LangChane is a popular open source framework to build applications that integrate LLMs with external data sources,

like APIs, databases, or custom knowledge bases.

It's commonly used for chatbots, question answering systems, and workflow automation.

Its flexibility and extensibility have made it something of a standard for creating sophisticated

AI-driven software. Eric Fries is a founding engineer at LangChain, and he leads their

integrations and open-source efforts. Eric joins the podcast to talk about what inspired the

creation of Langchain, agentic flows versus chain flows, emerging patterns of agentic AI design, and much more.

This episode is hosted by Sean Falconer.

Check the show.

Glad to have you here.

I've used Langchane a bunch, so I'm excited to get into it and talk about it.

Yeah, me too.

So you were there from the beginning.

So I just want to kind of go back to that point from what you can recall.

Like what sort of for you and other people that were kind of part of that initial founding

team?

What inspired you to create lane chain?

Like how did you sort of identify this need for a framework that chain together LMs created

the subtraction layer?

Yeah, definitely.

And some important context there is that there's

kind of a distinction between the starting of the open source project, which Harrison did in October

...