure it properly, and provide actionable recommendations.\\n\\nTask Type: \" + $json.task_type + \"\\n\\nInput Data:\\n\" + $json.input_data + \"\\n\\nProvide:\\n1. Structured analysis\\n2. Key insights\\n3. Data enrichment\\n4. Actionable recommendations\\n5. Quality score (1-10)\\n\\nFormat as JSON.\" }}",
"options": {
"systemMessage": "=You are processing data with {{ $json.routing_decision.provider }} ({{ $json.routing_decision.model }}). Quality level: {{ $json.routing_decision.expected_quality }}/10."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.7
},
{
"id": "09f9bc3c-184b-4865-ace9-b84518a15437",
"name": "Groq代理",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-1136,
-528
],
"parameters": {
"text": "={{ \"You are a data enrichment AI assistant. Analyze and enrich the following data with insights, structure it properly, and provide actionable recommendations.\\n\\nTask Type: \" + $json.task_type + \"\\n\\nInput Data:\\n\" + $json.input_data + \"\\n\\nProvide:\\n1. Structured analysis\\n2. Key insights\\n3. Data enrichment\\n4. Actionable recommendations\\n5. Quality score (1-10)\\n\\nFormat as JSON.\" }}",
"options": {
"systemMessage": "=You are processing data with {{ $json.routing_decision.provider }} ({{ $json.routing_decision.model }}). Quality level: {{ $json.routing_decision.expected_quality }}/10."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.7
},
{
"id": "5efa962a-2f6b-4834-8c0d-02452ff92233",
"name": "合并结果",
"type": "n8n-nodes-base.merge",
"position": [
-784,
-784
],
"parameters": {
"mode": "combine",
"options": {}
},
"typeVersion": 3
},
{
"id": "340f1fbc-31e7-44af-9649-52ea7de91418",
"name": "计算性能指标",
"type": "n8n-nodes-base.code",
"position": [
-624,
-784
],
"parameters": {
"jsCode": "// Calculate actual performance metrics\nconst startTime = new Date($input.first().json.timestamp).getTime();\nconst endTime = Date.now();\nconst processingTime = endTime - startTime;\n\nconst routingDecision = $input.first().json.routing_decision;\nconst aiResponse = $input.all()[1].json;\n\n// Extract quality score from AI response\nlet actualQuality = 0;\ntry {\n actualQuality = aiResponse.quality_score || routingDecision.expected_quality;\n} catch (e) {\n actualQuality = routingDecision.expected_quality;\n}\n\n// Performance evaluation\nconst costEfficiency = (actualQuality / routingDecision.estimated_cost).toFixed(2);\nconst performanceScore = ((actualQuality / 10) * 0.7 + (1 / (processingTime / 1000)) * 0.3).toFixed(2);\n\nreturn {\n original_input: $input.first().json.input_data,\n task_type: $input.first().json.task_type,\n priority: $input.first().json.priority,\n routing_decision: routingDecision,\n enriched_data: aiResponse,\n performance_metrics: {\n processing_time_ms: processingTime,\n actual_quality_score: actualQuality,\n expected_quality_score: routingDecision.expected_quality,\n estimated_cost: routingDecision.estimated_cost,\n cost_efficiency: parseFloat(costEfficiency),\n performance_score: parseFloat(performanceScore),\n provider_used: routingDecision.provider,\n model_used: routingDecision.model\n },\n timestamp: new Date().toISOString()\n};"
},
"typeVersion": 2
},
{
"id": "9e130394-0a3c-42de-a702-1b010e15ff8c",
"name": "响应Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
-448,
-784
],
"parameters": {
"options": {
"responseCode": 200,
"responseHeaders": {
"entries": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"respondWith": "allIncomingItems"
},
"typeVersion": 1.1
},
{
"id": "092da29d-efe6-4408-be28-c4360e94c8d3",
"name": "OpenAI模型",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-1168,
-912
