{"workflow":{"id":14039,"name":"Route AI tasks between Anthropic Claude models with Postgres policies and SLA","views":25,"recentViews":1,"totalViews":25,"createdAt":"2026-03-14T13:02:22.034Z","description":"## Overview\n\nThis workflow implements a **policy-driven LLM orchestration system** that dynamically routes AI tasks to different language models based on task complexity, policies, and performance constraints.\n\nInstead of sending every request to a single model, the workflow analyzes each task, applies policy rules, and selects the most appropriate model for execution. It also records telemetry data such as latency, token usage, and cost, enabling continuous optimization.\n\nA built-in **self-tuning mechanism** runs weekly to analyze historical telemetry and automatically update routing policies. This allows the system to improve cost efficiency, performance, and reliability over time without manual intervention.\n\nThis architecture is useful for teams building **AI APIs, agent platforms, or multi-model LLM systems** where intelligent routing is needed to balance cost, speed, and quality.\n\n---\n\n## How It Works\n\n1. **Webhook Task Input**\n   - The workflow begins when a request is sent to the webhook endpoint.\n   - The request contains a task and optional priority metadata.\n\n2. **Task Classification**\n   - A classifier agent analyzes the task and categorizes it into:\n     - extraction\n     - classification\n     - reasoning\n     - generation\n   - The agent also returns a confidence score.\n\n3. **Policy Engine**\n   - Policy rules are loaded from a database.\n   - These rules define execution constraints such as:\n     - preferred model size\n     - latency limits\n     - token budgets\n     - retry strategies\n     - cost ceilings.\n\n4. **Model Routing**\n   - A decision engine evaluates classification results and policy rules.\n   - Tasks are routed to either a **small model** (fast and cost-efficient) or a **large model** (higher reasoning capability).\n\n5. **Task Execution**\n   - The selected LLM processes the task and generates the response.\n\n6. **Telemetry Collection**\n   - Execution metrics are captured including:\n     - latency\n     - tokens used\n     - estimated cost\n     - model used\n     - success status.\n   - These metrics are stored in a database.\n\n7. **Weekly Self-Optimization**\n   - A scheduled workflow analyzes telemetry from the past 7 days.\n   - If performance trends change, routing policies are automatically updated.\n\n---\n\n## Setup Instructions\n\n1. **Configure a Postgres database**\n   - Create two tables:\n     - `policy_rules`\n     - `telemetry`\n\n2. **Add LLM credentials**\n   - Configure Anthropic credentials for the language model nodes.\n\n3. **Configure policy rules**\n   - Define preferred models, cost limits, and latency thresholds in the `policy_rules` table.\n\n4. **Configure workflow settings**\n   - Adjust parameters in the **Workflow Configuration** node:\n     - maximum latency\n     - cost ceiling\n     - token limits\n     - retry behavior.\n\n5. **Deploy the API endpoint**\n   - Send requests to the webhook endpoint:\n\n\n\n\n---\n\n## Use Cases\n\n### AI API Gateway\nRoute requests to different models based on complexity and cost constraints.\n\n### Multi-Model AI Platforms\nAutomatically choose the best model for each task without manual configuration.\n\n### Cost-Optimized AI Systems\nPrefer smaller models for simple tasks while reserving larger models for complex reasoning.\n\n### LLM Observability\nTrack token usage, latency, and cost for each AI request.\n\n### Self-Optimizing AI Infrastructure\nAutomatically improve routing policies using real execution telemetry.