This article provides a complete, practical walkthrough of the Http Stickynote Automation 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:
   Unlocking Free AI Research: Automating Deep Knowledge Discovery with Jina AI and n8n
   
   Meta Description:
   Discover how the "Open Deep Research 2.0" workflow uses n8n and Jina AI's DeepSearch to automate AI-powered research and generate insightful, structured reports—100% free, with no API key required.
   
   Keywords:
   AI research automation, Jina AI DeepSearch, automated research reports, n8n workflow, free AI tools, no-code automation, knowledge discovery, markdown formatting, open-source AI, AI for researchers
   
   Third-Party APIs Used:
   - Jina AI DeepSearch API (https://deepsearch.jina.ai)
   
   —
   
   Article:
   
   Open Deep Research 2.0: A No-Cost, Fully Automated AI Research Workflow with Jina AI and n8n
   
   In the ever-evolving landscape of automation and artificial intelligence, having access to powerful research tools is no longer a luxury reserved for data scientists or large enterprises. Enter Open Deep Research 2.0: a community-driven, fully automated workflow built in n8n that leverages Jina AI’s DeepSearch API to produce precise, structured, and insightful research reports—at zero cost and with no API key required.
   
   Developed by Leonard van Hemert, this workflow is a shining example of democratized AI innovation. By fusing the intuitive automation power of n8n with the deep semantic capabilities of Jina AI’s DeepSearch, this system opens the doors of academic-grade, AI-assisted research to hobbyists, students, startups, and anyone hungry for knowledge.
   
   🌟 Why This Matters
   
   Manual online research can be time-consuming and unstructured. Traditional AI-powered tools often come with paywalls, rate limits, and API key headaches. Open Deep Research 2.0 eliminates those barriers by offering a plug-and-play workflow that automates research queries, fetches analyzed insights, and packages them into clean, markdown-formatted reports—instantly and freely.
   
   🧠 How the Workflow Operates
   
   At its core, this workflow does four things:
   1. Accepts a user’s research prompt.
   2. Sends the query to Jina AI’s DeepSearch API for real-time semantic search and AI analysis.
   3. Cleans and formats the AI output into structured Markdown.
   4. Returns a professional-grade report ready for use.
   
   Let’s break down these steps in detail:
   
   Step 1: Research Input from the User  
   The process begins with a simple user-triggered chat interaction, handled via an n8n Langchain-compatible Chat Trigger Node. The user types a research topic or question, kicking off the automation.
   
   Step 2: DeepSearch AI Analysis  
   The input is sent as a POST request to Jina AI's DeepSearch API. Unlike generic search results, DeepSearch doesn’t return just a list of links—it returns semantically relevant insights, facts, and summaries. Moreover, the query is enriched with instructive prompts like:  
   “You are an advanced AI researcher that provides precise, well-structured, and insightful reports…”
   
   This ensures the response is not only accurate but also aligned with professional research standards.
   
   Step 3: Cleaning and Formatting the Output  
   The AI’s raw response comes as a data stream, containing multiple line-separated JSON chunks. The workflow includes a dedicated Code Node that systematically parses this stream. It:
   
   - Extracts the latest meaningful content block.
   - Formats footnotes and URLs for Markdown.
   - Removes unnecessary clutter.
   
   The output is polished, readable, and suitable for blog posts, academic whitepapers, or team briefings.
   
   Step 4: The Final Result  
   After formatting, the workflow outputs a clean Markdown report—well-structured, insightful, and ready for use. This end result is especially useful for generating summaries, competitive analyses, trend reports, and fact-based content without lifting a finger beyond typing your query.
   
   🚀 Built by the Community, for Everyone
   
   Leonard van Hemert created this tool with one mission: to democratize automated research. Not everyone has access to OpenAI API keys or enterprise-grade research tools. By using Jina AI—which currently offers free access to its DeepSearch—and n8n, a powerful open-source automation platform, this workflow paves the way for cost-free innovation.
   
   Leonard shared a message in the workflow itself:
   
   > “This workflow was created to democratize AI-powered research and make advanced automated knowledge discovery available to everyone, without API restrictions or cost barriers.”
   
   🤝 A Call to Action
   
   Open Deep Research 2.0 is more than a tool—it’s a movement. It invites developers, researchers, and digital thinkers to build, improve, and share their own versions. Whether you're a student researching climate change or an entrepreneur analyzing market trends, this tool provides the AI horsepower for digging deep, quickly.
   
   Want to collaborate or learn more from the creator? Follow Leonard van Hemert on LinkedIn to stay tuned with his future projects in AI and no-code automation.
   
   🔧 Getting Started
   
   To get this workflow running:
   
   1. Install or access n8n (cloud or self-hosted).
   2. Import the provided JSON file into your n8n workspace.
   3. Connect to the internet—no API keys needed.
   4. Trigger a research query via the chat interface.
   5. Copy, paste, or publish the final Markdown report.
   
   No API keys. No credit cards. No licenses.
   
   📌 Final Thoughts
   
   Free, powerful, and beautifully structured—Open Deep Research 2.0 redefines what’s possible with no-code AI. With just a few clicks, users can access research capabilities that rival commercial-grade tools, entirely within the open-source ecosystem.
   
   With growing interest in AI explainability and automated knowledge synthesis, tools like this are not just helpful—they are essential. Whether you're a researcher, teacher, student, or curious thinker, Open Deep Research 2.0 empowers you to find better answers, faster.
   
   Try it today and rediscover the joy of research—AI-powered and freely available to all.
   
   —  
   🧩 Powered by:  
   - n8n (https://n8n.io)  
   - Jina AI DeepSearch API (https://deepsearch.jina.ai)  
   
   👨‍💻 Developed by Leonard van Hemert  
   🔗 LinkedIn: https://www.linkedin.com/in/leonard-van-hemert/

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.