{"workflow":{"id":13966,"name":"Route AI queries cost‑efficiently with GPT‑4o‑mini, GPT‑4o and confidence scoring","views":21,"recentViews":0,"totalViews":21,"createdAt":"2026-03-09T14:52:48.507Z","description":"\n\nThis workflow implements a **cost-optimized AI routing system** using n8n. It intelligently decides whether a request should be handled by a **low-cost model** or escalated to a **higher-quality model** based on response confidence.\n\nThe goal is to **minimize LLM usage costs while maintaining high answer quality**.\n\nA query is first processed by a cheaper model. The response is then evaluated by a confidence-scoring AI agent. If the response quality is insufficient, the workflow automatically escalates the request to a more capable model.\n\nThis approach is useful for building scalable AI systems where **most queries can be answered cheaply**, while complex queries still receive high-quality responses.\n\n---\n\n## How It Works\n\n1. **Webhook Trigger**\n   - Receives a user query from an external application.\n\n2. **Workflow Configuration**\n   - Defines parameters such as:\n     - confidence threshold\n     - cheap model cost\n     - expensive model cost\n\n3. **Cheap Model Response**\n   - The query is first processed using `GPT-4o-mini` to minimize cost.\n\n4. **Confidence Evaluation**\n   - An AI agent analyzes the response quality.\n   - It evaluates accuracy, completeness, clarity, and relevance.\n\n5. **Structured Output Parsing**\n   - The evaluator returns structured data including:\n     - confidence score\n     - explanation\n     - escalation recommendation.\n\n6. **Decision Logic**\n   - If the confidence score is below the configured threshold, the workflow escalates the request.\n\n7. **Expensive Model Escalation**\n   - The query is reprocessed using `GPT-4o` for a higher-quality answer.\n\n8. **Cost Calculation**\n   - Token usage is analyzed to estimate:\n     - total cost\n     - cost difference between models.\n\n9. **Final Response Formatting**\n   - The workflow returns:\n     - AI response\n     - model used\n     - confidence score\n     - escalation status\n     - estimated cost.\n\n---\n\n## Setup Instructions\n\n1. Create an **OpenAI credential** in n8n.\n\n2. Configure the following nodes:\n   - `Cheap Model (GPT-4o-mini)`\n   - `Expensive Model (GPT-4o)`\n   - `OpenAI Chat Model` used by the confidence evaluator agent.\n\n3. Adjust configuration values in the **Workflow Configuration node**:\n   - `confidenceThreshold`\n   - `cheapModelCostPer1kTokens`\n   - `expensiveModelCostPer1kTokens`\n\n4. Deploy the workflow and send requests to the **Webhook URL**.\n\nExample webhook payload:\n\n```json\n{\n  \"query\": \"Explain how photosynthesis works.\"\n}","workflow":{"meta":{"instanceId":"48aac30adfc5487a33ef16e0e958096f27cef40df3ed0febcbe1ca199e789786"},"nodes":[{"id":"b316dd7b-3d2f-4365-a0f7-3389caaa6263","name":"Webhook Trigger","type":"n8n-nodes-base.webhook","position":[-1392,80],"webhookId":"5b4bf456-6536-4d2e-b505-80e58201a458","parameters":{"path":"5b4bf456-6536-4d2e-b505-80e58201a458","options":{},"httpMethod":"POST"},"typeVersion":2.1},{"id":"48c43fc1-86dc-4454-a6ce-2cfa581f3e7b","name":"Workflow Configuration","type":"n8n-nodes-base.set","position":[-1120,80],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"confidenceThreshold","type":"number","value":0.7},{"id":"id-2","name":"cheapModelCostPer1kTokens","type":"number","value":0.00015},{"id":"id-3","name":"expensiveModelCostPer1kTokens","type":"number","value":0.005}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"fc83230f-9772-4b50-835e-b394760bed2e","name":"Check Confidence