\n\n---\n\n## Requirements\n\n- n8n with **LangChain nodes enabled**\n- Postgres database\n- Anthropic API credentials\n- Tables:\n- `policy_rules`\n- `telemetry`\n\nOptional:\n\n- Monitoring dashboards connected to telemetry data\n- External policy management systems","workflow":{"meta":{"instanceId":"48aac30adfc5487a33ef16e0e958096f27cef40df3ed0febcbe1ca199e789786"},"nodes":[{"id":"0becb529-f4bf-4033-b90f-f2f7caabae8a","name":"Task Input Webhook","type":"n8n-nodes-base.webhook","position":[-1616,80],"webhookId":"297cdc22-8f0d-4cbc-a19f-b876a3894c14","parameters":{"path":"llm-orchestrator","options":{},"httpMethod":"POST","responseMode":"lastNode"},"typeVersion":2.1},{"id":"e46430e8-1081-4511-a38c-16d1fdbf54ae","name":"Workflow Configuration","type":"n8n-nodes-base.set","position":[-1360,80],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"maxLatencyMs","type":"number","value":30000},{"id":"id-2","name":"costCeiling","type":"number","value":0.5},{"id":"id-3","name":"timeoutMs","type":"number","value":25000},{"id":"id-4","name":"maxTokens","type":"number","value":4000},{"id":"id-5","name":"maxRetries","type":"number","value":2}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"a298d196-218a-4cd0-8c4c-fdc00ab4ee40","name":"Task Classifier Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[-1136,80],"parameters":{"text":"={{ $json.task }}","options":{"systemMessage":"You are a task classification expert. Analyze the provided task and classify it into one of these categories: extraction, classification, reasoning, or generation.\n\nProvide a confidence score (0-100) for your classification.\n\nConsider:\n- Extraction: pulling specific data from text\n- Classification: categorizing or labeling content\n- Reasoning: logical analysis or problem-solving\n- Generation: creating new content\n\nReturn your analysis in the structured format."},"promptType":"define","hasOutputParser":true},"typeVersion":3},{"id":"6564841d-fb51-4a1d-8721-451ccf0fa77f","name":"Load Policy Rules","type":"n8n-nodes-base.postgres","position":[-784,80],"parameters":{"query":"SELECT * FROM policy_rules WHERE task_type = $1 AND priority = $2","options":{"queryReplacement":"={{ $json.taskType }},={{ $json.priority }}"},"operation":"executeQuery"},"typeVersion":2.6},{"id":"d867f0a1-76e8-4df1-84c6-1f155b84bca5","name":"Policy Engine Decision","type":"n8n-nodes-base.code","position":[-624,80],"parameters":{"jsCode":"const config = $('Workflow Configuration').first().json;\nconst classification = $input.first().json;\nconst policy = $input.item.json;\n\n// Decision matrix based on policy rules\nconst decision = {\n  modelSize: policy.preferred_model || 'small',\n  allowTools: policy.allow_tools || false,\n  retryStrategy: policy.retry_strategy || 'fallback',\n  timeoutMs: Math.min(classification.priority === 'critical' ? config.timeoutMs * 0.8 : config.timeoutMs, policy.max_latency_ms || config.timeoutMs),\n  maxTokens: policy.max_tokens || config.maxTokens,\n  costBudget: Math.min(config.costCeiling, policy.cost_limit || config.costCeiling),\n  taskType: classification.taskType,\n  confidence: classification.confidence,\n  priority: classification.priority\n};\n\n// Override to large model for complex reasoning or low confidence\nif (classification.taskType === 'reasoning' && classification.confidence < 70) {\n  decision.modelSize = 'large';\n}\n\nif (classification.priority === 'critical') {\n  decision.modelSize = 'large';\n}\n\nreturn decision;"},"typeVersion":2},{"id":"5cbd74df-80e5-4389-88e8-eefebafe5084","name":"Route by Model Selection","type":"n8n-nodes-base.switch","position":[-272,64],"parameters":{"rules":{"values":[{"outputKey":"small","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"string","operation":"equals"},"leftValue":"={{ $json.modelSize }}","rightValue":"small"}]},"renameOutput":true},{"outputKey":"large","conditions":{"options":{"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"operator":{"type":"string","operation":"equals"},"leftValue":"={{ $json.modelSize }}","rightValue":"large"}]},"renameOutput":true}]},"options":{"fallbackOutput":"extra","renameFallbackOutput":"Fallback"}},"typeVersion":3.4},{"id":"5b78b42d-1d6e-4c7f-a496-cce92e0e6e01","name":"Large Model Execution","type":"@n8n/n8n-nodes-langchain.agent","position":[48,656],"parameters":{"text":"={{ $json.task }}","options":{"systemMessage":"=You are an advanced AI assistant capable of complex reasoning and detailed analysis. Process the task thoroughly and accurately.\n\nTask type: {{ $json.taskType }}\nPriority: {{ $json.priority }}\n\nProvide comprehensive, well-reasoned responses within the allocated time and token budget."},"promptType":"define"},"typeVersion":3},{"id":"d40f3de4-3bce-448f-afc6-2a98801832f4","name":"Store Telemetry","type":"n8n-nodes-base.postgres","position":[640,192],"parameters":{"table":{"__rl":true,"mode":"name","value":"telemetry"},"schema":{"__rl":true,"mode":"list","value":"public"},"columns":{"value":{"success":"={{ $json.success }}","priority":"={{ $json.priority }}","task_type":"={{ $json.task_type }}","timestamp":"={{ $json.timestamp }}","latency_ms":"={{ $json.latency_ms }}","model_used":"={{ $json.model_used }}","tokens_used":"={{ $json.tokens_used }}","failure_type":"={{ $json.failure_type }}","estimated_cost":"={{ $json.estimated_cost }}"},"schema":[{"id":"task_type","type":"string","display":true,"required":false,"displayName":"task_type","defaultMatch":true,"canBeUsedToMatch":true},{"id":"priority","type":"string","display":true,"required":false,"displayName":"priority","defaultMatch":true,"canBeUsedToMatch":true},{"id":"model_used","type":"string","display":true,"required":false,"displayName":"model_used","defaultMatch":true,"canBeUsedToMatch":true},{"id":"latency_ms","type":"number","display":true,"required":false,"displayName":"latency_ms","defaultMatch":true,"canBeUsedToMatch":true},{"id":"tokens_used","type":"number","display":true,"required":false,"displayName":"tokens_used","defaultMatch":true,"canBeUsedToMatch":true},{"id":"estimated_cost","type":"number","display":true,"required":false,"displayName":"estimated_cost","defaultMatch":true,"canBeUsedToMatch":true},{"id":"success","type":"boolean","display":true,"required":false,"displayName":"success","defaultMatch":true,"canBeUsedToMatch":true},{"id":"failure_type","type":"string","display":true,"required":false,"displayName":"failure_type","defaultMatch":true,"canBeUsedToMatch":true},{"id":"timestamp","type":"string","display":true,"required":false,"displayName":"timestamp","defaultMatch":true,"canBeUsedToMatch":true}],"mappingMode":"defineBelow","matchingColumns":["task_type","priority","model_used","latency_ms","tokens_used","estimated_cost","success","failure_type","timestamp"]},"options":{}},"typeVersion":2.6},{"id":"a40d7801-17d7-4f74-b00b-d5202126a41d","name":"Return Response","type":"n8n-nodes-base.respondToWebhook","position":[992,192],"parameters":{"options":{},"respondWith":"json","responseBody":"={\n  \"result\": {{ $json.output }},\n  \"latency\": {{ $json.executionLatencyMs }},\n  \"model\": {{ $json.modelUsed }},\n  \"cost\": {{ $json.estimatedCost }}\n}"},"typeVersion":1.5},{"id":"527af998-2b05-4141-933e-1e8ffe960e44","name":"Weekly Self-Tuning Schedule","type":"n8n-nodes-base.scheduleTrigger","position":[-1584,768],"parameters":{"rule":{"interval":[{"field":"weeks","triggerAtHour":2}]}},"typeVersion":1.3},{"id":"14f9e6bb-1e94-4b89-81ff-5025b1aeddb1","name":"Fetch Historical Data","type":"n8n-nodes-base.postgres","position":[-1312,768],"parameters":{"query":"SELECT task_type, model_used, AVG(latency_ms) as