Build Your First Research AI Agent in 12 mins (No Code)

Build Your First Research AI Agent in 12 mins (No Code)

by Nate Herk

Applied Sciences & Engineering AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 Nate Herk 👥 964K 📅 July 2, 2025 ⏱ 12 min 👁 83K 📄 tutorial 🧭 2026-08-28
Available in: English (current) Français

Keywords

n8nAI agentno-codePerplexityOpenRouter

Summary

This tutorial demonstrates how to build a research AI agent using n8n, a no-code automation platform, without writing any code. The agent uses OpenRouter as its ‘brain’ to access various AI models, and Perplexity as a tool to search the web and retrieve real-time information. The video walks through the entire setup process, including creating API keys for both services and configuring the necessary nodes in n8n. It shows how to connect a chat trigger to the agent, allowing users to ask questions and receive research reports. The tutorial also highlights the flexibility of the agent by demonstrating a more advanced use case: automatically researching competitors from a Google Sheet and writing the findings back. The author emphasizes the importance of system prompts to guide the agent’s behavior and mentions that the workflow can be adapted for various research tasks. The video concludes by promoting the author’s community and courses for further learning.

153 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides a practical, step-by-step guide that is easy to follow, making it valuable for beginners in AI automation. The argumentation is straightforward and based on a live demonstration, which adds credibility. The author clearly explains the purpose of each component (OpenRouter as the brain, Perplexity as the research tool) and shows how they integrate. The demonstration of a real-world use case (competitor research) effectively illustrates the agent’s potential. However, the video lacks critical discussion of limitations, such as cost implications, potential errors, or alternative approaches. The argumentation is more persuasive than analytical, focusing on the ease and power of the solution rather than a balanced evaluation.

Scientific Rigor, Source Quality, Title Accuracy

The tutorial is based on the author’s direct experience and provides a working demonstration, which is a form of primary evidence. However, the sources cited in the description are primarily promotional links to the author’s own products (courses, community, services) and affiliate links (e.g., Glaido, Hostinger). The video references Perplexity’s API documentation, but no specific academic or external sources are cited. The title accurately reflects the content, and the video delivers on its promise. The lack of external references and the promotional nature of the links slightly reduce the scientific rigor, but the practical demonstration compensates to some extent.

222 words

Title / Content Match

The title accurately reflects the content: a 12-minute no-code tutorial for building a research AI agent.

Quality & Reliability

7/10

The tutorial is clear, practical, and based on the author's direct experience. It demonstrates a working workflow and provides step-by-step instructions. However, it lacks in-depth technical explanations, does not discuss limitations or alternatives in detail, and the sources are primarily promotional links to the author's own products.

Chapters

Cited Sources

Concurring Sources

  • n8n AI Agent documentation — Official n8n documentation on AI agents, confirming the described functionality.
  • Perplexity API models — Official Perplexity documentation listing models like Sonar Deep Research, as used in the video.

Dissenting Sources

Contribution & Novelties

The video provides a clear, accessible tutorial for building a research AI agent without code, which is valuable for non-programmers. It demonstrates a practical integration of OpenRouter and Perplexity within n8n, showing how to create a flexible agent that can be adapted to various research tasks. The example of automating competitor research from a Google Sheet illustrates a real-world application, making the concept tangible. The video also emphasizes the importance of system prompts and shows how to leverage the agent’s ability to cite sources, which is a key feature for credibility.

Pour aller plus loin :

  • n8n documentation — Official documentation for n8n, useful for understanding nodes and workflows.
  • Perplexity API documentation — Official API docs for Perplexity, detailing models and parameters.
  • OpenRouter documentation — Official docs for OpenRouter, explaining model routing and API usage.
  • LangChain agents — A framework for building AI agents, offering a more code-based alternative.
  • AutoGPT — An open-source autonomous AI agent project, illustrating advanced agent capabilities.

161 words

Radar Profile

The radar profile shows a balanced performance across all dimensions, with slightly higher scores in information quantity and quality, reflecting the tutorial's practical value. The technical level is moderate, suitable for beginners, and the overall reliability is good due to the live demonstration. The profile suggests a solid, practical tutorial with room for deeper technical depth.

Reliability 7/10

💬 Très positif. Sur les 30 commentaires analysés, la majorité exprime une forte appréciation, saluant la clarté du tutoriel et son efficacité, avec quelques questions techniques et demandes de variantes.