Threshold","type":"n8n-nodes-base.if","position":[-224,80],"parameters":{"options":{},"conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"loose"},"combinator":"and","conditions":[{"id":"id-1","operator":{"type":"boolean","operation":"true"},"leftValue":"={{ $json.should_escalate }}"}]}},"typeVersion":2.3},{"id":"882dcd8e-b574-4cd3-8ff2-762a2a40533c","name":"Calculate Cost Difference","type":"n8n-nodes-base.code","position":[400,96],"parameters":{"mode":"runOnceForEachItem","jsCode":"// Get data from previous nodes\nconst confidenceData = $('Confidence Evaluator').item.json;\nconst configData = $('Workflow Configuration').first().json;\nconst cheapModelData = $('Cheap Model (GPT-5-mini)').item.json;\n\n// Check if request was escalated\nconst wasEscalated = confidenceData.confidence < configData.confidence_threshold;\n\n// Get cost rates from configuration\nconst cheapModelCostPer1kTokens = configData.cheap_model_cost_per_1k_tokens || 0.00015;\nconst expensiveModelCostPer1kTokens = configData.expensive_model_cost_per_1k_tokens || 0.005;\n\nlet modelUsed, totalTokens, costUsd, costDifferenceUsd;\n\nif (wasEscalated) {\n  // Request was escalated to expensive model\n  const expensiveModelData = $('Expensive Model (GPT-5.4)').item.json;\n  modelUsed = 'gpt-4o';\n  totalTokens = expensiveModelData.usage?.total_tokens || 0;\n  costUsd = (totalTokens / 1000) * expensiveModelCostPer1kTokens;\n  \n  // Calculate what it would have cost with cheap model\n  const cheapModelTokens = cheapModelData.usage?.total_tokens || 0;\n  const cheapModelCost = (cheapModelTokens / 1000) * cheapModelCostPer1kTokens;\n  \n  // Additional cost due to escalation (positive number)\n  costDifferenceUsd = costUsd - cheapModelCost;\n} else {\n  // Request stayed with cheap model\n  modelUsed = 'gpt-4o-mini';\n  totalTokens = cheapModelData.usage?.total_tokens || 0;\n  costUsd = (totalTokens / 1000) * cheapModelCostPer1kTokens;\n  \n  // Calculate what it would have cost with expensive model\n  const expensiveModelCost = (totalTokens / 1000) * expensiveModelCostPer1kTokens;\n  \n  // Cost savings (negative number indicates savings)\n  costDifferenceUsd = costUsd - expensiveModelCost;\n}\n\n// Return the result\nreturn {\n  model_used: modelUsed,\n  total_tokens: totalTokens,\n  cost_usd: parseFloat(costUsd.toFixed(6)),\n  escalated: wasEscalated,\n  cost_difference_usd: parseFloat(costDifferenceUsd.toFixed(6)),\n  response: wasEscalated ? $('Expensive Model (GPT-5.4)').item.json.message?.content : cheapModelData.message?.content,\n  confidence_score: confidenceData.confidence\n};"},"typeVersion":2},{"id":"095bccdd-3b90-424b-909e-122fe400dce1","name":"Format Final Response","type":"n8n-nodes-base.set","position":[672,96],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"response","type":"string","value":"={{ $('Expensive Model (GPT-5.4)').item.json.message?.content || $('Cheap Model (GPT-5-mini)').item.json.message?.content }}"},{"id":"id-2","name":"model_used","type":"string","value":"={{ $('Calculate Cost Difference').item.json.model_used }}"},{"id":"id-3","name":"confidence_score","type":"number","value":"={{ $('Confidence Evaluator').item.json.output?.confidence_score }}"},{"id":"id-4","name":"escalated","type":"boolean","value":"={{ $('Calculate Cost Difference').item.json.escalated }}"},{"id":"id-5","name":"cost_usd","type":"number","value":"={{ $('Calculate Cost Difference').item.json.cost_usd }}"},{"id":"id-6","name":"cost_analysis","type":"object","value":"={{ JSON.stringify($('Calculate Cost Difference').item.json.cost_analysis) }}"}]}},"typeVersion":3.4},{"id":"35c1daed-bcfb-41f6-9276-4dc6652e9205","name":"Confidence Evaluator","type":"@n8n/n8n-nodes-langchain.agent","position":[-592,80],"parameters":{"text":"=Original query: {{ $('Workflow Configuration').first().json.query }}\n\nCheap model response: {{ $json.message.content }}","options":{"systemMessage":"You are a confidence evaluator. Analyze the quality and completeness of the AI response provided.