avg_latency, AVG(estimated_cost) as avg_cost, COUNT(*) as total_executions, SUM(CASE WHEN success = true THEN 1 ELSE 0 END) as successful_executions FROM telemetry WHERE timestamp > NOW() - INTERVAL '7 days' GROUP BY task_type, model_used","options":{},"operation":"executeQuery"},"typeVersion":2.6},{"id":"81d0b945-eb1d-4f6e-abaf-b4b37a2e0f98","name":"Aggregate Success Metrics","type":"n8n-nodes-base.aggregate","position":[-1040,768],"parameters":{"include":"specifiedFields","options":{},"aggregate":"aggregateAllItemData","fieldsToInclude":"task_type"},"typeVersion":1},{"id":"50e7ded2-aa27-42d8-b72a-e005c6511f24","name":"Calculate Routing Adjustments","type":"n8n-nodes-base.code","position":[-848,768],"parameters":{"jsCode":"const metrics = $input.first().json;\n\n// Calculate success rates and performance\nconst adjustments = [];\n\nfor (const metric of metrics) {\n  const successRate = metric.successful_executions / metric.total_executions;\n  const avgLatency = metric.avg_latency;\n  const avgCost = metric.avg_cost;\n  \n  let recommendation = metric.model_used;\n  \n  // If small model has high success rate and low latency, prefer it\n  if (metric.model_used === 'large' && successRate > 0.95 && avgLatency < 5000) {\n    recommendation = 'small';\n  }\n  \n  // If small model has low success rate, upgrade to large\n  if (metric.model_used === 'small' && successRate < 0.80) {\n    recommendation = 'large';\n  }\n  \n  adjustments.push({\n    task_type: metric.task_type,\n    current_model: metric.model_used,\n    recommended_model: recommendation,\n    success_rate: successRate,\n    avg_latency: avgLatency,\n    avg_cost: avgCost,\n    adjustment_reason: recommendation !== metric.model_used ? 'Performance optimization' : 'No change needed'\n  });\n}\n\nreturn adjustments;"},"typeVersion":2},{"id":"8186382f-38a1-4471-8433-bae6bff34298","name":"Update Policy Rules","type":"n8n-nodes-base.postgres","position":[-512,768],"parameters":{"query":"UPDATE policy_rules SET preferred_model = $1 WHERE task_type = $2","options":{"queryReplacement":"={{ $json.recommended_model }},={{ $json.task_type }}"},"operation":"executeQuery"},"typeVersion":2.6},{"id":"bf404937-2cab-4730-9d3f-23bec52b23c7","name":"Anthropic Chat Model - Classifier","type":"@n8n/n8n-nodes-langchain.lmChatAnthropic","position":[-1168,288],"parameters":{"model":{"__rl":true,"mode":"id","value":"claude-3-5-haiku-20241022"},"options":{}},"typeVersion":1.3},{"id":"5b225513-a16e-44cb-b78c-d1e5e49246e5","name":"Classification Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[-912,304],"parameters":{"schemaType":"manual","inputSchema":"{\n\t\"type\": \"object\",\n\t\"properties\": {\n\t\t\"taskType\": {\n\t\t\t\"type\": \"string\",\n\t\t\t\"description\": \"The classified task type: extraction, classification, reasoning, or generation\"\n\t\t},\n\t\t\"confidence\": {\n\t\t\t\"type\": \"number\",\n\t\t\t\"description\": \"Confidence score from 0-100\"\n\t\t},\n\t\t\"priority\": {\n\t\t\t\"type\": \"string\",\n\t\t\t\"description\": \"Task priority from input\"\n\t\t}\n\t}\n}"},"typeVersion":1.3},{"id":"5c540c6b-5829-4306-bd9d-d5d3d17d583a","name":"Anthropic Chat Model - Small","type":"@n8n/n8n-nodes-langchain.lmChatAnthropic","position":[96,-192],"parameters":{"model":{"__rl":true,"mode":"id","value":"claude-3-5-haiku-20241022"},"options":{}},"typeVersion":1.3},{"id":"5163a1cc-433c-47f0-b7be-d38d92701f4a","name":"Anthropic Chat Model - Large","type":"@n8n/n8n-nodes-langchain.lmChatAnthropic","position":[-112,784],"parameters":{"model":{"__rl":true,"mode":"id","value":"claude-3-5-sonnet-20241022"},"options":{}},"typeVersion":1.3},{"id":"07413d00-5c68-415e-b92a-bde152e43f68","name":"Prepare Telemetry