Tavily extract integration

Integrate with the Tavily extract tool using LangChain JavaScript.

Tavily is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed. Tavily offers an Extract endpoint that can be used to extract the cleaned, parsed content of one or more URLs.

Overview

Integration details

Class	Package	PY support	Downloads	Version
TavilyExtract	@langchain/tavily	✅

Tool features

Returns artifact	Native async	Return data	Pricing
❌	✅	raw content and images	1,000 free credits / month

Setup

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

npm

npm install @langchain/tavily @langchain/core

yarn add @langchain/tavily @langchain/core

pnpm add @langchain/tavily @langchain/core

Credentials

Set up a Tavily API key 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"

Instantiation

The tool accepts the following parameters during instantiation:

• extractDepth (optional, string): Depth of the extraction, either "basic" or "advanced". Default is "basic".
• includeImages (optional, boolean): Whether to include images in the extraction. Default is false.
• format (optional, string): Content format. "markdown" or "text". Default is "markdown".
• includeFavicon (optional, boolean): Include each result’s favicon URL. Default is false.

For a comprehensive overview of the available parameters, refer to the Tavily Extract API documentation.

import { TavilyExtract } from "@langchain/tavily";

const tool = new TavilyExtract({
  extractDepth: "basic",
  includeImages: false,
});

Invocation

Invoke directly with args

The Tavily extract tool accepts the following arguments during invocation:

• urls (required): A list of URLs to extract content from.
• The following arguments can also be set during invocation: extractDepth, includeImages.

NOTE: The optional arguments are available for agents to dynamically set. If you set an argument during instantiation and then invoke the tool with a different value, the tool will use the value you passed during invocation.

await tool.invoke({ urls: ["https://en.wikipedia.org/wiki/Lionel_Messi"] });

{
  results: [{
    url: 'https://en.wikipedia.org/wiki/Lionel_Messi',
    raw_content: 'Lionel Messi - Wikipedia\nJump to content\nMain menu\n... (truncated)',
    images: []
  }],
  failed_results: [],
  response_time: 0.02
}

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",
};

const toolMsg = await tool.invoke(modelGeneratedToolCall);

// The content is a JSON string of results
console.log(toolMsg.content.slice(0, 400));

{"results": [{"url": "https://en.wikipedia.org/wiki/Lionel_Messi", "raw_content": "Lionel Messi - Wikipedia\nJump to content\nMain menu\nMain menu\nmove to sidebar hide\nNavigation\n\nMain page\nContents\nCurrent events\nRandom article\nAbout Wikipedia\nContact us\n\nContribute\n\nHelp\nLearn to edit\nCommunity portal\nRecent changes\nUpload file\nSpecial pages\n... (truncated)"}], "failed_results": [], "response_time": 0.02}

Use within an agent

We can use the extract tool directly with a LangChain agent by passing it to createAgent. The agent can dynamically set the list of URLs and toggles like extractDepth and includeImages as part of its tool call.

// @lc-docs-hide-cell
import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-5.5",
  temperature: 0,
});

import { TavilyExtract } from "@langchain/tavily";
import { createAgent } from "langchain";

const tavilyExtractTool = new TavilyExtract();

const agent = createAgent({
  model: llm,
  tools: [tavilyExtractTool],
});

const userInput = "Summarize https://en.wikipedia.org/wiki/Albert_Einstein and https://en.wikipedia.org/wiki/Theoretical_physics.";

const stream = await agent.streamEvents(
  { messages: [["human", userInput]] },
  { version: "v3" },
);

for await (const snapshot of stream.values) {
  const lastMsg = snapshot.messages[snapshot.messages.length - 1];
  if (lastMsg.tool_calls?.length) {
    console.dir(lastMsg.tool_calls, { depth: null });
  } else if (lastMsg.content) {
    console.log(lastMsg.content);
  }
}

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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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