정보
- Docs: https://python.langchain.com/docs/langgraph
- Github: https://github.com/langchain-ai/langgraph
- TeddyNote Youtube: https://www.youtube.com/watch?v=1scMJH93v0M
LangChain 공식 홈페이지 LangGraph 정의
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 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.
정리
- TeddyNote 유튜브: LangChain 이 만든 #LangGraph 출시! LangGraph 의 멀티 에이전트 콜라보레이션 찍먹하기
- 샘플 노트북
다중 에이전트 협업(Multi-Agent Collaboration)
- divide-and-conquer 접근법
- 각 작업이나 도메인에 대해 전문화된 에이전트를 만들고 작업을 올바른 ‘전문가’에게 라우팅
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation 방식에 LangGraph 활용
- 각 Agent마다 담당하는 분야를 할당(ex. 검색용 Agent, 차트 생성용 Agent, 코드 작성 Agent)
엄연한 로직, flow가 존재함