Build an AI Agent for Report Generation with NVIDIA Nemotron on OpenRouter | Nemotron Labs

Build an AI Agent for Report Generation with NVIDIA Nemotron on OpenRouter | Nemotron Labs

by NVIDIA Developer

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence
🎙 NVIDIA Developer 👥 222K 📅 September 16, 2025 ⏱ 37 min 👁 5K 📄 tutorial 🧭 2026-08-13
Available in: English (current) Français

Keywords

AI agentNVIDIA NemotronOpenRouterLangGraphreport generation

Summary

This livestream tutorial from NVIDIA Developer demonstrates how to build an AI agent for report generation using NVIDIA Nemotron models accessed through OpenRouter. The session is hosted by Chris Alexi (PR at NVIDIA) and features Shashank, an engineer at OpenRouter. They begin by showing a live demo of the agent generating a report on AI agents in 2025, highlighting its research and writing capabilities. The tutorial then covers the core components: setting up secrets, using the Brev environment, and building the agent with LangGraph. The agent architecture includes a research agent that uses Tavily for web search, and an author agent that dynamically generates report sections. Key technical points include using OpenRouter as a drop-in replacement for OpenAI API, configuring the Nemotron Nano v2 model with reasoning mode toggling, and managing state in LangGraph. The session also addresses viewer questions about OpenRouter’s hosted nature, template-based generation, and benchmarking capabilities. The tutorial is practical, with code walkthroughs and live demonstrations, making it accessible for beginners while providing valuable insights for intermediate developers.

171 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides high practical value by offering a step-by-step guide to building a functional AI agent. It demonstrates real code and explains the reasoning behind architectural choices, such as using a fast model like Nemotron Nano v2 for research tasks. The argumentation is solid, grounded in live demonstrations and concrete examples. The collaboration with an OpenRouter engineer adds credibility and provides insights into the platform’s capabilities. However, the video lacks deep theoretical discussion and does not critically evaluate alternative approaches or potential limitations.

93 words

Title / Content Match

The title accurately reflects the content: building an AI agent for report generation using NVIDIA Nemotron and OpenRouter.

Quality & Reliability

8/10

The content is a practical tutorial from NVIDIA Developer, featuring an engineer from OpenRouter. It demonstrates real code and provides technical details, but lacks formal citations and peer-reviewed sources.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

  • GTC DC 2025 — Mentioned as an upcoming NVIDIA conference for further learning.

Concurring Sources

Contribution & Novelties

The video provides a practical, hands-on tutorial for building an AI agent for report generation, combining NVIDIA Nemotron models with OpenRouter’s unified API. It offers a clear architectural pattern (research-plan-write) and demonstrates how to implement it with LangGraph. The collaboration with OpenRouter provides unique insights into model deployment and API design.

Pour aller plus loin :

  • LangGraph documentation — Official documentation for LangGraph, the framework used for building the agent.
  • OpenRouter documentation — Official documentation for OpenRouter, covering API usage and model routing.
  • NVIDIA Nemotron models — Official page for NVIDIA Nemotron models, including Nano v2.
  • Tavily Search API — Official site for Tavily, the web search tool used in the tutorial.

112 words

Radar Profile

The radar profile shows high scores in information quality and reliability, with moderate scores in quantity and technical level. This indicates a well-executed tutorial that is trustworthy but may not be exhaustive in depth or breadth.

Reliability 8/10

💬 No comments were provided for analysis.