\n\nEvaluate based on:\n1. Accuracy and correctness\n2. Completeness of the answer\n3. Clarity and coherence\n4. Relevance to the query\n\nReturn a confidence score between 0 and 1, where:\n- 0.9-1.0: Excellent response, highly confident\n- 0.7-0.89: Good response, confident\n- 0.5-0.69: Acceptable but could be better\n- Below 0.5: Poor response, needs improvement\n\nProvide a brief reason for your assessment and indicate whether escalation to a better model is recommended."},"promptType":"define","hasOutputParser":true},"typeVersion":3},{"id":"0338bd7b-58e9-4577-8288-d7bb4fcb03ca","name":"Parse Confidence JSON","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[-384,320],"parameters":{"jsonSchemaExample":"{\n\t\"confidence_score\": 0.85,\n\t\"reason\": \"Brief explanation of the confidence assessment\",\n\t\"should_escalate\": false\n}"},"typeVersion":1.3},{"id":"367f6833-da36-4b37-9b12-6cfb16ee9e93","name":"OpenAI Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[-624,320],"parameters":{"model":{"__rl":true,"mode":"id","value":"gpt-4o-mini"},"options":{},"builtInTools":{}},"typeVersion":1.3},{"id":"d5329d77-80d1-433d-bfe6-3a13b32db8c0","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-320,-64],"parameters":{"color":7,"width":272,"height":320,"content":"## Check Confidence Threshold\nDecision node that determines whether the response quality is sufficient."},"typeVersion":1},{"id":"86941910-a70c-4a2d-a5a2-fc6d5439b4e6","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-2032,-96],"parameters":{"width":464,"height":496,"content":"## Cost-Optimized AI Model Routing\n\nThis workflow demonstrates a cost-aware AI routing strategy that balances response quality and API cost. Instead of always using an expensive model, the workflow first generates a response using a lower-cost AI model. A confidence evaluator then reviews the response quality.\n\nThe workflow also estimates token usage and cost differences, helping measure the savings achieved through cost-optimized AI routing.\n\n### How it works\n\nWebhook Trigger receives a user query.\n\nA cheap AI model generates the initial response.\n\nA confidence evaluator agent analyzes the response quality.\n\nIf confidence is low, the request is escalated to a stronger model.\n\nThe workflow calculates token usage and cost difference.\n\nThe final response and cost analysis are returned. "},"typeVersion":1},{"id":"4ccfe423-62d7-4a2f-b079-0cae59970413","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-16,-128],"parameters":{"color":7,"width":288,"height":384,"content":"## Expensive Model (GPT-5.4)\n\nHandles queries that require higher quality reasoning.\nTriggered only when the confidence score is below the threshold."},"typeVersion":1},{"id":"9b4e4e10-5251-4cc9-abde-54133883f307","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[336,-128],"parameters":{"color":7,"width":224,"height":384,"content":"## Calculate Cost Difference\n\nCalculates token usage and estimated cost of the request.\nDetermines which model was used and compares costs between the cheap and expensive models."},"typeVersion":1},{"id":"a3b8794a-523b-4961-bfbb-56b2f373144d","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-624,-80],"parameters":{"color":7,"width":272,"height":304,"content":"## Confidence Evaluator\nAnalyzes the cheap model’s response to determine its quality.\nEvaluates accuracy, completeness, clarity, and relevance."},"typeVersion":1},{"id":"c3b6e1ff-507e-4b84-b6bf-5053e749ecaa","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[-896,-128],"parameters":{"color":7,"height":352,"content":"## Cheap Model (GPT-5-mini)\n\nProcesses the user query using a low-cost AI model.