React agent langchain. A A ReAct agent is an AI agent that uses the “reasoning and acting” (ReAct) framework to combine an LLM's chain of thought (CoT) reasoning with external tool use. Apr 12, 2025 · Introduction In this blog post, we delve deeper into the integration of AI Agents using LangGraph tools, building upon the foundation established in Simple ReAct Agent from Scratch. First, grasp the essence of ReAct Prompting with our guided example. The ReAct agent in LangChain is a versatile agent that utilizes the ReAct framework to select the appropriate tool based on its description. e. Callable Deprecated since version 0. Here’s an example: May 22, 2024 · This tutorial explores how three powerful technologies — LangChain’s ReAct Agents, the Qdrant Vector Database, and Llama3 Language Model. Agent Types This categorizes all the available agents along a few dimensions. ReActOutputParser [source] # Bases: AgentOutputParser Output parser for the ReAct agent. output_parser. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. Feb 27, 2025 · I am trying to build a simple react agent using langchain which uses a tool to get the weather condition of a location I tried making my custom react prompt template and used 'create_react_agent' a Jul 2, 2025 · LangChain In this chapter, we will implement a ReACT agent using only LangChain, without utilizing LangGraph. tool_names: contains all tool names. See the parameters, return value, and example code for createReactAgent function. agents. The agent operates by maintaining an internal state and iteratively performing actions based on the input and the results of previous actions. In those cases, you can create a custom ReAct agent. Feb 13, 2024 · To implement the ReAct pattern with the OpenAI tools agent in LangChain, you need to modify the create_openai_tools_agent function to ensure the agent first responds with a Thought before calling a tool, and correctly feeds in the Observation (tool response). prebuilt import create_react_agent # Create the agent memory = MemorySaver() model = init_chat_model("anthropic:claude-3-5-sonnet-latest") search = TavilySearch(max_results=2) tools = [search] agent Feb 7, 2024 · 🤖 Hey @652994331, great to see you diving into LangChain again! Always a pleasure to help out a familiar face. This section demonstrates basic setup — full implementation details follow in later sections. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. This allows you to easily deploy LangGraph agents as ReActOutputParser # class langchain. It is easy to write custom tools, and you can easily pass these to the model. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Learn how to create an agent that uses ReAct prompting, a method for synergizing reasoning and acting in language models. jsParams required to create the agent. ZeroShotAgent # class langchain. 安装依赖 如果您尚未安装，请安装 LangGraph 和 LangChain Deprecated since version 0. question-answering) agent, we'll use a simple ReACT architecture and give the agent tools for looking up track names, artist names, and album names based on various filters. This agent uses a search tool to look up answers to the simpler questions in order to answer the original complex question. Start learning now! Nov 29, 2024 · Today we are going to discuss about Agentic Framework — ReAct Pattern and it’s implementation using two different approaches: Jun 12, 2024 · シンプルな実装例。 エージェントの出力にツール呼び出しがなくなるまで処理が続行される。 create_react_agent の返り値の型は CompiledGraph なので、通常のコンパイル済みGraphと同様に invoke 等のメソッドでエージェントの実行が可能。 This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. This is driven by a LLMChain. Agent that calls the language model and deciding the action. Setup This tutorial uses LangGraph for agent orchestration, OpenAI's Feb 2, 2024 · 概要 前回のLangChainを用いてReActフレームワークの理解からバージョンの更新が進み、 LangChain Expression Languageという記法が導入されました。 記法の導入に伴い前回（v0. Learn how to use LangGraph's react agent executor to create tool-calling agents with OpenAI models. Install dependencies If you haven't already, install LangGraph and LangChain: Feb 18, 2025 · from langchain_core. 5k次，点赞13次，收藏33次。