!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/).

[

![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png)

](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com)

[More](#)

*   [People](/v0.1/docs/people/)
*   [Community](/v0.1/docs/community/)
*   [Tutorials](/v0.1/docs/additional_resources/tutorials/)
*   [Contributing](/v0.1/docs/contributing/)

[v0.1](#)

*   [v0.2](https://js.langchain.com/v0.2/docs/introduction)
*   [v0.1](/v0.1/docs/get_started/introduction/)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Get started](/v0.1/docs/get_started/)
    
    *   [Introduction](/v0.1/docs/get_started/introduction/)
    *   [Installation](/v0.1/docs/get_started/installation/)
    *   [Quickstart](/v0.1/docs/get_started/quickstart/)
*   [LangChain Expression Language](/v0.1/docs/expression_language/)
    
    *   [Get started](/v0.1/docs/expression_language/get_started/)
    *   [Why use LCEL?](/v0.1/docs/expression_language/why/)
    *   [Interface](/v0.1/docs/expression_language/interface/)
    *   [Streaming](/v0.1/docs/expression_language/streaming/)
    *   [How to](/v0.1/docs/expression_language/how_to/routing/)
        
    *   [Cookbook](/v0.1/docs/expression_language/cookbook/)
        
    *   [LangChain Expression Language (LCEL)](/v0.1/docs/expression_language/)
*   [Modules](/v0.1/docs/modules/)
    
    *   [Model I/O](/v0.1/docs/modules/model_io/)
        
    *   [Retrieval](/v0.1/docs/modules/data_connection/)
        
    *   [Chains](/v0.1/docs/modules/chains/)
    *   [Agents](/v0.1/docs/modules/agents/)
        
        *   [Quick start](/v0.1/docs/modules/agents/quick_start/)
        *   [Concepts](/v0.1/docs/modules/agents/concepts/)
        *   [Agent Types](/v0.1/docs/modules/agents/agent_types/)
            
            *   [Tool calling](/v0.1/docs/modules/agents/agent_types/tool_calling/)
            *   [OpenAI tools](/v0.1/docs/modules/agents/agent_types/openai_tools_agent/)
            *   [OpenAI functions](/v0.1/docs/modules/agents/agent_types/openai_functions_agent/)
            *   [XML Agent](/v0.1/docs/modules/agents/agent_types/xml/)
            *   [Conversational](/v0.1/docs/modules/agents/agent_types/chat_conversation_agent/)
            *   [OpenAI Assistant](/v0.1/docs/modules/agents/agent_types/openai_assistant/)
            *   [Plan and execute](/v0.1/docs/modules/agents/agent_types/plan_and_execute/)
            *   [ReAct](/v0.1/docs/modules/agents/agent_types/react/)
            *   [Structured chat](/v0.1/docs/modules/agents/agent_types/structured_chat/)
            *   [XML Agent](/v0.1/docs/modules/agents/agent_types/xml_legacy/)
        *   [How-to](/v0.1/docs/modules/agents/how_to/custom_agent/)
            
        *   [Agents](/v0.1/docs/modules/agents/)
        *   [Tools](/v0.1/docs/modules/agents/tools/)
            
    *   [More](/v0.1/docs/modules/memory/)
        
*   [Security](/v0.1/docs/security/)
*   [Guides](/v0.1/docs/guides/)
    
*   [Ecosystem](/v0.1/docs/ecosystem/)
    
*   [LangGraph](/v0.1/docs/langgraph/)
*   * * *
    

*   [](/v0.1/)
*   [Modules](/v0.1/docs/modules/)
*   [Agents](/v0.1/docs/modules/agents/)
*   [Agent Types](/v0.1/docs/modules/agents/agent_types/)
*   Structured chat

On this page

Structured chat
===============

info

If you are using a functions-capable model like ChatOpenAI, we currently recommend that you use the [OpenAI Functions agent](/v0.1/docs/modules/agents/agent_types/openai_functions_agent/) for more complex tool calling.

The structured chat agent is capable of using multi-input tools.

Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `schema` to populate the action input.

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, createOpenAIToolsAgent } from "langchain/agents";import { pull } from "langchain/hub";import { ChatOpenAI } from "@langchain/openai";import type { ChatPromptTemplate } 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/structured-chat-agentconst prompt = await pull<ChatPromptTemplate>(  "hwchase17/structured-chat-agent");const llm = new ChatOpenAI({  model: "gpt-3.5-turbo-1106",  temperature: 0,});const agent = await createStructuredChatAgent({  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/fe1b0993-4905-4e21-91d2-ff5fc16fdebd/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 project on GitHub that focuses on building applications with LLMs (Large Language Models) through composability. It offers resources, documentation, and encourages contributions to the project. LangChain 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).

    import { AIMessage, HumanMessage } from "@langchain/core/messages";const result2 = await agentExecutor.invoke({  input: "what's my name?",  chat_history: [    new HumanMessage("hi! my name is cob"),    new AIMessage("Hello Cob! How can I assist you today?"),  ],});console.log(result2);/*  {    input: "what's my name?",    chat_history: [      HumanMessage {        content: 'hi! my name is cob',        additional_kwargs: {}      },      AIMessage {        content: 'Hello Cob! How can I assist you today?',        additional_kwargs: {}      }    ],    output: 'Your name is Cob.'  }*/

* * *

#### Help us out by providing feedback on this documentation page:

[

Previous

ReAct

](/v0.1/docs/modules/agents/agent_types/react/)[

Next

XML Agent

](/v0.1/docs/modules/agents/agent_types/xml_legacy/)

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

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.