\nThis step provides an initial response while minimizing token costs."},"typeVersion":1},{"id":"dd2cc699-9b00-4e96-b932-1732e7eea5e4","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[-1184,-128],"parameters":{"color":7,"height":368,"content":"## Workflow Configuration\n\nDefines key configuration settings used across the workflow.\nIncludes the confidence threshold and token pricing"},"typeVersion":1},{"id":"a02448ce-02ae-41bf-8e4f-98c983883ff8","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[592,-128],"parameters":{"color":7,"height":384,"content":"## Format Final Response\n\nPrepares the final structured output returned by the workflow."},"typeVersion":1},{"id":"53f9248c-0c2e-4481-9617-b0102edae917","name":"Sticky Note10","type":"n8n-nodes-base.stickyNote","position":[-1456,-96],"parameters":{"color":7,"width":256,"height":336,"content":"## Webhook Trigger\n\nStarts the workflow when an external application sends a request.\n"},"typeVersion":1},{"id":"21d21b1c-5bf3-4ef1-a424-bb89dd282a84","name":"Cheap Model (GPT-5-mini)","type":"@n8n/n8n-nodes-langchain.openAi","position":[-880,80],"parameters":{"modelId":{"mode":"id","value":"gpt-4o-mini"},"options":{},"responses":{"values":[{"content":"={{ $json.query }}"}]},"builtInTools":{}},"typeVersion":2.1},{"id":"041cee60-6892-4324-a259-fb528479adf9","name":"Expensive Model (GPT-5.4)","type":"@n8n/n8n-nodes-langchain.openAi","position":[0,32],"parameters":{"modelId":{"mode":"id","value":"gpt-4o"},"options":{},"responses":{"values":[{"content":"={{ $('Workflow Configuration').first().json.query }}"}]},"builtInTools":{}},"typeVersion":2.1}],"pinData":{},"connections":{"Webhook Trigger":{"main":[[{"node":"Workflow Configuration","type":"main","index":0}]]},"OpenAI Chat Model":{"ai_languageModel":[[{"node":"Confidence Evaluator","type":"ai_languageModel","index":0}]]},"Confidence Evaluator":{"main":[[{"node":"Check Confidence Threshold","type":"main","index":0}]]},"Parse Confidence JSON":{"ai_outputParser":[[{"node":"Confidence Evaluator","type":"ai_outputParser","index":0}]]},"Workflow Configuration":{"main":[[{"node":"Cheap Model (GPT-5-mini)","type":"main","index":0}]]},"Cheap Model (GPT-5-mini)":{"main":[[{"node":"Confidence Evaluator","type":"main","index":0}]]},"Calculate Cost Difference":{"main":[[{"node":"Format Final Response","type":"main","index":0}]]},"Expensive Model (GPT-5.4)":{"main":[[{"node":"Calculate Cost Difference","type":"main","index":0}]]},"Check Confidence Threshold":{"main":[[{"node":"Expensive Model (GPT-5.4)","type":"main","index":0}],[{"node":"Calculate Cost Difference","type":"main","index":0}]]}}},"lastUpdatedBy":1,"workflowInfo":{"nodeCount":19,"nodeTypes":{"n8n-nodes-base.if":{"count":1},"n8n-nodes-base.set":{"count":2},"n8n-nodes-base.code":{"count":1},"n8n-nodes-base.webhook":{"count":1},"n8n-nodes-base.stickyNote":{"count":9},"@n8n/n8n-nodes-langchain.agent":{"count":1},"@n8n/n8n-nodes-langchain.openAi":{"count":2},"@n8n/n8n-nodes-langchain.lmChatOpenAi":{"count":1},"@n8n/n8n-nodes-langchain.outputParserStructured":{"count":1}}},"status":"published","readyToDemo":null,"user":{"name":"ResilNext","username":"rnair1996","bio":"","verified":true,"links":[""],"avatar":"https://gravatar.com/avatar/c20bc6c3bcdf260fac3c28c556a8db661ee93670037a3ceb857e047851f6f438?r=pg&d=retro&size=200"},"nodes":[{"id":20,"icon":"fa:map-signs","name":"n8n-nodes-base.if","codex":{"data":{"alias":["Router","Filter","Condition","Logic","Boolean","Branch"],"details":"The IF node can be used to implement binary conditional logic in your workflow. You can set up one-to-many conditions to evaluate each item of data being inputted into the node. That data will either evaluate to TRUE or FALSE and route out of the node accordingly.