This article provides a complete, practical walkthrough of the Stopanderror Splitout Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   DeepResearcher with n8n: Building an AI-Powered Recursive Research Agent
   
   Meta Description:  
   Discover how the DeepResearcher n8n workflow uses AI, web scraping, and automation to conduct in-depth research with minimal human input. Learn how topics are explored recursively and the results saved automatically to Notion.
   
   Keywords:  
   n8n workflow, DeepResearcher, AI research automation, recursive web scraper, OpenAI o3-mini, Apify API, Notion API, automated research, LLM workflows, LangChain n8n
   
   Third-Party APIs & Integrations Used:
   
   1. OpenAI API (model: o3-mini)
   2. Apify API
   3. Notion API
   4. Google Gemini (PaLM) API (optional alternative for AI block generation)
   
   Article:
   
   ---
   
   ### Building a Recursive AI Researcher with n8n: DeepResearcher Workflow Explained
   
   In an era of information overflow, finding concise, accurate, and useful insights on a topic requires significant time and mental bandwidth. Enter DeepResearcher — an AI-powered, multi-step recursive research bot built on the n8n automation platform that promises to conduct hours of deep investigation in a fraction of the time.
   
   This workflow, inspired by OpenAI’s DeepResearch feature, employs modern AI techniques, web scraping, and dynamic automation to perform intensive topic exploration—from surface-level facts to deeper contextual learnings—entirely autonomously. In this article, we’ll explore how the DeepResearcher workflow functions, the technologies involved, and how you can use or adapt it for your own research needs.
   
   ---
   
   ### What is DeepResearcher?
   
   DeepResearcher is an automation powered by n8n that intelligently:
   
   - Collects a user’s research query via a form
   - Automatically generates search engine queries
   - Uses a recursive loop to perform searches, scrape content, and summarize findings
   - Gathers "learnings" after each loop iteration with an LLM
   - Repeats the process a set number of times (determined by 'depth' and 'breadth')
   - Compiles a detailed, markdown-formatted report
   - Automatically writes the report into a Notion workspace using Notion’s API
   
   This workflow is designed to be fully self-operating after the initial user input—handling ambiguity, gathering web data, cleaning the results, summarizing, and finally archiving it neatly.
   
   ---
   
   ### Key Features
   
   #### 1. Smart Form Interface
   
   Using the n8n Form Trigger node, users provide:
   - A short description of the research topic
   - Desired “Depth” and “Breadth” values, which guide how many recursive loops and how many sub-queries will be explored
   - Acknowledgement of time/cost implications
   
   The UX is enhanced with custom HTML elements including sliders for easy configuration.
   
   #### 2. Clarifying Questions
   
   Before diving into research, an OpenAI model (via LangChain integration) asks up to 3 clarifying questions to refine ambiguous prompts. This dynamic form loop ensures high-quality query formulation, drawing on techniques from n8n’s “AI Interviewer” template.
   
   #### 3. Recursive Research Loop
   
   The core intelligence lies in its recursive loop. Here's how each cycle works:
   
   - AI generates SERP queries from the current research direction
   - These queries are run through the Apify Web Browser Actor to collect high-quality webpage content
   - The first-pass content is verified, cleaned, and parsed into markdown
   - From this markdown, learnings (key insights, facts, or summaries) are generated using the OpenAI o3-mini model
   - Each iteration accumulates learnings and proposes new follow-up research questions, which are then used to generate the next round of queries
   
   This loop continues until the user-defined depth is reached.
   
   #### 4. Notion Integration
   
   Before kicking off the search loop, a Notion page is created with status set as “In Progress.” The final detailed research report, when ready, is written back to this page in a highly readable format using Notion-compatible blocks (headings, lists, tables, etc.). The blocks are converted from markdown → HTML → Notion Block JSON using a mix of n8n’s built-in Markdown node and a language model for parsing.
   
   At the conclusion of the process, the Notion page is updated to “Done” and includes:
   - Final report in Notion-native structure
   - Organized sources
   - Timestamps and metadata
   
   ---
   
   ### Tech Stack and Nodes Involved
   
   Here are the main components and services powering DeepResearcher:
   
   - 📬 **OpenAI**: For all reasoning, clarifications, summarizations, and text-to-block transformations. Model used: `o3-mini`
   - 🔎 **Apify**: For SERP query execution and full web page content browser-based scraping
   - 📓 **Notion**: For report persistence and structured presentation
   - 🔄 **n8n**: As the workflow engine, orchestrating loops, forms, and API calls
   - 🧠 **LangChain n8n Nodes**: For integrating LLMs and structured output parsers
   - 🌎 **Google Gemini API**: (Optional) Alternative to OpenAI for parsing HTML to Notion block schemas
   
   ---
   
   ### Why Use DeepResearcher?
   
   This n8n workflow is ideal for:
   
   - Analysts researching new markets, technologies, or competitors
   - Content teams automating topic brief generation
   - Knowledge workers trying to navigate information dense topics
   - Students and researchers performing initial literature reviews
   - Anyone needing synthesized, structured knowledge fast
   
   Because it takes a recursive approach, each round of investigation becomes more targeted and insightful, closely resembling how skilled human researchers approach complex problems.
   
   ---
   
   ### Customization Ideas
   
   This template is flexible and highly extensible:
   
   - Swap Notion with Confluence, Obsidian, or Google Docs via APIs
   - Use other LLMs like Claude or Mistral if preferred
   - Integrate internal company wikis or APIs as additional data sources
   - Use Firecrawl.ai or Perplexity if you need AI-assisted search instead of Apify
   
   ---
   
   ### Final Thoughts
   
   DeepResearcher is a practical demonstration of what’s possible when automation meets reasoning-capable AI. By combining n8n’s powerful control flow with intelligent content transformation, DeepResearcher can reduce hours of manual research into a few clicks and a nicely formatted Notion report.
   
   If you’re ready to supercharge your research workflow and want something smarter than a search engine, this open-source approach is a great place to start.
   
   ---
   
   Want to try it yourself?  
   📚 Copy the Notion template: [DeepResearcher Notion DB](https://jimleuk.notion.site/19486dd60c0c80da9cb7eb1468ea9afd?v=19486dd60c0c805c8e0c000ce8c87acf)  
   🔧 Plug in your OpenAI and Apify API keys  
   🌐 Publish your n8n instance and launch your own AI Research Assistant today!
   
   Happy researching 🧠✨

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.