LangGraph: Intro

Key Takeaways

hello everyone today I want to talk about Lang grath a new library that we're releasing so langra Builds on top of Lang chain and makes it really easy to create agents and agent run times so what exactly is an agent and an agent runtime so in Lang chain we Define an agent as a system powered by a language model that decides what action to take there's then an agent runtime that basically runs that agent in a loop calls that agent decides what action to take then takes that action and then get records the observation and then passes that back in and starts to Loop over again and continues going through that Loop until the agent decides that it is finished so we've made it easy over the past few months to customize agents in Lang chain with Lang chain expression language and I'd recommend checking those out if you haven't what we're doing with Lang graph is we're making it easy to customize the agent runtime so previously the agent runtime was always the agent EX class and this was basically something that ran in a loop and and called uh Tools in a specific way and and handled errors in a specific way and that was great but it was just one way to create this runtime and so we want to create more ways to create this runtime and ways to create them more flexibly and dynamically and a key part of this runtime is the ability to add Cycles so you know the whole point of the agent runtime is running this agent this llm powered agent in a loop and so you need to be able to have cycles and Lang chain expression language and other uh you know dag likee Frameworks are non they're not cyclical um and so that's what we're introducing L graph for um it's it's a way to create these cyclical agent run times so that's hopefully an overview of what Lang graph is and and why we created it the rest of the videos in this series are going to focus on two of the main agent run times that we've added to start so first we've added a agent executor very similar to the agent executor in uh laying chain so we've recreated that agent executor with L graph and then we've added a chat agent executor the chat agent executive takes in a list of messages and then represents the agent State just as a list of messages and so it returns a list of messages as well the reason we did this is that a lot of the newer models are chat-based models and they intrinsically represent function calling um as as basically parameters of part of a message and then they also have uh function responses being a separate type of message and so representing this agent State as a list of messages is very natural for these types of models and so we've added a separate agent executor particularly for that then I'm going to highlight a bunch of different ways that you can modify the base agent executors to do things like add human in the loops um force uh specific tool to be called First and other exciting things like that hope you enjoy