\n\nThis node has multiple types of conditions: Bool, String, Number, and Date & Time.","resources":{"generic":[{"url":"https://n8n.io/blog/learn-to-automate-your-factorys-incident-reporting-a-step-by-step-guide/","icon":"🏭","label":"Learn to Automate Your Factory's Incident Reporting: A Step by Step Guide"},{"url":"https://n8n.io/blog/2021-the-year-to-automate-the-new-you-with-n8n/","icon":"☀️","label":"2021: The Year to Automate the New You with n8n"},{"url":"https://n8n.io/blog/why-business-process-automation-with-n8n-can-change-your-daily-life/","icon":"🧬","label":"Why business process automation with n8n can change your daily life"},{"url":"https://n8n.io/blog/create-a-toxic-language-detector-for-telegram/","icon":"🤬","label":"Create a toxic language detector for Telegram in 4 step"},{"url":"https://n8n.io/blog/no-code-ecommerce-workflow-automations/","icon":"store","label":"6 e-commerce workflows to power up your Shopify s"},{"url":"https://n8n.io/blog/how-to-build-a-low-code-self-hosted-url-shortener/","icon":"🔗","label":"How to build a low-code, self-hosted URL shortener in 3 steps"},{"url":"https://n8n.io/blog/automate-your-data-processing-pipeline-in-9-steps-with-n8n/","icon":"⚙️","label":"Automate your data processing pipeline in 9 steps"},{"url":"https://n8n.io/blog/how-to-get-started-with-crm-automation-and-no-code-workflow-ideas/","icon":"👥","label":"How to get started with CRM automation (with 3 no-code workflow ideas"},{"url":"https://n8n.io/blog/5-tasks-you-can-automate-with-notion-api/","icon":"⚡️","label":"5 tasks you can automate with the new Notion API "},{"url":"https://n8n.io/blog/automate-google-apps-for-productivity/","icon":"💡","label":"15 Google apps you can combine and automate to increase productivity"},{"url":"https://n8n.io/blog/automation-for-maintainers-of-open-source-projects/","icon":"🏷️","label":"How to automatically manage contributions to open-source projects"},{"url":"https://n8n.io/blog/how-uproc-scraped-a-multi-page-website-with-a-low-code-workflow/","icon":" 🕸️","label":"How uProc scraped a multi-page website with a low-code workflow"},{"url":"https://n8n.io/blog/5-workflow-automations-for-mattermost-that-we-love-at-n8n/","icon":"🤖","label":"5 workflow automations for Mattermost that we love at n8n"},{"url":"https://n8n.io/blog/why-this-product-manager-loves-workflow-automation-with-n8n/","icon":"🧠","label":"Why this Product Manager loves workflow automation with n8n"},{"url":"https://n8n.io/blog/sending-automated-congratulations-with-google-sheets-twilio-and-n8n/","icon":"🙌","label":"Sending Automated Congratulations with Google Sheets, Twilio, and n8n "},{"url":"https://n8n.io/blog/how-to-set-up-a-ci-cd-pipeline-with-no-code/","icon":"🎡","label":"How to set up a no-code CI/CD pipeline with GitHub and TravisCI"},{"url":"https://n8n.io/blog/benefits-of-automation-and-n8n-an-interview-with-hubspots-hugh-durkin/","icon":"🎖","label":"Benefits of automation and n8n: An interview with HubSpot's Hugh Durkin"},{"url":"https://n8n.io/blog/aws-workflow-automation/","label":"7 no-code workflow automations for Amazon Web Services"}],"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.if/"}]},"categories":["Core Nodes"],"nodeVersion":"1.0","codexVersion":"1.0","subcategories":{"Core Nodes":["Flow"]}}},"group":"[\"transform\"]","defaults":{"name":"If","color":"#408000"},"iconData":{"icon":"map-signs","type":"icon"},"displayName":"If","typeVersion":2,"nodeCategories":[{"id":9,"name":"Core Nodes"}]},{"id":38,"icon":"fa:pen","name":"n8n-nodes-base.set","codex":{"data":{"alias":["Set","JS","JSON","Filter","Transform","Map"],"resources":{"generic":[{"url":"https://n8n.io/blog/learn-to-automate-your-factorys-incident-reporting-a-step-by-step-guide/","icon":"🏭","label":"Learn