Research ANYTHING and Get a PDF Report (free n8n template)

Research ANYTHING and Get a PDF Report (free n8n template)

by Nate Herk

Applied Sciences & Engineering AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 Nate Herk 👥 964K 📅 March 30, 2025 ⏱ 28 min 👁 75K 📄 tutorial 🧭 2026-08-28
Available in: English (current) Français

Keywords

n8nAI researchautomationPDF reportOpenRouter

Summary

The video presents a comprehensive n8n workflow designed to generate in-depth research reports in PDF format. The creator demonstrates the system with a live example on the topic ‘sugar for breakfast’, showing how the workflow plans chapters, conducts research using Tavily, writes content via AI agents, and compiles a 40-page PDF with sources. The workflow is structured in three main stages: planning, research & writing, and finalization. The planning stage uses an AI agent to break down the topic into five chapters and generate an introduction. Each chapter then runs a research phase using Tavily search, followed by a writer agent that produces HTML content with inline citations. Data is stored in Google Sheets to manage the large volume of content. The finalization stage aggregates all content, generates a table of contents, and uses an HTML-to-PDF API to create the final document, which is emailed. The creator highlights the cost efficiency, claiming each report costs around $0.50, and compares it to OpenAI’s Deep Research, noting faster speed and lower cost. The video also discusses limitations, such as the lack of parallel execution and manual updates across branches, and offers the template for free via a Skool community.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides high practical value by offering a free, reusable n8n template and a detailed breakdown of its architecture. The argumentation is solid, supported by live demonstrations and specific cost calculations. The creator explains design choices and trade-offs, such as using Google Sheets for data persistence and the limitations of sequential processing. The comparison with OpenAI Deep Research is relevant and highlights the workflow’s advantages in cost and speed, though it lacks a rigorous benchmark.

Scientific Rigor, Source Quality, Title Accuracy

The video demonstrates a high level of technical rigor, with clear explanations of each node and API integration. Sources are primarily the creator’s own workflow and the tools used (Tavily, OpenRouter, HTML to PDF API). The title accurately reflects the content. The description includes links to the creator’s community and affiliate links, but no external scientific sources are cited. The video does not claim to be a scientific study, but rather a practical tutorial, so the lack of peer-reviewed sources is acceptable.

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Title / Content Match

The title accurately describes the content: a tutorial on building an n8n workflow that generates PDF research reports.

Quality & Reliability

7/10

The video provides a detailed, transparent walkthrough of an n8n workflow, including system prompts, API usage, and cost breakdowns. Claims are supported by live demonstrations and specific numbers, though some external claims (e.g., cost comparisons) are not independently verified.

Chapters

Cited Sources

  • n8n partner link — Affiliate link to n8n, the automation platform used in the workflow.
  • Nate Herk LinkedIn — Creator's professional profile.
  • Skool community (paid) — Paid community for deeper AI learning.
  • Skool community (free) — Free community where the workflow template is shared.
  • Watch next video — Link to a related video by the same creator.

Concurring Sources

  • n8n documentation — Official documentation for n8n, the platform used in the workflow.

Contribution & Novelties

The video offers a practical, cost-effective alternative to proprietary deep research tools, with a fully transparent and customizable n8n workflow. It demonstrates how to orchestrate multiple AI agents and APIs to produce structured, source-linked PDF reports. The approach is innovative in its use of Google Sheets as a data store to handle large content volumes and its modular design.

Pour aller plus loin :

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Radar Profile

The radar profile shows high scores in information quantity and technical level, reflecting the detailed tutorial nature. Quality and reliability are slightly lower due to the lack of external verification and the promotional aspects. The overall balance indicates a practical, hands-on resource rather than a rigorous scientific analysis.

Reliability 7/10

💬 Très positif. Sur les 30 commentaires analysés, la grande majorité exprime une admiration et une gratitude pour le partage gratuit du workflow, certains le qualifiant de 'game changer' et de 'meilleur créateur n8n'.