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Tavily Extract

Tavily is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed. Tavily offers two 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.

Overview​

Integration details​

ClassPackagePY supportPackage latest
TavilyExtract@langchain/tavily✅NPM - Version

Setup​

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

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/tavily @langchain/core

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";

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,
});

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.

  • Both extractDepth and includeImages can also be set during invocation

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

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);

Chaining​

We can use our tool in a chain by first binding it to a tool-calling model and then calling it:

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/groq

Add environment variables

GROQ_API_KEY=your-api-key

Instantiate the model

import { ChatGroq } from "@langchain/groq";

const llm = new ChatGroq({
  model: "llama-3.3-70b-versatile",
  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
  )
);

Agents​

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

API reference​

For detailed documentation of all Tavily Extract API features and configurations head to the API reference:

https://docs.tavily.com/documentation/api-reference/endpoint/extract

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