[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Génère un workflow n8n: Host Your Own AI Deep Research Agent with n8n, Apify and OpenAI o3

[Assistant]
<thinking>
**Analyse du besoin**: This template attempts to replicate OpenAI's DeepResearch feature which, at time of writing, is only available to their pro subscribers.

An agent that uses reasoning to synthesize large amount of onl...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 64 nodes utilisant ai, google, http.
Structure: logique conditionnelle, traitement par lots, code personnalisé, intégration IA.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

{"meta":{"instanceId":"408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9","templateCredsSetupCompleted":true},"nodes":[{"id":"a342005e-a88e-419b-b929-56ecbba4a936","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[1300,1180],"parameters":{"schemaType":"manual","inputSchema":"{\n  \"type\": \"object\",\n  \"properties\": {\n    \"learnings\": {\n      \"type\": \"array\",\n      \"description\": \"List of learnings, max of 3.\",\n      \"items\": { \"type\": \"string\" }\n    },\n    \"followUpQuestions\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\",\n        \"description\": \"List of follow-up questions to research the topic further, max of 3.\"\n      }\n    }\n  }\n}"},"typeVersion":1.2},{"id":"126b8151-6d20-43b8-8028-8163112c4c5b","name":"Set Variables","type":"n8n-nodes-base.set","position":[-1360,-460],"parameters":{"options":{},"assignments":{"assignments":[{"id":"df28b12e-7c20-4ff5-b5b8-dc773aa14d4b","name":"request_id","type":"string","value":"={{ $execution.id }}"},{"id":"9362c1e7-717d-444a-8ea2-6b5f958c9f3f","name":"prompt","type":"string","value":"={{ $json['What would you like to research?'] }}"},{"id":"09094be4-7844-4a9e-af82-cc8e39322398","name":"depth","type":"number","value":"={{\n!isNaN($json['input-depth'][0].toNumber())\n  ? $json['input-depth'][0].toNumber()\n  : 1\n}}"},{"id":"3fc30a30-7806-4013-835d-97e27ddd7ae1","name":"breadth","type":"number","value":"={{\n!isNaN($json['input-breadth'][0].toNumber())\n  ? $json['input-breadth'][0].toNumber()\n  : 1\n}}"}]}},"typeVersion":3.4},{"id":"1d0fb87b-263d-46c2-b016-a29ba1d407ab","name":"OpenAI Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1120,1180],"parameters":{"model":{"__rl":true,"mode":"id","value":"o3-mini"},"options":{}},"typeVersion":1.2},{"id":"39b300d9-11ba-44f6-8f43-2fe256fe4856","name":"OpenAI Chat Model1","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[-860,1760],"parameters":{"model":{"__rl":true,"mode":"id","value":"o3-mini"},"options":{}},"typeVersion":1.2},{"id":"018da029-a796-45c5-947c-791e087fe934","name":"OpenAI Chat Model2","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[-1060,-300],"parameters":{"model":{"__rl":true,"mode":"id","value":"o3-mini"},"options":{}},"typeVersion":1.2},{"id":"525da936-a9eb-4523-b27a-ff6ae7b0e5ef","name":"Structured Output Parser1","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[-840,-300],"parameters":{"schemaType":"manual","inputSchema":"{\n  \"type\": \"object\",\n  \"properties\": {\n    \"questions\": {\n      \"type\": \"array\",\n      \"description\": \"Follow up questions to clarify the research direction, max of 3.\",\n      \"items\": {\n          \"type\": \"string\"\n      }\n    }\n  }\n}"},"typeVersion":1.2},{"id":"e6664883-cff4-4e09-881e-6b6f684f9cac","name":"On form submission","type":"n8n-nodes-base.formTrigger","position":[-1760,-460],"webhookId":"026629c8-7644-493c-b830-d9c72eea307d","parameters":{"options":{"path":"deep_research","ignoreBots":true,"buttonLabel":"Next"},"formTitle":" DeepResearcher","formFields":{"values":[{"fieldType":"html"}]},"formDescription":"=DeepResearcher is a multi-step, recursive approach using the internet to solve complex research tasks, accomplishing in tens of minutes what a human would take many hours.\n\nTo use, provide a short summary of what the research and how \"deep\" you'd like the workflow to investigate. Note, the higher the numbers the more time and cost will occur for the research.\n\nThe workflow is designed to complete independently and when finished, a report will be saved in a designated Notion Database."},"typeVersion":2.2},{"id":"6b8ebc08-c0b1-4af8-99cc-79d09eea7316","name":"Generate SERP Queries","type":"@n8n/n8n-nodes-langchain.chainLlm","position":[-1040,820],"parameters":{"text":"=Given the following prompt from the user, generate a list of SERP queries to research the topic.\nReduce the number of words in each query to its keywords only.\nReturn a maximum of {{ $('JobType Router').first().json.data.breadth }} queries, but feel free to return less if the original prompt is clear. Make sure each query is unique and not similar to each other: <prompt>{{ $('JobType Router').first().json.data.query.trim() }}</prompt>\n\n{{\n$('JobType Router').first().json.data.learnings.length\n  ? `Here are some learnings from previous research, use them to generate more specific queries:\\n${$('JobType Router').first().json.data.learnings.map(text => `* ${text}`).join('\\n')}`\n  : ''\n}}","messages":{"messageValues":[{"type":"HumanMessagePromptTemplate","message":"=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n  - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n  - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n  - Be highly organized.