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "OGYj7DgYv5GFLFZk",
"name": "OpenAi account 2"
}
},
"typeVersion": 1
},
{
"id": "9b471188-3126-4bd3-a61f-d0498f00ea3c",
"name": "输出解析器1",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-1008,
-912
],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "58b782f0-871d-450a-92a5-a8e27163a652",
"name": "Groq模型",
"type": "@n8n/n8n-nodes-langchain.lmChatGroq",
"position": [
-1136,
-368
],
"parameters": {
"model": "llama-3.1-70b-versatile",
"options": {}
},
"typeVersion": 1
},
{
"id": "202e4932-082a-4dd2-b3de-c6859d622055",
"name": "Anthropic模型",
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"position": [
-1152,
-656
],
"parameters": {
"model": "claude-3-5-sonnet-20241022",
"options": {}
},
"credentials": {
"anthropicApi": {
"id": "S8laStQPC1u3EYuZ",
"name": "Anthropic account"
}
},
"typeVersion": 1
},
{
"id": "16f3a535-b740-418c-8090-b5b9fcdde583",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2352,
-1104
],
"parameters": {
"width": 512,
"height": 576,
"content": "## 简介"
},
"typeVersion": 1
},
{
"id": "6bebceb0-7370-469d-8c94-9d54d0d33ceb",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1808,
-640
],
"parameters": {
"color": 5,
"width": 400,
"height": 464,
"content": "## 设置说明"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "61425eed-0f21-4b21-a355-ef9669810241",
"connections": {
"Webhook": {
"main": [
[
{
"node": "Extract Input Parameters",
"type": "main",
"index": 0
}
]
]
},
"Groq Agent": {
"main": [
[
{
"node": "Merge Results",
"type": "main",
"index": 1
}
]
]
},
"Groq Model": {
"ai_languageModel": [
[
{
"node": "Groq Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenAI Agent": {
"main": [
[
{
"node": "Merge Results",
"type": "main",
"index": 1
}
]
]
},
"OpenAI Model": {
"ai_languageModel": [
[
{
"node": "OpenAI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Merge Results": {
"main": [
[
{
"node": "Calculate Performance Metrics",
"type": "main",
"index": 0
}
]
]
},
"Output Parser": {
"ai_outputParser": [
[
{
"node": "Anthropic Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Output Parser1": {
"ai_outputParser": [
[
{
"node": "OpenAI Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Output Parser2": {
"ai_outputParser": [
[
{
"node": "Groq Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Anthropic Agent": {
"main": [
[
{
"node": "Merge Results",
"type": "main",
"index": 1
}
]
]
},
"Anthropic Model": {
"ai_languageModel": [
[
{
"node": "Anthropic Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Route to Provider": {
"main": [
[
{
"node": "OpenAI Agent",
"type": "main",
"index": 0
}
],
[
{
"node": "Anthropic Agent",
"type": "main",
"index": 0
}
],
[
{
"node": "Groq Agent",
"type": "main",
"index": 0
}
]
]
},
"Dynamic LLM Router": {
"main": [
[
{
"node": "Route to Provider",
"type": "main",
"index": 0
},
{
"node": "Merge Results",
"type": "main",
"index": 0
}
]
]
},
"Extract Input Parameters": {
"main": [
[
{
"node": "Dynamic LLM Router",
"type": "main",
"index": 0
}
]
]
},
"Calculate Performance Metrics": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
这是一个高级难度的工作流，适用于Engineering、AI Summarization等场景。适合高级用户，包含 16+ 个节点的复杂工作流
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
高级
节点数量18
分类2
节点类型12
作者
Cheng Siong Chin
@cschinProf. Cheng Siong CHIN serves as Chair Professor in Intelligent Systems Modelling and Simulation in Newcastle University, Singapore. His academic credentials include an M.Sc. in Advanced Control and Systems Engineering from The University of Manchester and a Ph.D. in Robotics from Nanyang Technological University.
外部链接
在 n8n.io 上查看 →
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