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    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
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    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
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*   How to use tools

On this page

How to use tools
================

Prerequisites

This guide assumes familiarity with the following:

*   [Chatbots](/v0.2/docs/tutorials/chatbot)
*   [Tools](/v0.2/docs/concepts#tools)

This section will cover how to create conversational agents: chatbots that can interact with other systems and APIs using tools.

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

For this guide, we’ll be using an OpenAI tools agent with a single tool for searching the web. The default will be powered by [Tavily](/v0.2/docs/integrations/tools/tavily_search), but you can switch it out for any similar tool. The rest of this section will assume you’re using Tavily.

You’ll need to [sign up for an account on the Tavily website](https://tavily.com), and install the following packages:

*   npm
*   yarn
*   pnpm

    npm i @langchain/core @langchain/openai langchain

    yarn add @langchain/core @langchain/openai langchain

    pnpm add @langchain/core @langchain/openai langchain

    import { TavilySearchResults } from "@langchain/community/tools/tavily_search";import { ChatOpenAI } from "@langchain/openai";const tools = [  new TavilySearchResults({    maxResults: 1,  }),];const llm = new ChatOpenAI({  model: "gpt-3.5-turbo-1106",  temperature: 0,});

To make our agent conversational, we must also choose a prompt with a placeholder for our chat history. Here’s an example:

    import { ChatPromptTemplate } from "@langchain/core/prompts";// Adapted from https://smith.langchain.com/hub/hwchase17/openai-tools-agentconst prompt = ChatPromptTemplate.fromMessages([  [    "system",    "You are a helpful assistant. You may not need to use tools for every query - the user may just want to chat!",  ],  ["placeholder", "{messages}"],  ["placeholder", "{agent_scratchpad}"],]);

Great! Now let’s assemble our agent:

    import { AgentExecutor, createOpenAIToolsAgent } from "langchain/agents";const agent = await createOpenAIToolsAgent({  llm,  tools,  prompt,});const agentExecutor = new AgentExecutor({ agent, tools });

Running the agent[​](#running-the-agent "Direct link to Running the agent")
---------------------------------------------------------------------------

Now that we’ve set up our agent, let’s try interacting with it! It can handle both trivial queries that require no lookup:

    import { HumanMessage } from "@langchain/core/messages";await agentExecutor.invoke({  messages: [new HumanMessage("I'm Nemo!")],});

    {  messages: [    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "I'm Nemo!",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "I'm Nemo!",      name: undefined,      additional_kwargs: {},      response_metadata: {}    }  ],  output: "Hello Nemo! It's great to meet you. How can I assist you today?"}

Or, it can use of the passed search tool to get up to date information if needed:

    await agentExecutor.invoke({  messages: [    new HumanMessage(      "What is the current conservation status of the Great Barrier Reef?"    ),  ],});

    {  messages: [    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "What is the current conservation status of the Great Barrier Reef?",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "What is the current conservation status of the Great Barrier Reef?",      name: undefined,      additional_kwargs: {},      response_metadata: {}    }  ],  output: "The current conservation status of the Great Barrier Reef is a cause for concern. The International "... 801 more characters}

Conversational responses[​](#conversational-responses "Direct link to Conversational responses")
------------------------------------------------------------------------------------------------

Because our prompt contains a placeholder for chat history messages, our agent can also take previous interactions into account and respond conversationally like a standard chatbot:

    import { AIMessage } from "@langchain/core/messages";await agentExecutor.invoke({  messages: [    new HumanMessage("I'm Nemo!"),    new AIMessage("Hello Nemo! How can I assist you today?"),    new HumanMessage("What is my name?"),  ],});

    {  messages: [    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "I'm Nemo!",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "I'm Nemo!",      name: undefined,      additional_kwargs: {},      response_metadata: {}    },    AIMessage {      lc_serializable: true,      lc_kwargs: {        content: "Hello Nemo! How can I assist you today?",        tool_calls: [],        invalid_tool_calls: [],        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "Hello Nemo! How can I assist you today?",      name: undefined,      additional_kwargs: {},      response_metadata: {},      tool_calls: [],      invalid_tool_calls: []    },    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "What is my name?",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "What is my name?",      name: undefined,      additional_kwargs: {},      response_metadata: {}    }  ],  output: "Your name is Nemo!"}

