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*   ReAct

On this page

ReAct
=====

This walkthrough showcases using an agent to implement the [ReAct](https://react-lm.github.io/) logic.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

Install the OpenAI integration package, retrieve your key, and store it as an environment variable named `OPENAI_API_KEY`:

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

This demo also uses [Tavily](https://app.tavily.com), but you can also swap in another [built in tool](/v0.1/docs/integrations/platforms/). You'll need to sign up for an API key and set it as `TAVILY_API_KEY`.

Initialize Tools[​](#initialize-tools "Direct link to Initialize Tools")
------------------------------------------------------------------------

We will first create a tool:

    import { TavilySearchResults } from "@langchain/community/tools/tavily_search";// Define the tools the agent will have access to.const tools = [new TavilySearchResults({ maxResults: 1 })];

Create Agent[​](#create-agent "Direct link to Create Agent")
------------------------------------------------------------

    import { AgentExecutor, createReactAgent } from "langchain/agents";import { pull } from "langchain/hub";import { OpenAI } from "@langchain/openai";import type { PromptTemplate } from "@langchain/core/prompts";// Get the prompt to use - you can modify this!// If you want to see the prompt in full, you can at:// https://smith.langchain.com/hub/hwchase17/reactconst prompt = await pull<PromptTemplate>("hwchase17/react");const llm = new OpenAI({  model: "gpt-3.5-turbo-instruct",  temperature: 0,});const agent = await createReactAgent({  llm,  tools,  prompt,});

Run Agent[​](#run-agent "Direct link to Run Agent")
---------------------------------------------------

Now, let's run our agent!

tip

[LangSmith trace](https://smith.langchain.com/public/44989da5-8742-429f-9ab1-2377d773b0d2/r)

    const agentExecutor = new AgentExecutor({  agent,  tools,});const result = await agentExecutor.invoke({  input: "what is LangChain?",});console.log(result);/*  {    input: 'what is LangChain?',    output: 'LangChain is a platform for building applications using LLMs (Language Model Microservices) through composability. It can be used for tasks such as retrieval augmented generation, analyzing structured data, and creating chatbots.'  }*/

Using with chat history[​](#using-with-chat-history "Direct link to Using with chat history")
---------------------------------------------------------------------------------------------

For more details, see [this section of the agent quickstart](/v0.1/docs/modules/agents/quick_start/#adding-in-memory).

    // Get the prompt to use - you can modify this!// If you want to see the prompt in full, you can at:// https://smith.langchain.com/hub/hwchase17/react-chatconst promptWithChat = await pull<PromptTemplate>("hwchase17/react-chat");const agentWithChat = await createReactAgent({  llm,  tools,  prompt: promptWithChat,});const agentExecutorWithChat = new AgentExecutor({  agent: agentWithChat,  tools,});const result2 = await agentExecutorWithChat.invoke({  input: "what's my name?",  // Notice that chat_history is a string, since this prompt is aimed at LLMs, not chat models  chat_history: "Human: Hi! My name is Cob\nAI: Hello Cob! Nice to meet you",});console.log(result2);/*  {    input: "what's my name?",    chat_history: 'Human: Hi! My name is Cob\nAI: Hello Cob! Nice to meet you',    output: 'Your name is Cob.'  }*/

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Plan and execute

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Structured chat

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*   [Setup](#setup)
*   [Initialize Tools](#initialize-tools)
*   [Create Agent](#create-agent)
*   [Run Agent](#run-agent)
*   [Using with chat history](#using-with-chat-history)

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