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Tavily is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed. Tavily offers four key endpoints, one of which being Extract, which provides raw extracted content from a URL. This guide provides a quick overview for getting started with the Tavily tool. For a complete breakdown of the Tavily tool, you can find more detailed documentation in the API reference.

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Overview

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Integration details

ClassPackagePY supportVersion
TavilyExtract@langchain/tavily✅NPM - Version

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Setup

The integration lives in the @langchain/tavily package, which you can install as shown below: 
npm install @langchain/tavily @langchain/core

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Credentials

Set up an API key here and set it as an environment variable named TAVILY_API_KEY. 
process.env.TAVILY_API_KEY = "YOUR_API_KEY"
It’s also helpful (but not needed) to set up LangSmith for best-in-class observability: 
process.env.LANGSMITH_TRACING="true" process.env.LANGSMITH_API_KEY="your-api-key"

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Instantiation

You can import and instantiate an instance of the TavilyExtract tool like this: 
import { TavilyExtract } from "@langchain/tavily";  const tool = new TavilyExtract({  extractDepth: "basic",  includeImages: false, });

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Invocation

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Invoke directly with args

The Tavily Extract tool accepts the following arguments during invocation:
  • urls (required): A list of URLs to extract content from.
  • Both extractDepth and includeImages can also be set during invocation 
await tool.invoke({  "urls": ["https://en.wikipedia.org/wiki/Lionel_Messi"] });

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Invoke with ToolCall

We can also invoke the tool with a model-generated ToolCall, in which case a @[ToolMessage] will be returned: 
// This is usually generated by a model, but we'll create a tool call directly for demo purposes. const modelGeneratedToolCall = {  args: {"urls": ["https://en.wikipedia.org/wiki/Lionel_Messi"]},  id: "1",  name: tool.name,  type: "tool_call", }  await tool.invoke(modelGeneratedToolCall)

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Chaining

We can use our tool in a chain by first binding it to a tool-calling model and then calling it: 
// @lc-docs-hide-cell  import { ChatOpenAI } from "@langchain/openai"  const llm = new ChatOpenAI({  model: "gpt-4o",  temperature: 0, }) 
import { HumanMessage } from "@langchain/core/messages"; import { ChatPromptTemplate } from "@langchain/core/prompts"; import { RunnableLambda } from "@langchain/core/runnables";  const prompt = ChatPromptTemplate.fromMessages(  [  ["system", "You are a helpful assistant."],  ["placeholder", "{messages}"],  ] )  const llmWithTools = llm.bindTools([tool]);  const chain = prompt.pipe(llmWithTools);  const toolChain = RunnableLambda.from(  async (userInput: string, config) => {  const humanMessage = new HumanMessage(userInput);  const aiMsg = await chain.invoke({  messages: [new HumanMessage(userInput)],  }, config);  const toolMsgs = await tool.batch(aiMsg.tool_calls, config);  return chain.invoke({  messages: [humanMessage, aiMsg, ...toolMsgs],  }, config);  } );  const toolChainResult = await toolChain.invoke("['https://en.wikipedia.org/wiki/Albert_Einstein','https://en.wikipedia.org/wiki/Theoretical_physics']"); 
const { tool_calls, content } = toolChainResult;  console.log("AIMessage", JSON.stringify({  tool_calls,  content, }, null, 2));

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Agents

For guides on how to use LangChain tools in agents, see the LangGraph.js docs.

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API reference

For detailed documentation of all Tavily Extract API features and configurations head to the API reference: docs.tavily.com/documentation/api-reference/endpoint/extract
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