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paoloanzn/AI_research_Agent.json

Created May 28, 2025 19:38
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AI Research Agent n8n Workflow
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AI_research_Agent.json
{
"name": "AI research Agent",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "3b9a89f9-44f5-4759-8e46-eeb418eec7a1",
"options": {}
},
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
-20,
-300
],
"id": "1220c91c-ab27-418f-8cc8-9a2f000e6030",
"name": "Webhook",
"webhookId": "3b9a89f9-44f5-4759-8e46-eeb418eec7a1"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1.2,
"position": [
1768,
140
],
"id": "8a878ba3-37f0-4676-b2fc-feeca6464d09",
"name": "Embeddings OpenAI",
"credentials": {
"openAiApi": {
"id": "Z3R8Ji2VnI8qLeU1",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"mode": "insert",
"tableName": {
"__rl": true,
"value": "documents",
"mode": "list",
"cachedResultName": "documents"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"typeVersion": 1.1,
"position": [
1784,
-80
],
"id": "ab45325b-8f7f-482d-8818-c718cdc0d314",
"name": "Supabase Vector Store",
"credentials": {
"supabaseApi": {
"id": "6rCvzKWRmCqAynOD",
"name": "Supabase account"
}
}
},
{
"parameters": {
"jsonMode": "expressionData",
"jsonData": "=",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"typeVersion": 1,
"position": [
1888,
142.5
],
"id": "7e1df73e-b102-4bd8-b5b1-5cbd0548d99f",
"name": "Default Data Loader"
},
{
"parameters": {
"separator": "={{ \"\\n\" }}"
},
"type": "@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter",
"typeVersion": 1,
"position": [
1976,
340
],
"id": "d8d89ef5-d4ef-46d6-a40c-f393fa3d0ece",
"name": "Character Text Splitter"
},
{
"parameters": {
"mode": "load",
"tableName": {
"__rl": true,
"value": "documents",
"mode": "list",
"cachedResultName": "documents"
},
"prompt": "={{ $json.choices[0].message.content }}",
"topK": 5,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"typeVersion": 1.1,
"position": [
576,
-5
],
"id": "580d7567-0f08-4e0f-b599-f726603fe762",
"name": "Supabase Vector Store Retriever",
"credentials": {
"supabaseApi": {
"id": "6rCvzKWRmCqAynOD",
"name": "Supabase account"
}
}
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1.2,
"position": [
664,
215
],
"id": "2a115d36-191e-4da1-8ac3-9a2ab55d02f5",
"name": "Embeddings OpenAI1",
"credentials": {
"openAiApi": {
"id": "Z3R8Ji2VnI8qLeU1",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4.1",
"mode": "list",
"cachedResultName": "GPT-4.1"
},
"messages": {
"values": [
{
"content": "=Using the following research data: \n```txt\n{{ $json['Expert Analysis'][0].json.choices[0].message.content }}\n```\n\nact as a team of 3 different experts: [Innovation Consultant], [Financial Analyst], and [Consumer Behavior Expert].\n\nProvide new angles and insights on [{{ $json.Webhook[0].json.body.topic }}] to complement the initial research. Suggest additional business ideas and refine existing ones.\n\nThese are documents retrived using vector similarity search to collect corralet content from previous researches (can be empty):\n{{ $json.formattedDocuments }}"
}
]
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1172,
-5
],
"id": "dca7e629-34b4-45aa-9822-d83b4eeaaa05",
"name": "Experts Review",
"credentials": {
"openAiApi": {
"id": "Z3R8Ji2VnI8qLeU1",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"jsCode": "const documents = $('Supabase Vector Store Retriever').all()\nlet formattedDocuments = \"\"\nfor (let document of documents) {\n const content = document.json.document.pageContent\n formattedDocuments += `Document:\\n\\`\\`\\`txt\\n${JSON.stringify(content)}\\n\\`\\`\\`\\n\\n`\n}\nreturn { formattedDocuments, 'Expert Analysis': $('Expert Analysis').all(), 'Webhook': $('Webhook').all() }"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
952,
-5
],
"id": "b3a7a116-d3f3-469b-acde-8894597688dd",
"name": "RAG Formatter",
"alwaysOutputData": true
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4.1",
"mode": "list",
"cachedResultName": "GPT-4.1"
},
"messages": {
"values": [
{
"content": "=Respond as if you're a team of 3 experts: [Market Researcher], [Business Strategist], and [Industry Veteran]. Each expert should contribute their unique perspective on [{{ $json.body.topic }}], then collaborate on the final answer. Provide a detailed analysis including market trends, strategic recommendations, and industry insights for generating 10+ business ideas."
