Open in Colab:
It accompanies a youtube tutorial at: https://youtu.be/R8KB-Zcynxc
This tutorial provides a step-by-step guide on how to use LangGraph, a tool developed by the LangChain team to build Agent apps. LangGraph allows you to create graphs and call them, enabling you to create complex applications powered by LangChain.
- Brief overview of LangGraph and its purpose
- Mention the availability of a blog and notebooks provided by the LangChain team for further reference
- Installation instructions for LangGraph using pip
- Importing the LangGraph library and initializing a workflow
- Explanation of the simplest graph structure with two nodes connected by an edge
- Overview of the user input and functions used in the graph
- Code examples and explanations of function one and function two
- Instructions on compiling the graph app
- Explanation of invoking the graph using user input
- Output examples and explanation of the graph's functionality
- Introduction to making LLM calls in the graph
- Explanation of modifying function one to invoke an LLM model
- Instructions on installing the LangChain OpenAI package
- Steps to add the API key for making API calls
- Overview of tools available in LangGraph
- Instructions on binding a tool to the model and using tool invocation
- Code examples and explanations of tool usage in function two
- Explanation of the conditional edge in a graph
- Instructions on defining conditional behavior in the graph's functions
- Implementation of the conditional edge in the graph
Hope you get a good understanding of LangGraph. Reach out on Twitter if any questions