Data","type":"n8n-nodes-base.set","position":[400,192],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"executionLatencyMs","type":"number","value":"={{ $now.diff($('Task Classifier Agent').first().json.startTime) }}"},{"id":"id-2","name":"modelUsed","type":"string","value":"={{ $json.modelSize || 'unknown' }}"},{"id":"id-3","name":"tokensUsed","type":"number","value":"={{ $json.usage?.total_tokens || 0 }}"},{"id":"id-4","name":"estimatedCost","type":"number","value":"={{ ($json.usage?.total_tokens || 0) * 0.00001 }}"},{"id":"id-5","name":"success","type":"boolean","value":true},{"id":"id-6","name":"failureType","type":"string","value":"null"},{"id":"id-7","name":"timestamp","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"b3e3e753-0909-40fb-b131-a3e39352fc5a","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-1248,-176],"parameters":{"color":7,"width":800,"height":592,"content":"## Policy Engine\n\nLoads routing policies from the database and combines them with task classification results.\n\nThe policy engine determines:\n\n• model size (small or large)  \n• token limits  \n• latency constraints  \n• cost budget"},"typeVersion":1},{"id":"3050af39-dbed-4aa3-be9a-f5fd58e5c4df","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-1728,608],"parameters":{"color":7,"width":288,"height":320,"content":"## Weekly Optimization Trigger\n\nThis scheduled trigger runs once per week to analyze LLM execution performance."},"typeVersion":1},{"id":"47a36f16-2712-4371-b065-cf785a833892","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-1760,-112],"parameters":{"color":7,"width":336,"height":400,"content":"## Input\n\nEach request contains a task and optional priority metadata.  \nThis webhook acts as the API entry point for the LLM orchestration system."},"typeVersion":1},{"id":"258ba5db-8fb4-44b8-8e1f-0adb7318e2d0","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[-1088,608],"parameters":{"color":7,"width":400,"height":336,"content":"## Performance Aggregation\n\nTelemetry records are grouped by task type to calculate overall performance metrics.\n"},"typeVersion":1},{"id":"7640f65e-389b-404f-b9f4-fc10cb428942","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[-1424,608],"parameters":{"color":7,"width":320,"height":320,"content":"## Historical Telemetry Analysis\n\nExecution telemetry from the past 7 days is retrieved from the database."},"typeVersion":1},{"id":"adf0417d-65c1-4c5e-a2e5-ab82c2ba1169","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[-672,608],"parameters":{"color":7,"width":416,"height":336,"content":"## Automatic Policy Optimization\n\nModel performance is analyzed to determine if routing policies should change.\nIf a smaller model performs well, it may replace a larger one."},"typeVersion":1},{"id":"3e307f55-1408-4d9c-8794-3f6b59e7c29d","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[224,-64],"parameters":{"color":7,"width":624,"height":448,"content":"## Telemetry Collection\n\nExecution metrics are captured after each task:\n\n• latency  \n• tokens used  \n• estimated cost  \n• model used  \n• success status\n\nThese metrics are stored for monitoring and optimization."},"typeVersion":1},{"id":"6854ce54-122a-496a-b142-baca4ad8b0e9","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[864,-48],"parameters":{"color":7,"width":384,"height":400,"content":" API Response\n\nThe workflow returns the AI response along with execution metadata including latency, model used, and estimated cost."},"typeVersion":1},{"id":"0c5dbc16-ab23-436f-a95e-897cf410b776","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[-176,464],"parameters":{"color":7,"width":544,"height":448,"content":"## Large Model Execution\n\nProcesses complex tasks that require deeper reasoning or higher accuracy.