Redowan Ahmed Farhan This workflow corresponds to n8n.io template #15514 — we link there as the canonical source.
This workflow follows the Agent → Documentdefaultdataloader recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "JMqGFtkkOnwqm13H",
"name": "Multi-Agent Research Pipeline ( Supabase + Notion)",
"tags": [],
"nodes": [
{
"id": "870c9e7b-9204-4845-8259-7725cb0b6618",
"name": "Start Research",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-1248,
352
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f810ba3b-edb0-4da8-becc-b83ae4a34ad5",
"name": "Groq Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGroq",
"position": [
800,
512
],
"parameters": {
"model": "llama-3.3-70b-versatile",
"options": {}
},
"credentials": {
"groqApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "bb7414f3-1333-4fd9-bd5a-b1f70374f0e6",
"name": "Code in JavaScript",
"type": "n8n-nodes-base.code",
"position": [
1232,
144
],
"parameters": {
"jsCode": "const output = $input.first().json.output || '';\nconst chunks = [];\nfor (let i = 0; i < output.length; i += 1900) {\n chunks.push(output.slice(i, i + 1900));\n}\n// Return first 5 chunks max (Notion also limits children blocks)\nreturn chunks.slice(0, 5).map(chunk => ({ json: { ...$input.first().json, chunk } }));"
},
"typeVersion": 2
},
{
"id": "48bbb50e-2782-47e2-8135-8988ab057793",
"name": "Set Research Topic1",
"type": "n8n-nodes-base.set",
"position": [
-992,
352
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "topic",
"type": "string",
"value": "AI Automation Trends 2026"
},
{
"id": "2",
"name": "depth",
"type": "string",
"value": "comprehensive"
},
{
"id": "3",
"name": "session_id",
"type": "string",
"value": "={{ $now.toMillis() }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "5006f2b0-5b27-43b8-915c-d9ea43061b77",
"name": "Parse Orchestrator Tasks1",
"type": "n8n-nodes-base.code",
"position": [
-768,
352
],
"parameters": {
"jsCode": "const raw = $input.first().json.output || '';\nlet tasks = {};\ntry {\n const m = raw.match(/\\{[\\s\\S]*\\}/);\n if (m) tasks = JSON.parse(m[0]);\n} catch(e) {}\nif (!tasks.search_query_1) {\n tasks = {\n summary_task: 'Summarize key developments and recent facts',\n analysis_task: 'Analyze emerging trends, patterns and implications',\n synthesis_task: 'Synthesize into actionable strategic insights',\n search_query_1: 'AI automation 2025 enterprise adoption trends',\n search_query_2: 'workflow automation machine learning ROI case studies',\n search_query_3: 'AI agent orchestration frameworks tools comparison'\n };\n}\nconst topicData = $('Set Research Topic1').first().json;\nreturn [{ json: { ...tasks, topic: topicData.topic, session_id: String(topicData.session_id) } }];"
},
"typeVersion": 2
},
{
"id": "3ebc28bd-c73b-4ea7-9b9f-5ac3da0f0de5",
"name": "Web Search Query ",
"type": "n8n-nodes-base.httpRequest",
"position": [
-544,
160
],
"parameters": {
"url": "https://api.tavily.com/search",
"method": "POST",
"options": {},
"jsonBody": "={\"api_key\":\"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\",\"query\":\"{{ $json.search_query_1 }}\",\"search_depth\":\"advanced\",\"max_results\":5,\"include_raw_content\":false}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "267f02a5-51bc-4782-a204-2d20d6565e74",
"name": "Merge Search Results1",
"type": "n8n-nodes-base.merge",
"position": [
-320,
272
],
"parameters": {
"numberInputs": 3
},
"typeVersion": 3.2
},
{
"id": "3d7ed53c-1969-443e-ae5f-752ef9778b38",
"name": "Web Search Query 4",
"type": "n8n-nodes-base.httpRequest",
"position": [
-544,
352
],
"parameters": {
"url": "https://api.tavily.com/search",
"method": "POST",
"options": {},
"jsonBody": "={\"api_key\":\"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\",\"query\":\"{{ $json.search_query_2 }}\",\"search_depth\":\"advanced\",\"max_results\":5,\"include_raw_content\":false}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "8c152adf-b532-43a7-b90a-47f2dd73cd57",
