[Pioneering] LangGraph

[lukehinds]

🦜🕸️LangGraph https://github.com/langchain-ai/langgraph

⚡ Building language agents as graphs ⚡

Note

Looking for the JS version? See the JS repo and the JS docs.

Overview

LangGraph is a library for building
stateful, multi-actor applications with LLMs, used to create agent and multi-agent
workflows. Check out an introductory tutorial here.

LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

Why use LangGraph?

LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central persistence layer, enabling features that are common to most agent architectures:

• Memory: LangGraph persists arbitrary aspects of your application's state,
  supporting memory of conversations and other updates within and across user
  interactions;
• Human-in-the-loop: Because state is checkpointed, execution can be interrupted
  and resumed, allowing for decisions, validation, and corrections at key stages via
  human input.

Standardizing these components allows individuals and teams to focus on the behavior
of their agent, instead of its supporting infrastructure.

Through LangGraph Platform, LangGraph also provides tooling for
the development, deployment, debugging, and monitoring of your applications.

LangGraph integrates seamlessly with
LangChain and
LangSmith (but does not require them).

To learn more about LangGraph, check out our first LangChain Academy
course, Introduction to LangGraph, available for free
here.