Back to Catalog

Deep research agent - automated research & Notion report builder

Aziz BAziz B
2581 views
2/3/2026
Official Page

Overview

  • This workflow acts as an AI-powered research assistant that takes a topic from the user, performs multi-step intelligent research, and stores the final report in Notion. It uses advanced search, content extraction, and AI summarization to deliver a high-quality research report—fully automated from query to publication.

How It Works

  • User Interaction

    • The workflow starts by asking the user what topic they want to research.
    • A “Strategy Agent” asks 2–3 clarifying questions to refine the scope.
    • Once the user confirms, it creates a Notion database page with the research title.
  • Search Query Generation

    • Generates up to 3 relevant search queries for the given topic.
  • Data Gathering (Loop over each query)

    • Sends the query to Tavily Search API to find the most relevant blogs/articles.
    • Picks the top-matched link and uses Tavily again to extract its content.
    • Repeats the process for all 3 queries.
  • Report Compilation

    • Aggregates extracted content from all sources.
    • A Final Report Agent creates a well-structured research report in Markdown.
    • Converts Markdown → HTML → splits into chunks.
    • Pushes each chunk into the Notion report page.
  • Delivery

    • Sends the final Notion report link back to the user.

How to Use

  • This workflow is triggered via Webhook.
  • Attach the provided webhook URL to any application, form, or chatbot to collect the user’s topic.
  • Once triggered, the workflow will run automatically and deliver the research link without any manual steps.

Requirements

To use this workflow, you’ll need:

  • n8n account (self-hosted or cloud)
  • Notion account with a database where reports will be stored
  • Tavily API Key – for search & content extraction
  • OpenRouter API key or OpenAI API key – for AI agents & report generation
  • Google Gemini API Key – for converting Markdown to HTML and splitting content for Notion
  • Notion database ID connected in n8n
# Deep Research Agent - Automated Research & Notion Report Builder

This n8n workflow automates the process of conducting deep research on a given topic and compiling the findings into a structured report within Notion. It leverages AI agents to perform the research, summarize information, and generate a comprehensive output, streamlining the knowledge acquisition and documentation process.

## What it does

1.  **Receives Research Request**: Listens for an incoming webhook trigger, which is expected to contain the research topic.
2.  **Initial Data Preparation**: Processes the incoming request to set up the necessary fields for the AI agent.
3.  **Initiates AI Research Agent**: Calls an AI Agent (likely powered by LangChain and an LLM like OpenAI or Google Gemini) to perform deep research on the specified topic.
4.  **Extracts Structured Output**: Utilizes a Structured Output Parser to extract key information from the AI agent's response in a defined format.
5.  **Generates Markdown Report**: Converts the extracted research findings into a well-formatted Markdown report.
6.  **Creates Notion Page**: Creates a new page in Notion, using the generated Markdown content as the body of the report.
7.  **Responds to Webhook**: Sends a response back to the triggering webhook, indicating the completion of the research and Notion page creation.

## Prerequisites/Requirements

To use this workflow, you will need:

*   **n8n Instance**: A running n8n instance.
*   **Webhook Trigger**: An external system or application to send research requests via a webhook.
*   **AI Agent Credentials**:
    *   **OpenAI API Key** OR
    *   **Google Gemini API Key** OR
    *   **OpenRouter API Key** (depending on which LLM is configured in the "AI Agent" and "Chat Model" nodes).
*   **Notion Integration**: A Notion integration with appropriate permissions to create pages in your desired database or workspace. You will need to configure a Notion credential in n8n.

## Setup/Usage

1.  **Import the Workflow**:
    *   Download the provided JSON file.
    *   In your n8n instance, go to "Workflows" and click "New".
    *   Click the three dots menu (`...`) and select "Import from JSON".
    *   Paste the workflow JSON or upload the file.
2.  **Configure Credentials**:
    *   Locate the "AI Agent" node and its associated "Chat Model" node (e.g., "OpenAI", "Google Gemini Chat Model", or "OpenRouter Chat Model"). Configure the necessary API key credentials for your chosen Large Language Model.
    *   Locate the "Notion" node. Configure your Notion API key credential, ensuring it has access to the Notion database or page where you want to create reports.
3.  **Activate the Webhook**:
    *   The "Webhook" node is the trigger for this workflow. Once the workflow is active, it will provide a unique URL. Copy this URL.
4.  **Send Research Requests**:
    *   Send POST requests to the copied Webhook URL with a JSON body containing the research topic. For example:
        ```json
        {
          "topic": "The impact of AI on customer service in 2024"
        }
        ```
5.  **Run the Workflow**:
    *   Ensure the workflow is activated in n8n.
    *   Upon receiving a webhook request, the workflow will execute, conduct the research, and create a Notion page.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Automate Dutch Public Procurement Data Collection with TenderNed

TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte

Wessel BulteBy Wessel Bulte
247

AI-powered code review with linting, red-marked corrections in Google Sheets & Slack

Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.

higashiyama By higashiyama
90