"name": "Web Search Query 5",
"type": "n8n-nodes-base.httpRequest",
"position": [
-544,
544
],
"parameters": {
"url": "https://api.tavily.com/search",
"method": "POST",
"options": {},
"jsonBody": "={\"api_key\":\"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\",\"query\":\"{{ $json.search_query_3 }}\",\"search_depth\":\"advanced\",\"max_results\":5,\"include_raw_content\":false}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "070ed35c-8d60-4514-867c-132b1a2e16d3",
"name": "Aggregate Research Context1",
"type": "n8n-nodes-base.code",
"position": [
-96,
288
],
"parameters": {
"jsCode": "const allItems = $input.all();\nconst snippets = [];\nfor (const item of allItems) {\n const data = item.json;\n if (data.results && Array.isArray(data.results)) {\n for (const r of data.results) {\n snippets.push('SOURCE: ' + (r.title || 'Untitled') + '\\n' + (r.url || '') + '\\n' + ((r.content || r.snippet || '')).substring(0, 500));\n }\n }\n}\nconst tasks = $('Parse Orchestrator Tasks1').first().json;\nreturn [{ json: { topic: tasks.topic, session_id: tasks.session_id, summary_task: tasks.summary_task, analysis_task: tasks.analysis_task, synthesis_task: tasks.synthesis_task, research_context: snippets.slice(0, 12).join('\\n\\n---\\n\\n'), source_count: snippets.length } }];"
},
"typeVersion": 2
},
{
"id": "a4ab5e02-7d69-44f7-b7a6-0dc75677a72b",
"name": "Summarizer Sub-Agent1",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
256,
64
],
"parameters": {
"text": "=RESEARCH TASK: {{ $json.summary_task }}\nTOPIC: {{ $json.topic }}\n\nSOURCE MATERIAL:\n{{ $json.research_context }}\n\nProvide a structured summary with:\n1. Key Facts & Statistics (with numbers)\n2. Major Players & Developments\n3. Current State of the Field\n4. Most Important Recent Events\n\nBe specific and cite sources.",
"options": {
"systemMessage": "You are a specialist research summarizer."
},
"promptType": "define"
},
"typeVersion": 3.1
},
{
"id": "c474ac40-d937-4081-a036-279f0d8ff5f4",
"name": "Summarizer LLM1",
"type": "@n8n/n8n-nodes-langchain.lmChatGroq",
"position": [
336,
288
],
"parameters": {
"model": "llama-3.3-70b-versatile",
"options": {}
},
"credentials": {
"groqApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "39c87e44-6f0f-46c4-9403-e8772660f722",
"name": "Combine Agent Outputs1",
"type": "n8n-nodes-base.merge",
"position": [
640,
288
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3.2
},
{
"id": "4f2df28b-d006-4961-b272-b2ed2d671c80",
"name": "Analyst Sub-Agent1",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
304,
448
],
"parameters": {
"text": "=ANALYSIS TASK: {{ $json.analysis_task }}\nTOPIC: {{ $json.topic }}\n\nSOURCE MATERIAL:\n{{ $json.research_context }}\n\nProvide deep analysis covering:\n1. Emerging Trends & Patterns\n2. Market/Industry Implications\n3. Key Risks and Challenges\n4. Competitive Landscape\n5. 12-Month Future Projections\n\nBe analytical, specific, and strategic.",
"options": {
"systemMessage": "You are a specialist research analyst."
},
"promptType": "define"
},
"typeVersion": 3.1
},
{
"id": "34c15150-41b5-4e51-bd16-67d733b63ef9",
"name": "OpenAi Analyst LLM1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
352,
672
],
"parameters": {
"model": "openai/gpt-oss-20b",
"options": {}
},
"credentials": {
"openRouterApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "16c5e97b-dcfb-4b0a-973b-d39c155e3894",
"name": "Synthesis Agent1",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
832,
288
],
"parameters": {
"text": "=You are the final synthesis agent. Create the definitive research report.\n\nTOPIC: {{ $(\"Set Research Topic1\").first().json.topic }}\n\nSUMMARY AGENT OUTPUT:\n{{ $json.output || \"\" }}\n\nCreate a final executive research report in markdown:\n\n# Executive Summary\n## Key Findings\n## Trend Analysis\n## Strategic Implications\n## Recommended Actions\n## Research Confidence",
"options": {
"systemMessage": "You are an expert intelligence analyst."