create_react_agent 是用于构建基于 ReAct（思考-行动）模式 的智能代理（Agent）的核心函数，其作用是将大语言模型（LLM）与工具调用能力结合，实现动态任务处理_langchain react agent. chat_models import init_chat_model from langchain_tavily import TavilySearch from langgraph. language_models. Based on paper "ReAct: Synergizing Reasoning and Acting in Language Dec 5, 2023 · Master LangChain Agents and React Framework with our ultimate guide! Transform your AI skills, unleash intelligent automation. prebuilt import create_react_agent封装好的 Memory Savor本人加入一些补充说明什么是 A… Sep 17, 2024 · LangChain, a powerful library for building applications with large language models (LLMs), can be seamlessly integrated with React to create AI-powered web apps. Today, we are splitting that out of langgraph as part of a 0. Aug 29, 2024 · ReAct agents extend the capabilities of LLMs to respond to queries by mimicking human reactions to problems using external tools. This agent is the most general-purpose action agent available in LangChain and can be highly beneficial in situations where multiple tools are available, and selecting the right tool is time-consuming. prebuilt import create_react_agent # Define a custom system message system_message = "You are a helpful assistant. This opened the door for creative applications, like automatically accessing web Jan 24, 2025 · I don't get why this doesn't work. Learn how these AI techniques improve efficiency and accuracy in processing and generating reports. [docs] def create_react_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate, output_parser: Optional[AgentOutputParser] = None, tools_renderer: ToolsRenderer = render_text_description, *, stop_sequence: Union[bool, list[str]] = True, ) -> Runnable: """Create an agent that uses ReAct prompting. 🏃 The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. llms import OpenAI from langchain. In this article, we’ll dive into Langchain Agents, their components, and how to use [docs] def create_react_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate, output_parser: Optional[AgentOutputParser] = None, tools_renderer: ToolsRenderer = render_text_description, *, stop_sequence: Union[bool, List[str]] = True, ) -> Runnable: """Create an agent that uses ReAct prompting. The prompt must have input keys: tools: contains descriptions and arguments for each tool. ZeroShotAgent [source] # Bases: Agent Deprecated since version 0. prompts. LangGraph ReAct Memory Agent This repo provides a simple example of a ReAct-style agent with a tool to save memories. Jun 2, 2024 · LangChain vs. ts, demonstrates a flexible ReAct agent that Jun 26, 2024 · In this post, we’ve created a responsive AI agent using Langchain and OpenAI. Aug 22, 2023 · 2. This project showcases the creation of a ReAct (Reasoning and Acting) agent using the LangChain library. Mar 25, 2024 · To initialize a CONVERSATIONAL_REACT_DESCRIPTION agent using the create_react_agent method in LangChain, you can follow these steps: Import the necessary modules and classes from langchain and other libraries as shown in the example. Learn how to create a ReAct agent using LangGraph, a framework for building conversational agents with tools and models. Based on paper "ReAct: Synergizing Reasoning and Acting in Language Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. react. 3 release, and moving it into langgraph-prebuilt. The core logic, defined in src/react_agent/graph. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Jan 23, 2025 · Get started in minutes using MCP and ReAct agent for intelligent tool handling in LangChain. agents import initialize_agent, AgentType Sep 16, 2024 · The LangChain library spearheaded agent development with LLMs. Before exploring LangGraph in detail, it is useful to review some LangChain basics, as LangGraph is built upon LangChain. Prerequisites Before you start this tutorial, ensure you have the following: An Anthropic API key 1. Aug 21, 2023 · A step-by-step guide to building a LangChain enabled SQL database question answering agent. Using LangGraph's pre-built ReAct agent constructor, we can do this in one line. Contribute to langchain-ai/langchain-mcp-adapters development by creating an account on GitHub. 写在前面本文翻译自 LangChain 的官方文档 “Build an Agent”， 基于： LangGraph 封装好的 ReAct agent：from langgraph. Optional [~langchain. For details, refer to the LangGraph documentation as well as guides for This template showcases a ReAct agent implemented using LangGraph. Design industry-specific workflows. If the model generates tool calls, we execute the tool calls with available tools, append them as tool messages to our message list LangSmith - LangChain provides a template for implementing ReAct logic using LangChain. Jul 23, 2023 · LangChain の ReAct を使って見ます。同じ prompt や 同じ LLM を使って Plan-and-Execute の実行結果、verbose のログを比較することで LLM に上手くタスクを動作させるためのコツなども考えます。 