\n\nUsed when the policy engine detects low confidence, high priority, or reasoning tasks."},"typeVersion":1},{"id":"bc9d56ff-ecae-40d0-ad6a-62525ae1cbff","name":"Sticky Note10","type":"n8n-nodes-base.stickyNote","position":[-432,-144],"parameters":{"color":7,"width":336,"height":528,"content":"## Model Routing\n\nTasks are routed to different model pipelines depending on policy decisions.\n"},"typeVersion":1},{"id":"1d11df13-0164-42b1-bdb5-c3147a097fbd","name":"Sticky Note11","type":"n8n-nodes-base.stickyNote","position":[-256,-624],"parameters":{"color":7,"width":720,"height":464,"content":"## Small Model Execution\n\nHandles lightweight tasks such as extraction or classification.\n\nOptimized for speed and cost efficiency while respecting token and latency limits."},"typeVersion":1},{"id":"03475ddc-9a8d-49ee-8284-2dbd4e86aeca","name":"Small Model Execution","type":"@n8n/n8n-nodes-langchain.agent","position":[0,-400],"parameters":{"text":"={{ $json.task }}","options":{"systemMessage":"=You are a fast, efficient AI assistant optimized for quick responses. Process the task accurately and concisely.\n\nTask type: {{ $json.taskType }}\nPriority: {{ $json.priority }}\n\nProvide your response within the allocated time and token budget."},"promptType":"define"},"typeVersion":3},{"id":"49f091e1-1b4d-41a9-9c34-5733482af0b9","name":"Sticky Note12","type":"n8n-nodes-base.stickyNote","position":[-2576,-64],"parameters":{"width":688,"height":736,"content":"## Policy-Driven LLM Orchestrator with Self-Tuning Model Routing\n\nThis workflow acts as an intelligent orchestration layer for large language models. Instead of sending every task to a single model, it dynamically decides which model to use based on task complexity, policies, and historical performance.\n\n## How it works\n\nA webhook receives tasks and sends them to a classifier agent that determines the task type (extraction, classification, reasoning, or generation) along with a confidence score.\n\nThe workflow then loads policy rules from a database and applies a policy engine to determine the optimal execution strategy. These rules define model size, latency limits, token budgets, retry behavior, and cost ceilings.\n\nTasks are routed to either a small or large model depending on complexity and priority.\n\nTelemetry such as latency, tokens used, estimated cost, and success status is stored for every execution.\n\nA weekly optimization workflow analyzes this telemetry data to adjust routing policies automatically, improving cost efficiency and performance over time.\n\n## Setup steps\n\n1. Connect a Postgres database.\n2. Create tables: policy_rules and telemetry.\n3. Add Anthropic credentials for the LLM nodes.\n4. Configure latency, cost, and token limits in the configuration node.\n5. Send tasks to the webhook endpoint."},"typeVersion":1}],"pinData":{},"connections":{"Store Telemetry":{"main":[[{"node":"Return Response","type":"main","index":0}]]},"Load Policy Rules":{"main":[[{"node":"Policy Engine Decision","type":"main","index":0}]]},"Task Input Webhook":{"main":[[{"node":"Workflow Configuration","type":"main","index":0}]]},"Fetch Historical Data":{"main":[[{"node":"Aggregate Success Metrics","type":"main","index":0}]]},"Large Model Execution":{"main":[[{"node":"Prepare Telemetry Data","type":"main","index":0}]]},"Small Model Execution":{"main":[[{"node":"Prepare Telemetry Data","type":"main","index":0}]]},"Task Classifier Agent":{"main":[[{"node":"Load Policy Rules","type":"main","index":0}]]},"Policy Engine Decision":{"main":[[{"node":"Route by Model Selection","type":"main","index":0}]]},"Prepare Telemetry Data":{"main":[[{"node":"Store Telemetry","type":"main","index":0}]]},"Workflow Configuration":{"main":[[{"node":"Task Classifier 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