},
"promptType": "define"
},
"typeVersion": 3.1
},
{
"id": "ad432ae6-1cea-431c-808d-61ffe68e7b7a",
"name": "Save Report to Notion1",
"type": "n8n-nodes-base.notion",
"position": [
1424,
80
],
"parameters": {
"title": "={{ $(\"Set Research Topic1\").first().json.topic + \" \u2014 \" + $now.format(\"yyyy-MM-dd\") }}",
"blockUi": {
"blockValues": [
{
"textContent": "Executive Research Report"
},
{
"textContent": "={{ $json.chunk }}"
}
]
},
"options": {
"icon": "\ud83d\udd2c",
"iconType": "emoji"
},
"resource": "databasePage",
"databaseId": {
"__rl": true,
"mode": "url",
"value": "https://www.notion.so/35802106fce7800e8126c00c71bdb530?v=35802106fce7804fb9fc000c5d131b48&source=copy_link"
},
"authentication": "oAuth2"
},
"credentials": {
"notionOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 2.2
},
{
"id": "8c0d13b1-b0d3-4646-b342-e385662035f2",
"name": "Final Report Output1",
"type": "n8n-nodes-base.set",
"position": [
1232,
304
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1",
"name": "status",
"type": "string",
"value": "completed"
},
{
"id": "2",
"name": "topic",
"type": "string",
"value": "={{ $(\"Set Research Topic1\").first().json.topic }}"
},
{
"id": "3",
"name": "session_id",
"type": "string",
"value": "={{ $(\"Set Research Topic1\").first().json.session_id }}"
},
{
"id": "4",
"name": "agents_used",
"type": "string",
"value": "Orchestrator, Summarizer, Analyst, Synthesizer"
},
{
"id": "5",
"name": "report",
"type": "string",
"value": "={{ $(\"Synthesis Agent1\").first().json.output || \"Completed\" }}"
},
{
"id": "6",
"name": "completed_at",
"type": "string",
"value": "={{ $now.toISO() }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f06efcae-973e-4bb9-8acc-d1a66a89d001",
"name": "Supabase Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
1456,
304
],
"parameters": {
"mode": "insert",
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
},
{
"id": "0334fb0a-2c1a-4957-8c26-a9509224f130",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1472,
528
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "185d17ed-3e3e-4036-bc7f-4c616b61e148",
"name": "Default Data Loader1",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1600,
528
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "topic",
"value": "={{ $json.topic }}"
},
{
"name": "session_id",
"value": "={{ $json.session_id }}"
},
{
"name": "completed_at",
"value": "={{ $json.completed_at }}"
}
]
}
},
"jsonData": "={{ $json.report }}",
"jsonMode": "expressionData",
"textSplittingMode": "custom"
},
"typeVersion": 1.1
},
{
"id": "306311f7-de67-4356-a5be-029b95611ea2",
"name": "Recursive Character Text Splitter1",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1600,
736
],
"parameters": {
"options": {
"splitCode": "markdown"
},
"chunkOverlap": 200
},
"typeVersion": 1
},
{
"id": "355286cc-9888-4bed-b733-a7d88a473fa3",
"name": "Main Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2368,
-96
],
"parameters": {
"color": 7,
"width": 864,
"height": 1004,
"content": "## \ud83d\udd2c AI Research Agent \u2014 Multi-Source Intelligence Report\n\nThis workflow runs a fully automated multi-agent research pipeline that searches the web across three parallel queries, processes the results through specialized AI sub-agents, synthesizes a structured executive report, saves it to Notion, and indexes it in a Supabase vector store for semantic retrieval.\n\n**Perfect for:** Strategy teams, analysts, and knowledge managers who need on-demand, AI-generated intelligence reports on any topic.\n\n---\n\n## How it works\n\n1. **Start Research** \u2014 Manually triggers the research run.\n2. **Set Research Topic1** \u2014 Defines the research topic, depth level, and generates a unique session ID.\n3. **Parse Orchestrator Tasks1** \u2014 Extracts three tailored search queries and sub-agent task prompts from the topic config.\n4. **Web Search Query** \u2014 Executes the first Tavily advanced search (query 1).\n5. **Web Search Query 4** \u2014 Executes the second Tavily advanced search (query 2) in parallel.\n6. **Web Search Query 5** \u2014 Executes the third Tavily advanced search (query 3) in parallel.\n7. **Merge Search Results1** \u2014 Waits for all three searches and combines their outputs.\n8. **Aggregate Research Context1** \u2014 Flattens and deduplicates all search snippets into a single research context block.\n9. **Summarizer Sub-Agent1** \u2014 Groq Llama 3.3 70B agent that produces a structured factual summary with key statistics and developments.\n10. **Summarizer LLM1** \u2014 Provides the Groq Llama 3.3 70B language model to the Summarizer Sub-Agent.\n11. **Analyst Sub-Agent1** \u2014 OpenRouter GPT agent that performs deep trend analysis, risk assessment, and 12-month projections.