to Automate Your Factory's Incident Reporting: A Step by Step Guide"},{"url":"https://n8n.io/blog/2021-the-year-to-automate-the-new-you-with-n8n/","icon":"☀️","label":"2021: The Year to Automate the New You with n8n"},{"url":"https://n8n.io/blog/automatically-pulling-and-visualizing-data-with-n8n/","icon":"📈","label":"Automatically pulling and visualizing data with n8n"},{"url":"https://n8n.io/blog/database-monitoring-and-alerting-with-n8n/","icon":"📡","label":"Database Monitoring and Alerting with n8n"},{"url":"https://n8n.io/blog/automatically-adding-expense-receipts-to-google-sheets-with-telegram-mindee-twilio-and-n8n/","icon":"🧾","label":"Automatically Adding Expense Receipts to Google Sheets with Telegram, Mindee, Twilio, and n8n"},{"url":"https://n8n.io/blog/no-code-ecommerce-workflow-automations/","icon":"store","label":"6 e-commerce workflows to power up your Shopify s"},{"url":"https://n8n.io/blog/how-to-build-a-low-code-self-hosted-url-shortener/","icon":"🔗","label":"How to build a low-code, self-hosted URL shortener in 3 steps"},{"url":"https://n8n.io/blog/automate-your-data-processing-pipeline-in-9-steps-with-n8n/","icon":"⚙️","label":"Automate your data processing pipeline in 9 steps"},{"url":"https://n8n.io/blog/how-to-get-started-with-crm-automation-and-no-code-workflow-ideas/","icon":"👥","label":"How to get started with CRM automation (with 3 no-code workflow ideas"},{"url":"https://n8n.io/blog/5-tasks-you-can-automate-with-notion-api/","icon":"⚡️","label":"5 tasks you can automate with the new Notion API "},{"url":"https://n8n.io/blog/automate-google-apps-for-productivity/","icon":"💡","label":"15 Google apps you can combine and automate to increase productivity"},{"url":"https://n8n.io/blog/how-uproc-scraped-a-multi-page-website-with-a-low-code-workflow/","icon":" 🕸️","label":"How uProc scraped a multi-page website with a low-code workflow"},{"url":"https://n8n.io/blog/building-an-expense-tracking-app-in-10-minutes/","icon":"📱","label":"Building an expense tracking app in 10 minutes"},{"url":"https://n8n.io/blog/the-ultimate-guide-to-automate-your-video-collaboration-with-whereby-mattermost-and-n8n/","icon":"📹","label":"The ultimate guide to automate your video collaboration with Whereby, Mattermost, and n8n"},{"url":"https://n8n.io/blog/5-workflow-automations-for-mattermost-that-we-love-at-n8n/","icon":"🤖","label":"5 workflow automations for Mattermost that we love at n8n"},{"url":"https://n8n.io/blog/learn-to-build-powerful-api-endpoints-using-webhooks/","icon":"🧰","label":"Learn to Build Powerful API Endpoints Using Webhooks"},{"url":"https://n8n.io/blog/how-a-membership-development-manager-automates-his-work-and-investments/","icon":"📈","label":"How a Membership Development Manager automates his work and investments"},{"url":"https://n8n.io/blog/a-low-code-bitcoin-ticker-built-with-questdb-and-n8n-io/","icon":"📈","label":"A low-code bitcoin ticker built with QuestDB and n8n.io"},{"url":"https://n8n.io/blog/how-to-set-up-a-ci-cd-pipeline-with-no-code/","icon":"🎡","label":"How to set up a no-code CI/CD pipeline with GitHub and TravisCI"},{"url":"https://n8n.io/blog/benefits-of-automation-and-n8n-an-interview-with-hubspots-hugh-durkin/","icon":"🎖","label":"Benefits of automation and n8n: An interview with HubSpot's Hugh Durkin"},{"url":"https://n8n.io/blog/how-goomer-automated-their-operations-with-over-200-n8n-workflows/","icon":"🛵","label":"How Goomer automated their operations with over 200 n8n workflows"},{"url":"https://n8n.io/blog/aws-workflow-automation/","label":"7 no-code workflow automations for Amazon Web Services"}],"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.set/"}]},"categories":["Core