This repository contains sample code to demonstrate how to create a ReAct agent using Langchain. Test a ReAct agent with Pytest/Vitest and LangSmith This tutorial will show you how to use LangSmith's integrations with popular testing tools Pytest and Vitest/Jest to evaluate your LLM application. memory import MemorySaver from langgraph. ReActOutputParser ¶ Note ReActOutputParser implements the standard Runnable Interface. With the LangChain ReAct agents, you can enable your model to react to problems or take actions according to its understanding of the real world. In Chains, a sequence of actions is hardcoded. An agent that breaks down a complex question into a series of simpler questions. See parameters, return type, examples and prompt format for create_react_agent function. It incorporates the React framework to determine which tool to use and utilizes memory to remember previous conversation interactions. ReAct framework: Similar to a chain of thought reasoning, however, it retraces to a prior step. Learn how to build 3 types of planning agents in LangGraph in this post. These agents repeatedly questioning their output until a solution to a given task is found. Aug 27, 2023 · C-O-T by Wei et al. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. js application which enables chatting with any LangGraph server with a messages key through a chat interface. The ReAct agent is a tool-calling agent that operates as follows: Queries are issued to a chat model; If the model generates no tool calls, we return the model response. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. Mar 30, 2025 · LangChain-MCP-Adapters is a toolkit provided by LangChain AI that enables AI agents to interact with external tools and data sources through the Model Context Protocol (MCP). BasePromptTemplate, output_parser: ~typing. Aug 28, 2024 · A comprehensive tutorial on building multi-tool LangChain agents to automate tasks in Python using LLMs and chat models using OpenAI. Conversational ReAct This agent is designed for use in conversational settings. tools. LangChain 🔌 MCP. Jun 2, 2024 · We introduced three types of agents: the Zero-shot ReAct Agent, Conversational Agent, ReAct Docstore and Self-ask with search catering to beginners. 5-sonnet, gpt-4o, o1, and o3-mini across two domains of tasks. This guide shows how to implement ReAct agent from scratch using LangGraph. It's the code from the documentation, which clearly states that create_react_agent has a response_format option, but it returns an Jun 12, 2024 · Exploring LangChain Agents with Memory: Basic Concepts and Hands-On Code Multi-Agent Chatbot is a sophisticated chatbot application that leverages multiple agents to handle different types of queries. Jun 14, 2024 · ReAct 本次演示展示了如何使用一个agent来实现 ReAct 的逻辑。 agents # Agent is a class that uses an LLM to choose a sequence of actions to take. Specifically, we enable this model to call tools by providing it a list of LangChain tools. The main thing this affects is the prompting strategy used. # Import relevant functionality from langchain. 0. For example, you can look up albums by a particular artist, artists who released songs with a specific name, etc. It breaks down a query into actionable sub-tasks, and each task is followed Jan 31, 2024 · In this blog, we will delve into the implementation of the ReAct framework within Langchain and provide a detailed, step-by-step guide on the functioning of a simple agent. Use LLMs like GPT or Llama for advanced natural language processing. We’ve set up the environment, pulled a React prompt, initialized the language model, and added the capability to The prompt must have input keys: tools: contains descriptions and arguments for each tool. Includes an LLM, tools, and prompt. Packages: langgraph, langchain-openai. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. create_react_agent ¶ langchain. To get started, you can instantiate the structured chat agent executor using the following code snippet: from langchain. mrkl. This is a simple way to let an agent persist important information to reuse later. The goal is to enable the agent to process user queries, interact with an SQL database, and return coherent, context-aware Feb 10, 2025 · We explore how increasing the number of instructions and tools available to a single ReAct agent affects its performance, benchmarking models like claude-3. Agents select and use Tools and Toolkits for actions. base. We are also introducing a new set of prebuilt agents built on top of LangGraph, in both Python and JavaScript. py" script. create_react_agent(llm: ~langchain_core. May 2, 2023 · Due to limitations in the default prompts and output parsers of previous agents, they do not effectively work with structured tools without extra customization. It supports streaming, generative UI, human-in-the-loop, and other UX paradigms crucial for agentic applications. BaseLanguageModel, tools: ~typing. 