Comprehensive Guide to Creating Multi-Agent Methods in Langgraph

Comprehensive Guide to Creating Multi-Agent Methods in Langgraph

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

Building Agentic Workflows Using LangGraph

Building Agentic Workflows Using LangGraph

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

Enhancing RAG Effectiveness with LangChain and LangGraph

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

Building a Financial Analyst Agent with LangGraph and OpenAI

Building a Financial Analyst Agent with LangGraph and OpenAI

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

Building Intelligent Dialogue Systems with LangGraph: A Complete Guide

Building Intelligent Dialogue Systems with LangGraph: A Complete Guide

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: A Framework for Developing Intelligent Agents Based on Graph Structures

LangGraph: A Framework for Developing Intelligent Agents Based on Graph Structures

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

Building an AI Coding Agent with LangGraph Using LangChain

Building an AI Coding Agent with LangGraph Using LangChain

● Understand what LangGraph is. ● Explore the basics of LangGraph for building stateful agents. ● Explore TogetherAI to access open-access models like DeepSeekCoder. ● Build an AI coding agent using LangGraph to write unit tests. This article is published as part of the Data Science Blog Marathon. What is LangGraph? LangGraph is an extension … Read more

LangGraph | Beginner’s Guide

LangGraph | Beginner's Guide

Click 01 Muggle Society Follow the official account, and you won’t get lost in AI learning LangGraph is an important feature recently released by LangChain, marking its move towards a multi-agent framework. LangGraph is built on top of LangChain, helping developers easily create powerful agent runtimes. LangChain and its expression language (LCEL) provide technical support … Read more

Multi-Agent Workflow with LangGraph

Multi-Agent Workflow with LangGraph

Introduction The emergence of large language models (LLMs) has reshaped how AI systems interact with and interpret the world. Traditionally, single-agent architectures have been used to handle inputs, make decisions, and produce outputs. However, as AI systems scale to manage more complex, multi-step tasks, researchers and developers are increasingly turning to multi-agent systems and advanced … Read more