Yaron Been This workflow corresponds to n8n.io template #4578 — we link there as the canonical source.
This workflow follows the Agent → OpenAI Embeddings recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"id": "czDg0Q7wsAoqYQIx",
"name": "COO Task Prioritization",
"tags": [],
"nodes": [
{
"id": "39d955ab-7c8e-4faa-ae3d-948dfa5e0615",
"name": "Pinecone Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
640,
500
],
"parameters": {
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": ""
}
},
"credentials": {
"pineconeApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "923a5e54-785a-4c4f-b6b8-f540bb1c4e19",
"name": "Daily 9AM Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
0,
0
],
"parameters": {
"rule": {
"interval": [
{
"triggerAtHour": 9
}
]
}
},
"typeVersion": 1.2
},
{
"id": "28adcd8d-e4cf-45f7-ad32-16ffe30a525b",
"name": "Fetch Tasks",
"type": "n8n-nodes-base.asana",
"position": [
220,
0
],
"parameters": {
"filters": {
"project": ""
},
"operation": "getAll",
"returnAll": true
},
"typeVersion": 1
},
{
"id": "b43946e1-1b62-46fc-9211-72551b6c55c7",
"name": "COO Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
660,
0
],
"parameters": {
"text": "={{ $json.data }}",
"options": {
"systemMessage": "=You are a COO AI agent that helps prioritize operational tasks. Use the retrieved context from vector_database_tool to assess task urgency and strategic importance.\n\nFor the most priority task, output:\n\nTask Name\n\nPriority Level: High / Medium / Low\n\nDue Date\n\nReason for Priority\n\nConsider urgency (due date), impact on operations, and dependencies retrieved from vector knowledge.\nJust give back the most important task"
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.9
},
{
"id": "b716a7b4-0357-4730-89b2-8bc295d5a22a",
"name": "Save Prioritized Task",
"type": "n8n-nodes-base.postgres",
"position": [
1600,
0
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "prioritized_tasks",
"cachedResultName": "prioritized_tasks"
},
"schema": {
"__rl": true,
"mode": "list",
"value": "public"
},
"columns": {
"value": {
"task_name": "={{ $json.output.tasks[0][\"Task Name\"] }}",
"priority_level": "={{ $json.output.tasks[0][\"Priority Level\"] }}",
"reason_for_priority": "={{ $json.output.tasks[0][\"Reason for Priority\"] }}"
},
"schema": [
{
"id": "id",
"type": "number",
"display": true,
"removed": true,
"required": false,
"displayName": "id",
"defaultMatch": true,
"canBeUsedToMatch": true
},
{
"id": "task_name",
"type": "string",
"display": true,
"required": true,
"displayName": "task_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "priority_level",
"type": "string",
"display": true,
"required": false,
"displayName": "priority_level",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "reason_for_priority",
"type": "string",
"display": true,
"required": false,
"displayName": "reason_for_priority",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"credentials": {
"postgres": {
"name": "<your credential>"
}
},
"typeVersion": 2.6
},
{
"id": "759f799a-d046-4609-8894-99a9b951e43f",
"name": "Task Reasoning Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
560,
260
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "4e3a60ec-d900-44e9-beaf-0de5905d9b2e",
"name": "Priority Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1120,
180
],
"parameters": {
"jsonSchemaExample": "{\n \"tasks\": [\n {\n \"Task Name\": \"Review quarterly KPIs\",\n \"Priority Level\": \"High\",\n \"Reason for Priority\": \"Provides critical performance metrics for all departments; deadline is approaching.\"\n },\n"
},
"typeVersion": 1.2
},
{
"id": "d7ae1305-e2f2-44c0-9c21-8cb29310a5d8",
"name": "Task Knowledge Vector DB",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
820,
220
],
"parameters": {
"name": "vector_database_tool",
"description": "The information related to task prioritization is here"
},
"typeVersion": 1
},
{
"id": "906b83fc-d501-4442-b470-3e90148ace33",
"name": "Vector Query Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
960,
440
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "e2b8e43c-1f7e-43e0-bba1-d186776d1e2b",
"name": "Generate Task Embeddings",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
580,
720