\n  - Suggest solutions that I didn't think about.\n  - Be proactive and anticipate my needs.\n  - Treat me as an expert in all subject matter.\n  - Mistakes erode my trust, so be accurate and thorough.\n  - Provide detailed explanations, I'm comfortable with lots of detail.\n  - Value good arguments over authorities, the source is irrelevant.\n  - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n  - You may use high levels of speculation or prediction, just flag it for me."}]},"promptType":"define","hasOutputParser":true},"typeVersion":1.5},{"id":"34e1fa5d-bc0c-4b9e-84a7-35db2b08c772","name":"Structured Output Parser2","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[-860,980],"parameters":{"schemaType":"manual","inputSchema":"{\n  \"type\": \"object\",\n  \"properties\": {\n    \"queries\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"query\": {\n            \"type\": \"string\",\n            \"description\": \"The SERP query\"\n          },\n          \"researchGoal\": {\n            \"type\": \"string\",\n            \"description\": \"First talk about the goal of the research that this query is meant to accomplish, then go deeper into how to advance the research once the results are found, mention additional research directions. 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\n
\n
\nTotal {{ $('Feedback to Items').all().length }} questions.\n
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Check your API token is valid and make sure you put \"Bearer <api_key>\" if using HeaderAuth."},"typeVersion":1}],"connections":{"Item Ref":{"main":[[{"node":"RAG Web Browser","type":"main","index":0}]]},"Create Row":{"main":[[{"node":"Initiate DeepResearch","type":"main","index":0}]]},"Confirmation":{"main":[[{"node":"End Form","type":"main","index":0}]]},"Has Content?":{"main":[[{"node":"Get Markdown + URL","type":"main","index":0}],[{"node":"Empty Response","type":"main","index":0}]]},"Valid Blocks":{"main":[[{"node":"Append Blocks","type":"main","index":0}]]},"Append Blocks":{"main":[[{"node":"For Each Block...","type":"main","index":0}]]},"HTML to Array":{"main":[[{"node":"Tags to Items","type":"main","index":0}]]},"SERP to Items":{"main":[[{"node":"For Each Query...","type":"main","index":0}]]},"Set Variables":{"main":[[{"node":"Clarifying Questions","type":"main","index":0}]]},"Tags to Items":{"main":[[{"node":"Notion Block Generator","type":"main","index":0}]]},"Valid 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Query":{"main":[[{"node":"Generate Learnings","type":"main","index":0}]]},"Accumulate Results":{"main":[[{"node":"Is Depth Reached?","type":"main","index":0}]]},"Generate Learnings":{"main":[[{"node":"Accumulate Results","type":"main","index":0}]]},"Get Markdown + URL":{"main":[[{"node":"DeepResearch Learnings","type":"main","index":0}]]},"On form submission":{"main":[[{"node":"Research Request","type":"main","index":0}]]},"OpenAI Chat Model1":{"ai_languageModel":[[{"node":"DeepResearch Report","type":"ai_languageModel","index":0}]]},"OpenAI Chat Model2":{"ai_languageModel":[[{"node":"Clarifying Questions","type":"ai_languageModel","index":0}]]},"OpenAI Chat Model3":{"ai_languageModel":[[{"node":"Generate SERP Queries","type":"ai_languageModel","index":0}]]},"OpenAI Chat Model4":{"ai_languageModel":[[{"node":"Report Page Generator","type":"ai_languageModel","index":0}]]},"DeepResearch Report":{"main":[[{"node":"Convert to HTML","type":"main","index":0}]]},"Clarifying 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Page":{"main":[[{"node":"For Each Block...","type":"main","index":0}],[]]},"DeepResearch Learnings":{"main":[[{"node":"Research Goal + Learnings","type":"main","index":0}]]},"Notion Block Generator":{"main":[[{"node":"Parse JSON blocks","type":"main","index":0}]]},"DeepResearch Subworkflow":{"main":[[{"node":"Execution Data","type":"main","index":0}]]},"Google Gemini Chat Model":{"ai_languageModel":[[{"node":"Notion Block Generator","type":"ai_languageModel","index":0}]]},"Structured Output Parser":{"ai_outputParser":[[{"node":"DeepResearch Learnings","type":"ai_outputParser","index":0}]]},"Research Goal + Learnings":{"main":[[{"node":"For Each Query...","type":"main","index":0}]]},"Structured Output Parser1":{"ai_outputParser":[[{"node":"Clarifying Questions","type":"ai_outputParser","index":0}]]},"Structured Output Parser2":{"ai_outputParser":[[{"node":"Generate SERP Queries","type":"ai_outputParser","index":0}]]},"Structured Output Parser4":{"ai_outputParser":[[{"node":"Report Page Generator","type":"ai_outputParser","index":0}]]}}}


