
Hermes Agent with Qwen3.6 (Local) | AI App Template with LangChain, LangGraph, llama.cpp | 🔴 Live
Venelin Valkov16 June 2026Watch on YouTube
Part of series
Ep. 2 · Cpp Llama Local
View the seriesWhat you'll learn
- Qwen 3.6 (35B) is deployed locally and tested in a practical AI agent application
- LangChain and LangGraph form the foundation for building a Hermes Agent template
- NextJS is used to integrate a fully working AI app with the local language model
- Learn how to implement a local LLM instead of cloud-based APIs for AI applications
Frequently asked questions
What is a Hermes Agent and how is it used in this tutorial?
Why would you choose a local model instead of a cloud API?
Which technologies are combined in this AI app template?
What is Qwen 3.6 and how does it perform compared to other models?
Topics
Description from the channel
We had our first look at Qwen3.6 (35B A3B) and compared it to Gemma 4 on some tests. Now is time to test if it is a good "workhorse" in Hermes Agent. We'll run it locally and build an AI app template that uses LangChain, Langraph and NextJS (part 1). Let's try it out! AI Academy: https://mlexpert.io/ Work with me: https://mlexpert.io/consulting LinkedIn: https://www.linkedin.com/in/venelin-valkov/ Follow me on X: https://twitter.com/venelin_valkov Discord: https://discord.gg/UaNPxVD6tv Subscribe: http://bit.ly/venelin-subscribe GitHub repository: https://github.com/curiousily/AI-Bootcamp 👍 Don't Forget to Like, Comment, and Subscribe for More Tutorials! Join this channel to get access to the perks and support my work: https://www.youtube.com/channel/UCoW_WzQNJVAjxo4osNAxd_g/join