}
]
},
"simplify": false,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
180,
-160
],
"id": "4d402c53-f3dd-4c9e-9b37-ddbb793897cc",
"name": "Expert Analysis",
"credentials": {
"openAiApi": {
"id": "Z3R8Ji2VnI8qLeU1",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"jsCode": "const expertAnalysisOutput = (() => {\n try {\n const input = $input.first().json.choices[0].message.content \n return input\n } catch (error) {\n return null\n }\n})();\n\nconst expertReviewOutput = (() => {\n try {\n const input = $input.first().json.message.content\n return input\n } catch (error) {\n return null\n }\n\n})();\n\nreturn {\n data: expertReviewOutput ?? expertAnalysisOutput\n}"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1580,
-160
],
"id": "40fb337f-ac3a-4858-9c36-23cf75a32513",
"name": "Data Parser",
"alwaysOutputData": true
},
{
"parameters": {
"modelId": {
"__rl": true,
"value": "gpt-4.1",
"mode": "list",
"cachedResultName": "GPT-4.1"
},
"messages": {
"values": [
{
"content": "=Using the initial research: \n```txt\n{{ $('Expert Analysis').last().json.choices[0].message.content }}\n```\nand additional perspectives:\n```txt\n{{ $json.message.content }}\n``` \n\ngenerate a final report for [{{ $('Webhook').last().json.body.topic }}]. Include 10+ business ideas with detailed descriptions, market analysis (trends, opportunities), competitive landscape, and step-by-step execution plans for each idea. Format the output as a structured markdown document. Output only the report, nothing else.\n\n"
}
]
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1820,
-320
],
"id": "991a6142-4b06-47b7-ae16-14be63fb4cd2",
"name": "OpenAI",
"executeOnce": false,
"credentials": {
"openAiApi": {
"id": "Z3R8Ji2VnI8qLeU1",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"content": "## How to run the workflow\n\nThe workflow can be triggered via remote POST request, making it flexible for any kind of integration/remote action\n\nYou need to send a POST request over the webhook with body containing the following data:\n```json\n{\"topic\": \"research topic\"}\n```\n",
"height": 260,
"width": 420
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
120,
-540
],
"id": "cc7529c4-fff7-4625-b865-49ca6b038a16",
"name": "Sticky Note"
},
{
"parameters": {
"operation": "sendDocument",
"binaryData": true,
"binaryPropertyName": "binary",
"additionalFields": {}
},
"type": "n8n-nodes-base.telegram",
"typeVersion": 1.2,
"position": [
2420,
-320
],
"id": "a4875070-d904-4f62-b9ab-86e7f6249e3b",
"name": "Telegram",
"webhookId": "cdb4ca6a-a9b6-4239-a37c-5695b6b26dba",
"credentials": {
"telegramApi": {
"id": "GkIBeM0cQICtGE13",
"name": "Telegram account"
}
}
},
{
"parameters": {
"content": "## Self-Learning feature\n\nThe workflow stores embeddings of previous researches to improve the learning process each time by implement a RAG system to retrieve contextual data",
"height": 260,
"width": 420,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
2160,
-120
],
"id": "a9fc90f5-46b3-406a-8d7a-ea1247627795",
"name": "Sticky Note1"
},
{
"parameters": {
"content": "## How to setup supabase vector storage\n\nThe setup is very simple, just create a standard free supabase project and in sql editor run this query to create the vector storage table:\n\n```sql\n-- Enable the pgvector extension to work with embedding vectors\ncreate extension vector;\n\n-- Create a table to store your documents\ncreate table documents (\n id bigserial primary key,\n content text, -- corresponds to Document.pageContent\n metadata jsonb, -- corresponds to Document.metadata\n embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed\n);\n\n-- Create