LangGraph Deep Dive: Agents with Parallel Function Calling

I reduced token usage by 87% by configuring my LangGraph agent to use parallel function calling.
In this video, I walk through the basics of parallel function calling in LLMs, then dive into code on how to build a custom LangGraph agent from scratch that utilizes parallel function calling with OpenAI and LangChain. And in the second half of the video, I also run through a step-by-step walkthrough of LangGraph to observe how the graph, nodes, and graph state work in extreme detail.
Interested in talking about a project? Reach out!
Email: [e-mail]
LinkedIn: linkedin.com/in/christianerice
Timestamps:
0:00 - Intro
0:52 - Concept Overview: Parallel Function Calling
7:26 - Code Example: A LangGraph Agent with Parallel Tools
16:04 - Code Deep Dive: LangGraph Step By Step
Follow along with the code here: github.com/christianrice/demo...

[@arthurwandzel7039]

I liked the overview you did before jumping into the code - so helpful! Most tutorials just jump a s s backwards into the code.

[@deployingai]

Thanks for the feedback!

[@joshuashepherd7189]

Wow, I can't believe how informative your videos are! I can tell you put alot of work into them. Definitely looking forward to more releases from you!!

[@JDWilsonJr]

Just found your channel, subscribed, and working through the demos. Thank you so much for taking the time to compose them and the generosity to share them.

[@GiovanneAfonso]

Great video!!!!

[@deployingai]

Thanks for watching!

[@J.AlexanderMarin-sp5gr]

Very nice. re: the UI component, could you please provide a bit more info? Thanks!