AI Agent 
An AI travel assistant built on LangGraph: ai-travel-agent, which can search for flights, book hotels, and organize itineraries to email you. It has a memory capability, able to remember user interaction history and continue from where the last conversation left off. It adopts a human-machine collaboration model, allowing users to control key operations, such as … Read more
Click the “Blue Word” to Follow Us In today’s rapidly evolving AI field, building Agents (Which Multi-Agent Framework is Better? A Deep Dive into Five Popular Options) has become an important means of achieving automation and decision optimization. LangGraph, as a powerful tool, provides developers with an intuitive and efficient way to build AI Agents … Read more
Andrew Ng recently said in his public lecture: I believe that AI Agent workflows will drive significant advances in artificial intelligence this year, possibly surpassing the next-generation foundational models. This is an important trend, and I urge everyone working in AI to pay attention to it. Coupled with the four paradigms for implementing Agent workflows … Read more
This article introduces how to build a memory chatbot using LangChain and LangGraph. LangGraph is a Python library developed by the LangChain team specifically for creating complex AI workflows and multi-agent systems that can remember state. Its core goal is to address key pain points in traditional AI orchestration: • Inability to handle complex decision … Read more
There are five ways to create multi-agents in Langgraph: Network: Each agent can communicate with all other agents, and all agents can decide which agent to call next. Supervisor: Each agent can communicate with a supervisor agent, which decides which agent to call next. Supervisor (tool-calling): This is a special case of the supervisor architecture … Read more
Introduction Langchain recently launched an impressive course focused on LangGraph and its key features in developing powerful agent and multi-agent workflows. In this series, we will explore the essential insights from the course and create applications that leverage agent workflows. In the first part, we will cover the basic concepts of LangGraph and how to … Read more
Introduction on how to use LangGraph to improve RAG. Long press to follow “Python Learning Base”, join the reader group, and share more wonderful content. 1. Introduction LangGraph is the latest member of the LangChain, LangServe, and LangSmith series, aimed at building generative AI applications using LLMs. Remember, all these are independent packages and must … Read more
Introduction In the world of stock trading, investors rely on various tools and methods to make informed decisions. Fundamental analysis is a common approach that provides actionable insights by assessing a company’s financial health and stock performance. With advancements in artificial intelligence and machine learning, stock analysis can now be highly automated. In this article, … Read more
Introduction In today’s AI era, building an intelligent dialogue system is no longer just a simple Q&A model. Modern dialogue systems need to possess complex functionalities such as comprehensive context understanding, knowledge retrieval, and personalized recommendations. This article will use an educational scenario as an example to detail how to build a fully functional intelligent … Read more
LangGraph is a library developed by LangChainAI for creating workflows for agents and multi-agent systems. It offers the following core advantages: cycles, controllability, and persistence, which undoubtedly reduce the workload for agent developers. This article will highlight the key points and usage methods of LangGraph from my perspective during the development process. Basic Introduction The … Read more