a function to search for documents\ncreate function match_documents (\n query_embedding vector(1536),\n match_count int default null,\n filter jsonb DEFAULT '{}'\n) returns table (\n id bigint,\n content text,\n metadata jsonb,\n similarity float\n)\nlanguage plpgsql\nas $$\n#variable_conflict use_column\nbegin\n return query\n select\n id,\n content,\n metadata,\n 1 - (documents.embedding <=> query_embedding) as similarity\n from documents\n where metadata @> filter\n order by documents.embedding <=> query_embedding\n limit match_count;\nend;\n$$;\n```\n\n",
"height": 780,
"width": 880,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
840,
220
],
"id": "5c2cb9a3-6d4f-4063-afd9-2de3b77acc8b",
"name": "Sticky Note2"
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "b29f5208-7031-425b-b12c-e4cd69776054",
"leftValue": "={{ $json.body.topic }}",
"rightValue": "",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
}
},
{
"id": "bcac2f6e-e9f6-4f49-bf18-fa1b12bded75",
"leftValue": "={{ $json.message.content }}",
"rightValue": "",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
}
},
{
"id": "fdef2cb8-1f49-4846-9a66-b0782e6f8b04",
"leftValue": "={{ $json.choices[0].message.content }}",
"rightValue": "",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"type": "n8n-nodes-base.if",
"typeVersion": 2.2,
"position": [
1460,
-340
],
"id": "51f0c298-daf2-4ba7-b14d-939d81295747",
"name": "If"
},
{
"parameters": {
"operation": "toText",
"sourceProperty": "message.content",
"binaryPropertyName": "=binary",
"options": {
"encoding": "utf8",
"fileName": "={{ `report.md` }}"
}
},
"type": "n8n-nodes-base.convertToFile",
"typeVersion": 1.1,
"position": [
2180,
-320
],
"id": "a0fefee2-e859-45af-8637-ef45953a834e",
"name": "Convert to File"
},
{
"parameters": {
"content": "Message @get_id_bot to get your chat ID",
"height": 80,
"width": 300
},
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
2320,
-380
],
"id": "1354b9d1-bf51-4638-8939-5133c5357a19",
"name": "Sticky Note3"
}
],
"pinData": {},
"connections": {
"Webhook": {
"main": [
[
{
"node": "Expert Analysis",
"type": "main",
"index": 0
},
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Supabase Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store Retriever",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Supabase Vector Store Retriever": {
"main": [
[
{
"node": "RAG Formatter",
"type": "main",
"index": 0
}
]
]
},
"Experts Review": {
"main": [
[
{
"node": "Data Parser",
"type": "main",
"index": 0
},
{
"node": "OpenAI",
"type": "main",
"index": 0
},
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"Expert Analysis": {
"main": [
[
{
"node": "Supabase Vector Store Retriever",
"type": "main",
"index": 0
},
{
"node": "Data Parser",
"type": "main",
"index": 0
},
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"RAG Formatter": {
"main": [
[
{
"node": "Experts Review",
"type": "main",
"index": 0
}
]
]
},
"Data Parser": {
"main": [
[
{
"node": "Supabase Vector Store",
"type": "main",
"index": 0
}
]
]
},
"OpenAI": {
"main": [
[
{
"node": "Convert to File",
"type": "main",
"index": 0
}
]
]
},
"If": {
"main": [
[
{
"node": "OpenAI",
"type": "main",
"index": 0
}
]
]
},
"Convert to File": {
"main": [
[
{
"node": "Telegram",
"type": "main",
"index": 0
}
]
]
},
"Telegram": {
"main": [
[]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "a65aa255-e7d2-47fb-b560-5059cb72b797",
"meta": {
"templateCredsSetupCompleted": true,
"instanceId": "db3c2d3698a1d65c9632c318b0f8874c99c3c8ec38f9fa1684001dc1e7ae08d8"
},
"id": "q8FgZjvRFs5C77ZP",
"tags": []
}
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