LangGraph

tags :

Built on top of LangChain #

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(LCEL) 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. URL

The main use is for adding cycles to your LLM application. Crucially, LangGraph is NOT optimized for only DAG workflows. If you want to build a DAG, you should just use LangChain Expression Language(LCEL).

pip install langgraph

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langgraph.graph import END, MessageGraph

model = ChatOpenAI(temperature=0)

graph = MessageGraph()

graph.add_node("oracle", model)
graph.add_edge("oracle", END)

graph.set_entry_point("oracle")

runnable = graph.compile()