[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Crée un workflow n8n pour: You still can use the app in a workflow even if we don’t have a node for that or the existing operation for that. With the HTTP Request node, it is possible to call any API point and use the incoming data in your workflow Main use cases: Connect with apps and services that n8n doesn’t have integration with Web scraping How it works This workflow can be divided into three branches, each serving a distinct purpose: 1.Splitting into Items (HTTP Request - Get Mock Albums): The workflow initiates with a manual trigger (On clicking 'execute'). It performs an HTTP request to retrieve mock albums data from "https://jsonplaceholder.typicode.com/albums." The obtained data is split into items using the Item Lists node, facilitating easier management. 2.Data Scraping (HTTP Request - Get Wikipedia Page

[Assistant]
<thinking>
**Analyse du besoin**: You still can use the app in a workflow even if we don’t have a node for that or the existing operation for that. With the HTTP Request node, it is possible to call any API point and use the incoming ...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 10 nodes utilisant http.
Structure: logique conditionnelle, flux standard.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

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[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Génère un workflow n8n: Pulling data from services that n8n doesn’t have a pre-built integration for

[Assistant]
<thinking>
**Analyse du besoin**: You still can use the app in a workflow even if we don’t have a node for that or the existing operation for that. With the HTTP Request node, it is possible to call any API point and use the incoming ...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 10 nodes utilisant http.
Structure: logique conditionnelle, flux standard.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

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[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Crée un workflow n8n pour: Automate your post-event networking with this intelligent n8n workflow. Triggered instantly after an event, it collects attendee and interaction data, enriches profiles with LinkedIn insights, and uses GPT-4 to analyze engagement and generate tailored follow-up messages. High-value leads are prioritized, messages are sent via email, LinkedIn, or Slack, and all activity is logged in your CRM and database. Save hours of manual follow-up while boosting relationship-building and ROI. 🤝✨ Advanced Features Webhook automation – Starts instantly on event completion Multi-Source Enrichment – Combines event data, interactions, and LinkedIn profiles AI-Powered Insights – GPT-4 analyzes behavior and suggests personalized talking points Smart Priority Filtering – Routes leads into High, Medium, and Low

[Assistant]
<thinking>
**Analyse du besoin**: Automate your post-event networking with this intelligent n8n workflow. Triggered instantly after an event, it collects attendee and interaction data, enriches profiles with LinkedIn insights, and use...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 20 nodes utilisant ai, database, http.
Structure: code personnalisé, intégration IA.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