\n12. **OpenAi Analyst LLM1** \u2014 Provides the OpenRouter GPT-OSS 20B model to the Analyst Sub-Agent.\n13. **Combine Agent Outputs1** \u2014 Merges the summarizer and analyst outputs side-by-side.\n14. **Groq Chat Model** \u2014 Provides the Groq Llama 3.3 70B model to the Synthesis Agent.\n15. **Synthesis Agent1** \u2014 Final Groq agent that writes the definitive executive research report in structured markdown.\n16. **Final Report Output1** \u2014 Packages the completed report with metadata (topic, session ID, agents used, timestamp).\n17. **Code in JavaScript** \u2014 Splits the report into 1,900-character chunks for Notion's block size limits.\n18. **Save Report to Notion1** \u2014 Creates a new database page in Notion and writes all report chunks as content blocks.\n19. **Supabase Vector Store1** \u2014 Inserts the report into a Supabase `documents` table as vector embeddings for semantic search.\n20. **Embeddings OpenAI1** \u2014 Generates OpenAI embeddings for the report text before insertion.\n21. **Default Data Loader1** \u2014 Loads the report JSON with metadata into the document pipeline.\n22. **Recursive Character Text Splitter1** \u2014 Splits the report text using markdown-aware chunking with 200-token overlap.\n\n---\n\n## Setup (~15 minutes)\n\n1. **Tavily API** \u2014 Replace the hardcoded `api_key` value in *Web Search Query*, *Web Search Query 4*, and *Web Search Query 5* with your own key from tavily.com.\n2. **Groq API** \u2014 Add your Groq credential in the *Summarizer LLM1* and *Groq Chat Model* nodes.\n3. **OpenRouter API** \u2014 Add your OpenRouter credential in the *OpenAi Analyst LLM1* node.\n4. **Notion OAuth2** \u2014 Connect your Notion account and set the correct `databaseId` in the *Save Report to Notion1* node.\n5. **Supabase** \u2014 Add your Supabase credential and confirm the `documents` table exists with a vector column in *Supabase Vector Store1*.\n6. **OpenAI Embeddings** \u2014 Add your OpenAI API key (or use AI Gateway) in the *Embeddings OpenAI1* node.\n> \u26a0\ufe0f The Tavily API key in this workflow is a dev/demo key \u2014 replace it before production use. Groq and OpenRouter usage may incur costs depending on your plan."
},
"typeVersion": 1
},
{
"id": "04d1aea2-de68-4c96-8e2e-1721aa6ee727",
"name": "Section 1: Trigger & Topic Setup",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1456,
160
],
"parameters": {
"color": 5,
"width": 792,
"height": 396,
"content": "## 1\ufe0f\u20e3 Trigger & Topic Setup\n\nThe **Start Research** node manually kicks off the workflow on demand. **Set Research Topic1** defines the target research subject (defaulting to \"AI Automation Trends 2026\"), the desired depth, and stamps the run with a unique millisecond session ID. **Parse Orchestrator Tasks1** then decodes those settings into three distinct Tavily search queries and individual task descriptions for the downstream summarizer, analyst, and synthesis agents."
},
"typeVersion": 1
},
{
"id": "3ccc0813-56c6-4a52-a570-759f7903035b",
"name": "Section 2: Parallel Web Research",
"type": "n8n-nodes-base.stickyNote",
"position": [
-624,
0
],
"parameters": {
"color": 3,
"width": 792,
"height": 716,
"content": "## 2\ufe0f\u20e3 Parallel Web Research\n\n**Web Search Query**, **Web Search Query 4**, and **Web Search Query 5** fire simultaneously against the Tavily advanced search API, each targeting a different angle of the research topic and returning up to five high-quality results. **Merge Search Results1** collects all three streams and passes the combined data to **Aggregate Research Context1**, which flattens all snippets into a single, deduplicated research context block (capped at 12 sources) ready for AI processing."
},
"typeVersion": 1
},
{
"id": "f677e39e-5ba2-404a-adf7-d057ed77c1d7",
"name": "Section 3: Multi-Agent AI Analysis",
"type": "n8n-nodes-base.stickyNote",
"position": [
192,
-112
],
"parameters": {
"color": 6,
"width": 952,
"height": 920,
"content": "## 3\ufe0f\u20e3 Multi-Agent AI Analysis\n\nThe aggregated research context is split into two parallel AI tracks. **Summarizer Sub-Agent1** (powered by **Summarizer LLM1**, Groq Llama 3.3 70B) extracts key facts, statistics, and major developments into a structured summary. Concurrently, **Analyst Sub-Agent1** (powered by **OpenAi Analyst LLM1**, OpenRouter GPT-OSS 20B) performs deep trend analysis, competitive landscape assessment, and 12-month projections. **Combine Agent Outputs1** merges both outputs, which are then passed to **Synthesis Agent1** \u2014 backed by **Groq Chat Model** (Llama 3.3 70B) \u2014 to produce the final structured executive research report in markdown format."
