React agent langchain example. For a more robust and feature-rich implementation, we recommend using the create_react_agent function from the LangGraph library. The core logic, defined in src/react_agent/graph. Apr 12, 2025 · Common Tools of LangChain Prompt Templates Cyclic graphs (LangGraphs) Built-in persistence Human-in-the-loop Prompt templates Langchain hub is one of the centralized to store templatized prompt Here is an example of a ReAct prompt that we used in part 1: React System Prompt As you can see, we can pass tools, tool names and input as parameters Jul 28, 2025 · This comprehensive guide explores how LangChain's ReAct framework enables you to build intelligent agents that can navigate complex queries through iterative reasoning and tool interaction, ultimately delivering more accurate and contextually relevant responses to your users. This walkthrough showcases using an agent to implement the ReAct logic. Aug 25, 2024 · AgentExecutor and create_react_agent : Classes and functions used to create and manage agents in LangChain. For your specific use case, feel free to add any other state keys that you need. This guide demonstrates how to implement a ReAct agent using the LangGraph Functional API. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. agent_scratchpad: contains previous agent actions and tool outputs as a string. Aug 27, 2023 · However, the ReAct framework takes an extra step and makes use of the external environment as a wellspring of information. tool_names: contains all tool names. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. note Dec 9, 2024 · The prompt must have input keys: tools: contains descriptions and arguments for each tool. This project showcases the creation of a ReAct (Reasoning and Acting) agent using the LangChain library. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. Tool : A class from LangChain that represents a tool the agent can use. Here’s an example: 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. 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. 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 . ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. Warning This implementation is based on the foundational ReAct paper but is older and not well-suited for production applications. This repository contains sample code to demonstrate how to create a ReAct agent using Langchain. py, demonstrates a flexible ReAct agent that iteratively reasons about user queries and executes actions, showcasing the power of this approach for complex problem-solving tasks. LangChain has several built agents that wrap around the ReAct framework. It's designed to be simple yet informative, guiding you through the essentials of integrating custom tools with Langchain. Create ReAct agent Now that you have installed the required packages and set your environment variables, we can code our ReAct agent! Define graph state We are going to define the most basic ReAct state in this example, which will just contain a list of messages. ntxt soj aaycgta snfpp deg kprk hdscsk oeavx vewstk xou