1に合わせつつ、エージェントの概念を Sep 11, 2024 · Checked other resources I added a very descriptive title to this question. Feb 28, 2025 · Familiarity with agent architectures, chat models, and tools. Here’s an example: Nov 18, 2024 · Learn to build a LangChain ReAct agent using the Requests Toolkit. ” In essence, agents are systems that can independently make decisions, use tools, take actions, and pursue a goal without direct human guidance. LangGraph quickstart This guide shows you how to set up and use LangGraph's prebuilt, reusable components, which are designed to help you construct agentic systems quickly and reliably. This project provides a user-friendly interface for deploying ReAct agents that can access various data sources and APIs Jul 4, 2025 · LangChain similarly defines them as systems using an LLM to “decide the control flow of an application. Class hierarchy: Agent Chat UI is a Next. This walkthrough showcases using an agent to implement the ReAct logic. I searched the LangChain documentation with the integrated search. Lookup agent For the lookup (i. Learn how to create an agent that uses React prompting with LangChain. Nov 22, 2024 · By combining React agents with LangGraph, you can: Implement multi-agent systems. This allows you to verify, if the agent uses the best tool for the question. Mar 20, 2024 · ただ、上記のサイトで紹介されている"initialize_agent"を実行すると非推奨と出るように、Langchain0. BaseTool], prompt: ~langchain_core. It integrates with LangChain, OpenAI, and various tools to deliver accurate and helpful responses. This article will guide you through the process of combining LangChain with React to build intelligent and interactive web applications. 🦜🎤 Voice ReAct Agent This is an implementation of a ReAct -style agent that uses OpenAI's new Realtime API. Let's work together to resolve your issue. LangGraph 快速入门 本指南将向您展示如何设置和使用 LangGraph 的 预构建 、 可复用 组件，这些组件旨在帮助您快速可靠地构建代理系统。 先决条件 在开始本教程之前，请确保您具备以下条件： 一个 Anthropic API 密钥 1. The most well-known agent implementation are ReAct Agents. Lay the foundation… Aug 13, 2024 · Hello, @invalidexplorer! I'm here to assist you with any bugs, questions, or contributions you might have. 198）からLangChainの中身が大きく変わっていましたので、中身について改めて確認したものとなります。 Agentを改造する際にどの You can copy and paste the example questions from this table to replace the question at the bottom of the "ai-langchain-react-agent. What’s a ReAct agent? Sep 16, 2023 · Don’t rush into agent applications in LangChain. Dec 21, 2023 · Explore implementing ReAct and function-calling agent types to enhance natural language reporting. agent_scratchpad: contains previous agent actions and tool outputs as a string. It's designed to be simple yet informative, guiding you through the essentials of integrating custom tools with Langchain. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well. This project is designed to create and configure a ReAct (Reasoning and Acting) agent using LangChain and OpenAI's GPT-4o model. js, designed for LangGraph Studio. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. By building on the basics from Part 1, we've seen how to implement complex AI workflows utilizing both parallel and sequential function calls. We explore new tools and techniques that enable more sophisticated AI agent implementations. memory import ConversationBufferMemory llm = OpenAI(openai_api_key Documentation for LangChain. What is LangChain? Dec 9, 2024 · langchain. Previous Chapter: Jan 6, 2024 · LangChain also has another concept called Agent, that we can use to interpret the input from the user and determine which tool or tools to use to complete the task. The ReAct framework is a powerful approach that combines reasoning capabilities with actionable outputs, enabling language models to interact with external tools and answer complex questions Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Here's an example of how you might do this: A Python library for creating hierarchical multi-agent systems using LangGraph. Sep 11, 2024 · TL;DR: assistant-ui is an embeddable AI chat frontend for React applications. Dec 9, 2024 · langchain. Compare the configuration parameters and usage of LangChain agents and LangGraph react agents. Feb 13, 2024 · Plan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. Based on paper "ReAct: Synergizing Reasoning and Acting in Language ReAct Agents Overview ReAct agents in LangChain are designed to handle natural language inputs, process them, and determine the appropriate actions to take using a set of integrated tools. Jun 17, 2025 · LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. Below we assemble a minimal SQL agent. LlamaIndex，在 ReAct 模式、Tool use / function call 上的異同與特性又是什麼？