},
"typeVersion": 1
},
{
"id": "e93c2e18-9865-4586-a2ea-8120b09875b9",
"name": "Section 4: Output & Vector Storage",
"type": "n8n-nodes-base.stickyNote",
"position": [
1184,
-144
],
"parameters": {
"color": 4,
"width": 708,
"height": 1028,
"content": "## 4\ufe0f\u20e3 Output & Vector Storage\n\n**Final Report Output1** bundles the completed report with full metadata including topic, session ID, and a list of agents used. **Code in JavaScript** then chunks the report into 1,900-character segments to comply with Notion's block limits, and **Save Report to Notion1** writes each chunk as a content block under a new timestamped database page. In parallel, **Supabase Vector Store1** persists the full report as vector embeddings \u2014 using **Embeddings OpenAI1** for embedding generation, **Default Data Loader1** to attach metadata, and **Recursive Character Text Splitter1** to split the markdown text with 200-token overlap \u2014 enabling future semantic search and retrieval."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"executionOrder": "v1"
},
"versionId": "2abe98ab-4998-482f-88c3-b13c1e6bd30d",
"connections": {
"Start Research": {
"main": [
[
{
"node": "Set Research Topic1",
"type": "main",
"index": 0
}
]
]
},
"Groq Chat Model": {
"ai_languageModel": [
[
{
"node": "Synthesis Agent1",
"type": "ai_languageModel",
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}
]
]
},
"Summarizer LLM1": {
"ai_languageModel": [
[
{
"node": "Summarizer Sub-Agent1",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Synthesis Agent1": {
"main": [
[
{
"node": "Final Report Output1",
"type": "main",
"index": 0
},
{
"node": "Code in JavaScript",
"type": "main",
"index": 0
}
]
]
},
"Web Search Query ": {
"main": [
[
{
"node": "Merge Search Results1",
"type": "main",
"index": 0
}
]
]
},
"Analyst Sub-Agent1": {
"main": [
[
{
"node": "Combine Agent Outputs1",
"type": "main",
"index": 1
}
]
]
},
"Code in JavaScript": {
"main": [
[
{
"node": "Save Report to Notion1",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Web Search Query 4": {
"main": [
[
{
"node": "Merge Search Results1",
"type": "main",
"index": 1
}
]
]
},
"Web Search Query 5": {
"main": [
[
{
"node": "Merge Search Results1",
"type": "main",
"index": 2
}
]
]
},
"OpenAi Analyst LLM1": {
"ai_languageModel": [
[
{
"node": "Analyst Sub-Agent1",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Set Research Topic1": {
"main": [
[
{
"node": "Parse Orchestrator Tasks1",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader1": {
"ai_document": [
[
{
"node": "Supabase Vector Store1",
"type": "ai_document",
"index": 0
}
]
]
},
"Final Report Output1": {
"main": [
[
{
"node": "Supabase Vector Store1",
"type": "main",
"index": 0
}
]
]
},
"Merge Search Results1": {
"main": [
[
{
"node": "Aggregate Research Context1",
"type": "main",
"index": 0
}
]
]
},
"Summarizer Sub-Agent1": {
"main": [
[
{
"node": "Combine Agent Outputs1",
"type": "main",
"index": 0
}
]
]
},
"Combine Agent Outputs1": {
"main": [
[
{
"node": "Synthesis Agent1",
"type": "main",
"index": 0
}
]
]
},
"Parse Orchestrator Tasks1": {
"main": [
[
{
"node": "Web Search Query ",
"type": "main",
"index": 0
},
{
"node": "Web Search Query 4",
"type": "main",
"index": 0
},
{
"node": "Web Search Query 5",
"type": "main",
"index": 0
}
]
]
},
"Aggregate Research Context1": {
"main": [
[
{
"node": "Summarizer Sub-Agent1",
"type": "main",
"index": 0
},
{
"node": "Analyst Sub-Agent1",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader1",
"type": "ai_textSplitter",
"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.
groqApinotionOAuth2ApiopenAiApiopenRouterApisupabaseApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
How it works A manual trigger sets the research topic and generates three parallel Tavily web search queries A Summarizer Agent (Groq Llama 3.3 70B) and an Analyst Agent (OpenRouter GPT) process the search results simultaneously, producing a factual summary and a deep trend…
Source: https://n8n.io/workflows/15514/ — original creator credit. Request a take-down →
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