If preferred, you can also wrap the agent executor in a `RunnableWithMessageHistory` class to internally manage history messages. First, we need to slightly modify the prompt to take a separate input variable so that the wrapper can parse which input value to store as history:

    // Adapted from https://smith.langchain.com/hub/hwchase17/openai-tools-agentconst prompt2 = ChatPromptTemplate.fromMessages([  [    "system",    "You are a helpful assistant. You may not need to use tools for every query - the user may just want to chat!",  ],  ["placeholder", "{chat_history}"],  ["human", "{input}"],  ["placeholder", "{agent_scratchpad}"],]);const agent2 = await createOpenAIToolsAgent({  llm,  tools,  prompt: prompt2,});const agentExecutor2 = new AgentExecutor({ agent: agent2, tools });

Then, because our agent executor has multiple outputs, we also have to set the `outputMessagesKey` property when initializing the wrapper:

    import { ChatMessageHistory } from "langchain/stores/message/in_memory";import { RunnableWithMessageHistory } from "@langchain/core/runnables";const demoEphemeralChatMessageHistory = new ChatMessageHistory();const conversationalAgentExecutor = new RunnableWithMessageHistory({  runnable: agentExecutor2,  getMessageHistory: (_sessionId) => demoEphemeralChatMessageHistory,  inputMessagesKey: "input",  outputMessagesKey: "output",  historyMessagesKey: "chat_history",});

    await conversationalAgentExecutor.invoke(  { input: "I'm Nemo!" },  { configurable: { sessionId: "unused" } });

    {  input: "I'm Nemo!",  chat_history: [    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "I'm Nemo!",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "I'm Nemo!",      name: undefined,      additional_kwargs: {},      response_metadata: {}    },    AIMessage {      lc_serializable: true,      lc_kwargs: {        content: "Hello Nemo! It's great to meet you. How can I assist you today?",        tool_calls: [],        invalid_tool_calls: [],        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "Hello Nemo! It's great to meet you. How can I assist you today?",      name: undefined,      additional_kwargs: {},      response_metadata: {},      tool_calls: [],      invalid_tool_calls: []    }  ],  output: "Hello Nemo! It's great to meet you. How can I assist you today?"}

    await conversationalAgentExecutor.invoke(  { input: "What is my name?" },  { configurable: { sessionId: "unused" } });

    {  input: "What is my name?",  chat_history: [    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "I'm Nemo!",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "I'm Nemo!",      name: undefined,      additional_kwargs: {},      response_metadata: {}    },    AIMessage {      lc_serializable: true,      lc_kwargs: {        content: "Hello Nemo! It's great to meet you. How can I assist you today?",        tool_calls: [],        invalid_tool_calls: [],        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "Hello Nemo! It's great to meet you. How can I assist you today?",      name: undefined,      additional_kwargs: {},      response_metadata: {},      tool_calls: [],      invalid_tool_calls: []    },    HumanMessage {      lc_serializable: true,      lc_kwargs: {        content: "What is my name?",        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "What is my name?",      name: undefined,      additional_kwargs: {},      response_metadata: {}    },    AIMessage {      lc_serializable: true,      lc_kwargs: {        content: "Your name is Nemo!",        tool_calls: [],        invalid_tool_calls: [],        additional_kwargs: {},        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "Your name is Nemo!",      name: undefined,      additional_kwargs: {},      response_metadata: {},      tool_calls: [],      invalid_tool_calls: []    }  ],  output: "Your name is Nemo!"}

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You’ve now learned how to create chatbots with tool-use capabilities.

For more, check out the other guides in this section, including [how to add history to your chatbots](/v0.2/docs/how_to/chatbots_memory).

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