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "61c92166-6068-4884-ac39-af52ae4a496a",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-40,
-480
],
"parameters": {
"color": 6,
"width": 400,
"height": 680,
"content": "## \ud83d\udd01 Section 1: Trigger & Data Retrieval\n\n### \ud83d\udd39 **Node: `Daily 9AM Trigger`**\n\n* **Function**: Starts the workflow every day at 9:00 AM.\n* **Type**: Time-based scheduler.\n* **Why**: Ensures task processing is automated and consistent on a daily schedule.\n\n### \ud83d\udd39 **Node: `Fetch Asana Tasks`**\n\n* **Function**: Retrieves all tasks from Asana that are due or assigned.\n* **Type**: API Connector to Asana.\n* **Why**: Acts as the entry point for incoming task data that needs prioritization.\n"
},
"typeVersion": 1
},
{
"id": "c9d36c9c-2cd3-407c-bc97-c765e6cb3bc6",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
500,
-600
],
"parameters": {
"color": 5,
"width": 760,
"height": 1480,
"content": "## \ud83e\udde0 Section 2: AI Prioritization\n\n### \ud83d\udd39 **Node: `COO Task Prioritization Agent`**\n\n* **Function**: Uses an AI Agent to evaluate and assign a priority to each task.\n* **Inputs**: Raw task data from Asana.\n* **Outputs**:\n\n * `task_name`\n * `priority_level` (e.g., High, Medium, Low)\n * `reason_for_priority` (a justification provided by the AI)\n* **Why**: Simulates a COO\u2019s judgment to focus on strategic and urgent tasks.\n\n### \ud83d\udd39 **Node: `Task Reasoning Model`**\n\n* **Function**: Enhances the AI Agent's decision-making by providing natural language reasoning.\n* **Model**: OpenAI Chat Model.\n* **Why**: Adds contextual understanding to why a task is important.\n\n### \ud83d\udd39 **Node: `Priority Output Parser`**\n\n* **Function**: Extracts structured data (task name, priority level, reasoning) from the AI\u2019s output.\n* **Why**: Ensures only clean, usable data is passed to the database layer."
},
"typeVersion": 1
},
{
"id": "4030c4f7-140c-453b-8441-4ed230da0928",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1460,
-460
],
"parameters": {
"color": 3,
"width": 380,
"height": 640,
"content": "## \ud83d\uddc3\ufe0f Section 3: Data Storage & Enrichment\n\n### \ud83d\udd39 **Node: `Save Prioritized Tasks`**\n\n* **Function**: Inserts each prioritized task into a PostgreSQL database.\n* **Fields**:\n\n * `task_name`\n * `priority_level`\n * `reason_for_priority`\n* **Why**: Provides a persistent store for further reporting, monitoring, or automation."
},
"typeVersion": 1
},
{
"id": "cd768682-0aee-41d9-bdbc-0b3e8509a042",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1980,
-460
],
"parameters": {
"color": 4,
"width": 1300,
"height": 320,
"content": "=======================================\n WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n - YouTube: https://www.youtube.com/@YaronBeen/videos\n - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n"
},
"typeVersion": 1
},
{
"id": "019cb716-597a-49b3-8eeb-fc523f210c8f",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1980,
-120
],
"parameters": {
"color": 4,
"width": 1289,
"height": 2238,
"content": "Absolutely! Here's a **very detailed, polished explanation** of your workflow \u2014 structured in a **clean, professional format** with icons and broken into **three logical sections**. This is suitable for documentation, presentations, or onboarding team members.\n\n---\n\n# \ud83e\udde0 COO Task Automation Workflow Documentation\n\nThis intelligent automation workflow simulates a **COO\u2019s decision-making** by prioritizing daily tasks fetched from Asana using AI. It operates autonomously every morning at 9 AM, analyzes the tasks using an AI reasoning engine with memory and context, and stores the prioritized outcomes in a structured database for operational use.\n\n---\n\n## \u23f0 1. Trigger & Task Collection\n\n### \ud83d\udcc5 **Automated Daily Start**\n\n* **Time-based Scheduling**: The system is configured to initiate **automatically every day at 9:00 AM** without any manual input.\n* This guarantees consistency in daily planning and ensures tasks are prioritized early in the workday.\n\n### \ud83d\udd17 **Integration with Asana**\n\n* The workflow connects directly to the **Asana task management platform**.