Open sourcing AI app development with Harrison Chase from LangChain

**Sarah** (0:05)
Hi, listeners, and welcome to another episode of No Priors. Today, we're talking to Harrison Chase, the CEO and co-founder of LangChain, a popular open-source framework and developer toolkit that helps people build LLM applications.
We're excited to talk to Harrison about the state of AI application development, the open-source ecosystem, and its open questions.
Welcome, Harrison.

**Harrison Chase** (0:25)
Thanks for having me. I'm excited to be here.

**Sarah** (0:27)
LangChain is a really unique story, and it started actually as a personal project for you. Can you talk a little bit about what LangChain is and what it was originally?

**Harrison Chase** (0:37)
Yeah, absolutely. So how I would answer the question, what LangChain is, has kind of evolved over time, as has the entire landscape. LangChain, the open-source package, started, yeah, as a side project.
So my background's in ML and MLOps. I was at my previous company. I knew I was gonna leave. I didn't know what I was gonna do.
This was in September, October of 2022
And so I went to a bunch of hackathons, went to a bunch of meetups, chatted with folks. They were playing around with LLMs and saw some common abstractions, put it in a Python project as a just fun side project. Turned out to strike a chord, be fantastic timing. ChadGPD came out like a month later. And this kind of evolved from there. So right now, LangChain, the company, there's really two main products that we have. One is the LangChain open-source packages and I'm happy to dive into that more. And then the other is LangSmith platform for testing, evaluation, monitoring, and all of those types of things. And so what LangChain is has evolved over time as the company's grown.

**Sarah** (1:42)
One thing that we talked about the last time we saw each other in person was just how quickly the AI ecosystem and research field is evolving and what it means to manage an open source project through that. Can you talk a little bit about what you decide to keep stable and change when you both have like big ecosystem of users now and like very rapidly changing environment of applications and technology?

**Harrison Chase** (2:06)
That's been a fun exercise. So, I mean, if we go back to the original version of LangChain, what it was when it came out was essentially three kind of like high level implementations, two were based on research papers and then one was based on Nat Friedman's, like Natbot type of agent web crawler thing. And so there was some high level kind of like abstractions and then there was a few like integrations. So we had integrations with, I think, like OpenAI, Coherent Hugging Face to start or something like that. And those two layers have kind of like remained. So we have, you know, 700 different integrations, we have a bunch of kind of like higher level chains and agents for doing particular things. I think the thing that we've put a lot of emphasis in to your point around kind of like what's remained constant and what's remained and what's changed is like a lower level kind of like abstraction and runtime for joining these things together. One of the things that we pretty quickly saw was that as people wanted to improve the performance, go from prototype to production, they wanted to customize a lot of these bits.
And so we've invested a lot in a lower level kind of like chaining protocol, so LangChain expression language. And then in a different protocol, LangGraph, which is something we're really excited about and that's more aimed at basically graphs that are not DAGs. So, you know, all these agents are basically running an LLM in a loop, you need cycles.
And so LangGraph helps with that. And so I think what we've kind of seen is the underlying bits of, there's all these different integrations and like, you know, there's LLMs, vector stores, and sometimes they change, right? When chat models came out, like that was a, that's a very big change in the API interface. And so we had to add a new abstraction for that. But those have, especially over the past few months, remained relatively stable.
We've invested a lot in this underlying runtime, which emphasizes a few things, streaming, structured outputs, and the importance of those has remained relatively stable. But then the way that you put things together and the kind of like patterns for building things has definitely evolved over time, from like simple chains to complex chains, to then these kinds of like autonomous agents, to now something maybe in the middle of like complex state machines or graphs or something. And so it's really that upper layer, which is like the common ways to put things together that I think we've seen the most rapid kind of like churn.