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Analytics":{"main":[[{"node":"Send Response","type":"main","index":0}]]},"Slack Notification":{"main":[[{"node":"Save to Database","type":"main","index":0}]]},"Merge & Enrich Data":{"main":[[{"node":"AI Agent","type":"main","index":0}]]},"Enrich LinkedIn Data":{"main":[[{"node":"Merge & Enrich Data","type":"main","index":0}]]},"Filter High Priority":{"main":[[{"node":"AI Agent1","type":"main","index":0}]]},"Generate LinkedIn Msg":{"ai_languageModel":[[{"node":"AI Agent2","type":"ai_languageModel","index":0}]]},"Filter Medium Priority":{"main":[[{"node":"AI Agent2","type":"main","index":0}]]}}}


[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Génère un workflow n8n: Automate Event Follow-Ups with GPT-4, LinkedIn & HubSpot Multi-Channel Outreach

[Assistant]
<thinking>
**Analyse du besoin**: Automate your post-event networking with this intelligent n8n workflow. Triggered instantly after an event, it collects attendee and interaction data, enriches profiles with LinkedIn insights, and use...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 20 nodes utilisant ai, database, http.
Structure: code personnalisé, intégration IA.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

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LinkedIn","type":"main","index":0},{"node":"Slack Notification","type":"main","index":0}]]},"Send email":{"main":[[{"node":"Update CRM","type":"main","index":0}]]},"Update CRM":{"main":[[{"node":"Save to Database","type":"main","index":0}]]},"Get Attendees":{"main":[[{"node":"Merge & Enrich Data","type":"main","index":0}]]},"Send LinkedIn":{"main":[[{"node":"Update CRM","type":"main","index":0}]]},"Generate Email":{"ai_languageModel":[[{"node":"AI Agent1","type":"ai_languageModel","index":0}]]},"Webhook Trigger":{"main":[[{"node":"Get Attendees","type":"main","index":0},{"node":"Get Interactions","type":"main","index":0},{"node":"Enrich LinkedIn Data","type":"main","index":0}]]},"Get Interactions":{"main":[[{"node":"Merge & Enrich Data","type":"main","index":0}]]},"Save to Database":{"main":[[{"node":"Generate Analytics","type":"main","index":0}]]},"AI Analyze Profile":{"ai_languageModel":[[{"node":"AI Agent","type":"ai_languageModel","index":0}]]},"Generate 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[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Crée un workflow n8n pour: Learn Customer Onboarding Automation with n8n ✅ How It Works This smart onboarding automation handles new customer signups by: Receiving signup data via webhook Validating required customer info Creating a contact in HubSpot CRM Sending a personalized welcome email Delivering onboarding documents after 2 hours Sending a personal check-in email after 1 day Sending a Week 1 success guide after 3 days Updating CRM status and notifying the team at each milestone It’s designed for professional onboarding, with built-in timing, CRM integration, and smart notifications to improve engagement and retention. 🛠 Setup Steps Create Webhook Add a Webhook node in n8n with POST method — this triggers when a new customer signs up. Validate Customer Data Add an IF node to check if email and customerName ar

[Assistant]
<thinking>
**Analyse du besoin**: Learn Customer Onboarding Automation with n8n
✅ How It Works

This smart onboarding automation handles new customer signups by:

Receiving signup data via webhook
Validating required customer info
Cre...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 15 nodes utilisant nodes natifs.
Structure: logique conditionnelle, flux standard.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

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[System]
Tu es un expert n8n. Crée des workflows JSON complets et fonctionnels.
Utilise <thinking> pour expliquer ton raisonnement avant de générer le JSON.
Structure ton thinking: analyse du besoin, choix du trigger, architecture, flux de données, points d'attention.

[User] Génère un workflow n8n: Customer Onboarding Automation with HubSpot, Email Sequences and Team Alerts

[Assistant]
<thinking>
**Analyse du besoin**: Learn Customer Onboarding Automation with n8n
✅ How It Works

This smart onboarding automation handles new customer signups by:

Receiving signup data via webhook
Validating required customer info
Cre...

**Choix du trigger**: Sélection basée sur le cas d'usage décrit.

**Architecture**: 15 nodes utilisant nodes natifs.
Structure: logique conditionnelle, flux standard.

**Flux de données**: Connexions définies entre les nodes pour assurer le passage des données.

**Points d'attention**: Vérifier les credentials, tester chaque node individuellement.
</thinking>

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