This article provides a complete, practical walkthrough of the Http Stickynote Automate 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:**  
   Open Deep Research: Automating AI-Driven Investigations Using n8n
   
   **Meta Description:**  
   Discover how the "Open Deep Research" workflow leverages n8n, AI language models, and web automation tools like SerpAPI and Jina AI to create autonomous, end-to-end research pipelines. From search query generation to a comprehensive Markdown report, this intelligent workflow transforms inquiries into actionable insights.
   
   **Keywords:**  
   n8n, AI research automation, Langchain, OpenRouter API, Jina AI, SerpAPI, LLM workflows, autonomous research tool, AI workflow automation, automated report generation, research assistant AI, GPT automation
   
   ---
   
   # Open Deep Research: Automating AI-Driven Investigations Using n8n
   
   As the need for reliable and rapid information retrieval continues to grow across industries, automating the research process becomes not just an advantage—but a necessity. Enter Open Deep Research, a powerful AI-powered autonomous research workflow designed with n8n, LangChain, and a suite of third-party AI tools. This automation pipeline transforms user queries into well-structured research reports in Markdown format by chaining together AI capabilities and real-time web search technologies.
   
   Let's delve inside the architecture of this workflow and explore how it intelligently handles the end-to-end process of research—from interpreting a question to submitting it through multiple stages of logic, scraping, analysis, and final report generation.
   
   ---
   
   ## Workflow Overview
   
   The "Open Deep Research" workflow is a multi-step automation built in n8n, a popular node-based workflow automation tool. Designed to handle everything from initial user queries to delivery of structured outputs, its powerful combination of large language models (LLMs), APIs, and parsing logic makes it ideal for academic researchers, content creators, analysts, and knowledge workers who want quick yet comprehensive responses.
   
   Here’s a breakdown of each major component.
   
   ---
   
   ## 1. Query Ingestion via Chat Message Trigger  
   It all begins with a chat-based user input represented by the "Chat Message Trigger" node. This makes the workflow suitable for chatbot integrations or custom front-end UIs.
   
   ---
   
   ## 2. Query Expansion: Generating Search Phrases with LLM  
   Using LangChain's chainLlm node, powered by OpenRouter's LLM backend (using Google Gemini), the system takes the user’s raw question and converts it into four distinct and targeted search phrases. These help diversify the research scope and ensure rich coverage of the topic across sources.
   
   ---
   
   ## 3. Parsing & Distribution  
   The output list of queries is parsed with a custom JavaScript code node, then split into batches for execution against real-time search engines. This includes chunking logic to optimize API processing in later stages.
   
   ---
   
   ## 4. Real-Time Search via SerpAPI  
   The workflow connects to SerpAPI—an API that delivers real-time Google Search data— to retrieve organic search results. Each query batch feeds into an HTTP Request node set to fetch results from Google based on the generated keywords.
   
   ---
   
   ## 5. Extracting and Cleaning Web Links  
   The search results are captured and formatted to extract key attributes: page title, source URL, and content provider. This is vital for the next step, where each link’s content is analyzed.
   
   ---
   
   ## 6. Contextual Extraction Using Jina AI  
   For each URL, the content is fetched and analyzed using Jina AI. This tool performs zero-click summarizations by fetching and condensing content from the web page directly without traditional scraping. Results are then processed by a LangChain agent that extracts only contextually relevant information based on the initial user query.
   
   ---
   
   ## 7. Context Aggregation and Report Writing  
   Once enough relevant context is gathered across multiple sources, the information is merged and sent to another Langchain-powered LLM agent. It uses a structured Markdown prompt to generate a fully formatted research report. The format typically includes:
   
   - Title with user query
   - Key findings
   - In-depth analysis categorized by topics
   - Cited sources
   
   ---
   
   ## 8. Memory Buffer for Session Continuity  
   To maintain coherence across asynchronous executions, the workflow utilizes LangChain's memory buffer. It holds prior LLM dialog data (both input and output) so that context can be reused in ongoing or future conversations during the session.
   
   ---
   
   ## 9. Optional Wikipedia Integration  
   In cases where additional encyclopedic knowledge is required or source web pages are sparse, the workflow incorporates a Wikipedia tool node. It enhances the AI report by providing universally trusted, structured information.
   
   ---
   
   ## Final Output: AI-Generated Research Report  
   The result of this entire pipeline is a Markdown-formatted report delivered in a clear, readable structure, suitable for publishing or conversion to other formats like PDF or HTML. Typical use cases include:
   
   - Market intelligence briefs
   - Technology landscape analyses
   - Health and medical topic summaries
   - Educational content generation
   
   ---
   
   ## Major Technologies & APIs Used
   
   To successfully automate this sophisticated AI research system, several powerful tools and APIs were integrated:
   
   ### ✅ Third-Party APIs:
   1. **SerpAPI**  
      - Used for retrieving Google organic search results in real-time.  
      - Website: [https://serpapi.com](https://serpapi.com)
   
   2. **Jina AI Zero-Click Access**  
      - Summarizes any article or web page using a single URL.  
      - Website: [https://jina.ai](https://jina.ai)
   
   3. **OpenRouter API**  
      - A unified interface to multiple large language models including Google Gemini, Anthropic Claude, Meta LLaMA, etc.  
      - Website: [https://openrouter.ai](https://openrouter.ai)
   
   4. **Wikipedia Tool via LangChain**  
      - Fetches relevant encyclopedia entries.  
      - Integrated as a built-in AI tool through LangChain.
   
   ---
   
   ## The Power of Open Deep Research
   
   With n8n acting as the orchestration layer, Open Deep Research is more than a tech demo—it’s a practical, scalable tool for any knowledge-driven workflow. Its modular design makes it extensible, allowing for plugin of different AI models, sources, and even front-end integrations based on the user’s application domain.
   
   This kind of autonomous intelligence reshapes how we think about discovery—no longer a manual, time-consuming process, but an orchestrated dance between multiple AI agents and real-time data engines. It’s not merely assisting research—it’s actively doing it.
   
   ---
   
   Experience the future of AI-augmented research with workflows like Open Deep Research. Efficient, expansive, and entirely automated.

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.