why graphs would be superior to using Python

Gemini 1.5 is so powerful that it can read all the Harry Potter books at once and generate a graph of the characters with perfect accuracy

a second set of use cases for langgraph - multi-agent workflows. In this blog we will cover:What does "multi-agent" mean?Why are "multi-agent" workflows interesting?Three concrete examples of using LangGraph for multi-agent workflowsTwo examples of third-party applications built on top of LangGraph using multi-agent workflows (GPT-Newspaper and CrewAI)Comparison to other frameworks (Autogen and CrewAI)

🦜🕸️LangGraph⚡ Building language agents as graphs ⚡Overview​LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. It is inspired by Pregel and Apache Beam. The current interface exposed is one inspired by NetworkX.The main use is for adding cycles to your LLM application. Crucially, this is NOT a DAG framework. If you want to build a DAG, you should use just use LangChain Expression Language.Cycles are important for agent-like behaviors, where you call an LLM in a loop, asking it what action to take next.

Leveraging Graph Algorithms to Enable Responsible AI Reasoning