這是一系列的比較，讓你更了解該選擇 LangChain 還是 LlamaIndex Apr 6, 2025 · 文章浏览阅读2. I used the GitHub search to find a similar question and Sep 18, 2024 · A key feature of Langchain is its Agents — dynamic tools that enable LLMs to perform tasks autonomously. agents import initialize_agent, load_tools from langchain. Documentation for LangChain. [Github] The Jupiter Notebook of this implementation Common Tools of LangChain Prompt Templates Cyclic graphs (LangGraphs Dec 9, 2024 · [docs] def create_react_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate, output_parser: Optional[AgentOutputParser] = None, tools_renderer: ToolsRenderer = render_text_description, *, stop_sequence: Union[bool, List[str]] = True, ) -> Runnable: """Create an agent that uses ReAct prompting. 🚀 To create a zero-shot react agent in LangChain with the ability of a csv_agent embedded inside, you would need to create a csv_agent as a BaseTool and include it in the tools sequence when creating the react agent. This class is designed to handle ReAct-style LLM calls and ensures that the output is parsed correctly 使用预置的 ReAct 代理 create_react_agent 是一个很好的入门方式，但有时您可能需要更多的控制和定制。 在这种情况下，您可以创建自定义的 ReAct 代理。 本指南展示了如何使用 LangGraph 从头开始实现 ReAct 代理。 设置 首先，让我们安装所需的软件包并设置我们的 API Nov 14, 2023 · From the theory of the original academic paper to its Python implementation with OpenAI, Weaviate, and LangChain This walkthrough demonstrates how to use an agent optimized for conversation. This template shows a basic ReAct agent that reasons and acts on user queries with Tavily and Anthropic or OpenAI chat models. Agent # class langchain. You can use an agent with a different type of model than it is intended for, but it likely won't produce Documentation for LangChain. 1. For details, refer to the LangGraph documentation as well as guides for ReActChain # class langchain. We will equip it with a set of tools using LangChain's SQLDatabaseToolkit. Code from langchain. Intended Model Type Whether this agent is intended for Chat Models (takes in messages, outputs message) or LLMs (takes in string, outputs string). messages import SystemMessage from langgraph. Sequence [~langchain_core. The agent is integrated with a set of tools, such as an SQL tool, and utilizes a memory buffer to maintain conversation history across sessions. We’ve worked with Simon (the maintainer) to add a tight integration with LangGraph Cloud. However, our exploration doesn’t conclude here. CONVERSATIONAL_REACT_DESCRIPTION = 'conversational-react-description' # CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'chat-zero-shot-react-description' # Feb 27, 2025 · It was create_react_agent, a wrapper for creating a simple tool calling agent. Make APIs work with natural language for easy, real-time data retrieval. Tools are essentially functions that extend the agent’s capabilities by Apr 12, 2025 · In this continuation of our exploration into AI Agents within LangGraph, we've leveraged LangChain tools to enhance our agent's capabilities. Class hierarchy: Agent # class langchain. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. We will create a ReAct agent that answers questions about publicly traded stocks and write a comprehensive test suite for it. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. AgentOutputParser] = None, tools_renderer: ~typing. 1では別の書き方が推奨されます。 （もちろん'zero-shot-react-description'もなくなっています） エージェントやツールの概念は参考にできるのですが、書き方を0. When running an LLM in a continuous loop, and providing the capability to browse external data stores and a chat history, context-aware agents can be created. js, a library for building AI applications. checkpoint. This guide demonstrates how to implement a ReAct agent using the LangGraph Functional API. ReActChain [source] # Bases: AgentExecutor They can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table). To get structured output from a ReAct Agent in LangChain without encountering JSON parsing errors, you can use the ReActOutputParser class. Using the prebuilt ReAct agent create_react_agent is a great way to get started, but sometimes you might want more control and customization. agent. LangGraph offers a more flexible and full-featured framework for building agents, including support for tool-calling, persistence of state, and human-in-the-loop workflows. In this case, we save all memories scoped to a configurable user_id, which lets the bot learn a user's preferences across conversational threads. rldv ibaeki zlc ahk tvfejwd hdycfh yydwho ptoef jnw tkbrkps