Nodes"],"nodeVersion":"1.0","codexVersion":"1.0","subcategories":{"Core Nodes":["Data Transformation"]}}},"group":"[\"input\"]","defaults":{"name":"Edit Fields"},"iconData":{"icon":"pen","type":"icon"},"displayName":"Edit Fields (Set)","typeVersion":3,"nodeCategories":[{"id":9,"name":"Core Nodes"}]},{"id":47,"icon":"file:webhook.svg","name":"n8n-nodes-base.webhook","codex":{"data":{"alias":["HTTP","API","Build","WH"],"resources":{"generic":[{"url":"https://n8n.io/blog/learn-how-to-automatically-cross-post-your-content-with-n8n/","icon":"✍️","label":"Learn how to automatically cross-post your content with n8n"},{"url":"https://n8n.io/blog/running-n8n-on-ships-an-interview-with-maranics/","icon":"🛳","label":"Running n8n on ships: An interview with Maranics"},{"url":"https://n8n.io/blog/how-to-build-a-low-code-self-hosted-url-shortener/","icon":"🔗","label":"How to build a low-code, self-hosted URL shortener in 3 steps"},{"url":"https://n8n.io/blog/what-are-apis-how-to-use-them-with-no-code/","icon":" 🪢","label":"What are APIs and how to use them with no code"},{"url":"https://n8n.io/blog/5-tasks-you-can-automate-with-notion-api/","icon":"⚡️","label":"5 tasks you can automate with the new Notion API "},{"url":"https://n8n.io/blog/how-a-digital-strategist-uses-n8n-for-online-marketing/","icon":"💻","label":"How a digital strategist uses n8n for online marketing"},{"url":"https://n8n.io/blog/the-ultimate-guide-to-automate-your-video-collaboration-with-whereby-mattermost-and-n8n/","icon":"📹","label":"The ultimate guide to automate your video collaboration with Whereby, Mattermost, and n8n"},{"url":"https://n8n.io/blog/how-to-automatically-give-kudos-to-contributors-with-github-slack-and-n8n/","icon":"👏","label":"How to automatically give kudos to contributors with GitHub, Slack, and n8n"},{"url":"https://n8n.io/blog/5-workflow-automations-for-mattermost-that-we-love-at-n8n/","icon":"🤖","label":"5 workflow automations for Mattermost that we love at n8n"},{"url":"https://n8n.io/blog/why-this-product-manager-loves-workflow-automation-with-n8n/","icon":"🧠","label":"Why this Product Manager loves workflow automation with n8n"},{"url":"https://n8n.io/blog/creating-custom-incident-response-workflows-with-n8n/","label":"How to automate every step of an incident response workflow"},{"url":"https://n8n.io/blog/learn-to-build-powerful-api-endpoints-using-webhooks/","icon":"🧰","label":"Learn to Build Powerful API Endpoints Using Webhooks"},{"url":"https://n8n.io/blog/learn-how-to-use-webhooks-with-mattermost-slash-commands/","icon":"🦄","label":"Learn how to use webhooks with Mattermost slash commands"},{"url":"https://n8n.io/blog/how-goomer-automated-their-operations-with-over-200-n8n-workflows/","icon":"🛵","label":"How Goomer automated their operations with over 200 n8n workflows"}],"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.webhook/"}]},"categories":["Development","Core Nodes"],"nodeVersion":"1.0","codexVersion":"1.0","subcategories":{"Core Nodes":["Helpers"]}}},"group":"[\"trigger\"]","defaults":{"name":"Webhook"},"iconData":{"type":"file","fileBuffer":"data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI0OCIgaGVpZ2h0PSI0OCI+PHBhdGggZmlsbD0iIzM3NDc0ZiIgZD0iTTM1IDM3Yy0yLjIgMC00LTEuOC00LTRzMS44LTQgNC00IDQgMS44IDQgNC0xLjggNC00IDQiLz48cGF0aCBmaWxsPSIjMzc0NzRmIiBkPSJNMzUgNDNjLTMgMC01LjktMS40LTcuOC0zLjdsMy4xLTIuNWMxLjEgMS40IDIuOSAyLjMgNC43IDIuMyAzLjMgMCA2LTIuNyA2LTZzLTIuNy02LTYtNmMtMSAwLTIgLjMtMi45LjdsLTEuNyAxTDIzLjMgMTZsMy41LTEuOSA1LjMgOS40YzEtLjMgMi0uNSAzLS41IDUuNSAwIDEwIDQuNSAxMCAxMFM0MC41IDQzIDM1IDQzIi8+PHBhdGggZmlsbD0iIzM3NDc0ZiIgZD0iTTE0IDQzQzguNSA0MyA0IDM4LjUgNCAzM2MwLTQuNiAzLjEtOC41IDcuNS05LjdsMSAzLjlDOS45IDI3LjkgOCAzMC4zIDggMzNjMCAzLjMgMi43IDYgNiA2czYtMi43IDYtNnYtMmgxNXY0SDIzLjhjLS45IDQuNi01IDgtOS44IDgiLz48cGF0