\n* It fetches tasks assigned to the team or specific users, filtered by criteria such as due date, project, or tag.\n* These tasks include metadata like task name, description, due date, assignee, and any custom fields.\n* All this information becomes the **raw input** for the AI decision-making engine.\n\n---\n\n## \ud83e\udde0 2. Intelligent Task Prioritization\n\n### \ud83e\udd16 **AI-Powered Prioritization Logic**\n\n* Once tasks are collected, they are passed to a custom **AI Agent**.\n* This AI agent plays the role of a **virtual COO**, applying strategic reasoning to prioritize tasks based on business urgency, impact, and context.\n\n### \ud83e\udde9 **Reasoning Through Language Models**\n\n* Behind the scenes, the agent leverages a **large language model (LLM)** to:\n\n * Analyze each task's content.\n * Cross-reference it with similar past tasks or organizational priorities.\n * Generate an intelligent output:\n\n * **Task Name**\n * **Priority Level** (High, Medium, Low)\n * **Reason for Priority** (justification written in natural language).\n\n### \ud83d\uddc2\ufe0f **Structured Output Conversion**\n\n* Since the AI outputs natural language, the system uses a **parser** to extract structured fields.\n* This step ensures that the response is normalized and can be cleanly inserted into a relational database.\n\n---\n\n## \ud83d\udce6 3. Enrichment & Persistent Storage\n\n### \ud83e\uddfe **Saving to PostgreSQL**\n\n* The structured data is stored in a **PostgreSQL database** with the following schema:\n\n * `task_name`: The name or summary of the task.\n * `priority_level`: Categorized as High, Medium, or Low.\n * `reason_for_priority`: A brief but clear explanation of why the task received this priority.\n\n### \ud83d\udcda **Contextual Intelligence with Vector Embeddings**\n\n* To make future decisions more intelligent, the system incorporates a **semantic memory layer** using **vector databases** and **embeddings**.\n* All task-YOUR_OPENAI_KEY_HERE text is converted into **numerical vector representations** using an embeddings model.\n* These embeddings are stored in a **vector database**, enabling the AI agent to:\n\n * Recall similar past tasks.\n * Understand historical context.\n * Find patterns across time and projects.\n\n### \ud83d\udd0d **Semantic Search & Retrieval**\n\n* When making prioritization decisions, the AI queries the vector database to retrieve **semantically similar tasks**.\n* This enables **context-aware decision-making**, allowing the AI to make better judgments based on past learnings, not just current inputs."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "8c201cdf-caaa-423e-ad38-f6ac7955ff32",
"connections": {
"COO Agent": {
"main": [
[
{
"node": "Save Prioritized Task",
"type": "main",
"index": 0
}
]
]
},
"Fetch Tasks": {
"main": [
[
{
"node": "COO Agent",
"type": "main",
"index": 0
}
]
]
},
"Daily 9AM Trigger": {
"main": [
[
{
"node": "Fetch Tasks",
"type": "main",
"index": 0
}
]
]
},
"Vector Query Model": {
"ai_languageModel": [
[
{
"node": "Task Knowledge Vector DB",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Task Reasoning Model": {
"ai_languageModel": [
[
{
"node": "COO Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Pinecone Vector Store": {
"ai_vectorStore": [
[
{
"node": "Task Knowledge Vector DB",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Priority Output Parser": {
"ai_outputParser": [
[
{
"node": "COO Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Generate Task Embeddings": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Task Knowledge Vector DB": {
"ai_tool": [
[
{
"node": "COO Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}
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.
openAiApipineconeApipostgres
Pro
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About this workflow
Replace manual task prioritization with intelligent AI reasoning that thinks like a Chief Operating Officer. This workflow automatically fetches your Asana tasks every morning, analyzes them using advanced AI models, and delivers the single most critical task with detailed…
Source: https://n8n.io/workflows/4578/ — original creator credit. Request a take-down →
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