Parallel performance increased by 300%! How does LangGraph reshape large language model task orchestration
300% Parallel Performance Improvement! How LangGraph Reshapes Large Model Task Orchestration
via Juejin AI's Hottest This Month (author: Lao Zhou Talks about Large Models)
Telegraph
300% Parallel Performance Improvement! How LangGraph Reshapes Large Model Task OrchestrationDeep Dive into LangGraph: Building Next-Generation Large Language Model Applications with Graph Structures. Follow Lao Zhou and never get lost. This article is relatively long. It is recommended to like and bookmark it to avoid losing it. Due to the limited length of the article, for more knowledge points on salary increases, you can also check the latest free learning materials for AI large model application development on the homepage. Introduction. When developing complex large language model applications, engineers are often troubled by problems such as multi-step reasoning, state management, and task coordination. Traditional code structures often fall short when dealing with scenarios involving decision-making, backtracking, state transfer, and multi-round interactions. The birth of LangGraph is precisely to address these challenges. It transforms application logic into an actionable graph structure, making the design of complex workflows more intuitive and efficient than ever before. LangGraph's Technical Positioning: Transcending Traditional Workflow Engines...