aCBmaWxsPSIjZTkxZTYzIiBkPSJNMTQgMzdjLTIuMiAwLTQtMS44LTQtNHMxLjgtNCA0LTQgNCAxLjggNCA0LTEuOCA0LTQgNCIvPjxwYXRoIGZpbGw9IiMzNzQ3NGYiIGQ9Ik0yNSAxOWMtMi4yIDAtNC0xLjgtNC00czEuOC00IDQtNCA0IDEuOCA0IDQtMS44IDQtNCA0Ii8+PHBhdGggZmlsbD0iI2U5MWU2MyIgZD0ibTE1LjcgMzQtMy40LTIgNS45LTkuN2MtMi0xLjktMy4yLTQuNS0zLjItNy4zIDAtNS41IDQuNS0xMCAxMC0xMHMxMCA0LjUgMTAgMTBjMCAuOS0uMSAxLjctLjMgMi41bC0zLjktMWMuMS0uNS4yLTEgLjItMS41IDAtMy4zLTIuNy02LTYtNnMtNiAyLjctNiA2YzAgMi4xIDEuMSA0IDIuOSA1LjFsMS43IDF6Ii8+PC9zdmc+"},"displayName":"Webhook","typeVersion":2,"nodeCategories":[{"id":5,"name":"Development"},{"id":9,"name":"Core 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":834,"icon":"file:code.svg","name":"n8n-nodes-base.code","codex":{"data":{"alias":["cpde","Javascript","JS","Python","Script","Custom Code","Function"],"details":"The Code node allows you to execute JavaScript in your workflow.","resources":{"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"}]},"categories":["Development","Core Nodes"],"nodeVersion":"1.0","codexVersion":"1.0","subcategories":{"Core Nodes":["Helpers","Data Transformation"]}}},"group":"[\"transform\"]","defaults":{"name":"Code"},"iconData":{"type":"file","fileBuffer":"data:image/svg+xml;base64,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"},"displayName":"Code","typeVersion":2,"nodeCategories":[{"id":5,"name":"Development"},{"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"],"resources":{"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"}]},"categories":["AI","Langchain"],"subcategories":{"AI":["Agents","Root Nodes"]}}},"group":"[\"transform\"]","defaults":{"name":"AI Agent","color":"#404040"},"iconData":{"icon":"robot","type":"icon"},"displayName":"AI Agent","typeVersion":3,"nodeCategories":[{"id":25,"name":"AI"},{"id":26,"name":"Langchain"}]},{"id":1153,"icon":"file:openAiLight.svg","name":"@n8n/n8n-nodes-langchain.lmChatOpenAi","codex":{"data":{"resources":{"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatopenai/"}]},"categories":["AI","Langchain"],"subcategories":{"AI":["Language Models","Root Nodes"],"Language Models":["Chat Models (Recommended)"]}}},"group":"[\"transform\"]","defaults":{"name":"OpenAI Chat Model"},"iconData":{"type":"file","fileBuffer":"data:image/svg+xml;base64,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"},"displayName":"OpenAI Chat Model","typeVersion":1,"nodeCategories":[{"id":25,"name":"AI"},{"id":26,"name":"Langchain"}]},{"id":1179,"icon":"fa:code","name":"@n8n/n8n-nodes-langchain.outputParserStructured","codex":{"data":{"alias":["json","zod"],"resources":{"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.outputparserstructured/"}]},"categories":["AI","Langchain"],"subcategories":{"AI":["Output Parsers"]}}},"group":"[\"transform\"]","defaults":{"name":"Structured Output Parser"},"iconData":{"icon":"code","type":"icon"},"displayName":"Structured Output Parser","typeVersion":1,"nodeCategories":[{"id":25,"name":"AI"},{"id":26,"name":"Langchain"}]},{"id":1250,"icon":"file:openAi.svg","name":"@n8n/n8n-nodes-langchain.openAi","codex":{"data":{"alias":["LangChain","ChatGPT","Sora","DallE","whisper","audio","transcribe","tts","assistant"],"resources":{"primaryDocumentation":[{"url":"https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-langchain.openai/"}]},"categories":["AI","Langchain"],"subcategories":{"AI":["Agents","Miscellaneous","Root Nodes"]}}},"group":"[\"transform\"]","defaults":{"name":"OpenAI"},"iconData":{"type":"file","fileBuffer":"data:image/svg+xml;base64,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"},"displayName":"OpenAI","typeVersion":2,"nodeCategories":[{"id":25,"name":"AI"},{"id":26,"name":"Langchain"}]}],"categories":[{"id":5,"name":"Engineering"},{"id":49,"name":"AI Summarization"}],"image":[]}}