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TavilySearchResults

Tavily Search is a robust search API tailored specifically for LLM Agents. It seamlessly integrates with diverse data sources to ensure a superior, relevant search experience.

This guide provides a quick overview for getting started with the Tavily search results tool. For detailed documentation of all TavilySearchResults features and configurations head to the API reference.

Overview​

Integration details​

ClassPackagePY supportPackage latest
TavilySearchResults@langchain/community✅NPM - Version

Setup​

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

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/community

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.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_API_KEY = "your-api-key";

Instantiation​

You can import and instantiate an instance of the TavilySearchResults tool like this:

import { TavilySearchResults } from "@langchain/community/tools/tavily_search";

const tool = new TavilySearchResults({
  maxResults: 2,
  // ...
});

Invocation​

Invoke directly with args​

You can invoke the tool directly like this:

await tool.invoke({
  input: "what is the current weather in SF?",
});
[{"title":"San Francisco, CA Current Weather | AccuWeather","url":"https://www.accuweather.com/en/us/san-francisco/94103/current-weather/347629","content":"Current weather in San Francisco, CA. Check current conditions in San Francisco, CA with radar, hourly, and more.","score":0.9428234,"raw_content":null},{"title":"National Weather Service","url":"https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA","content":"NOAA National Weather Service. Current conditions at SAN FRANCISCO DOWNTOWN (SFOC1) Lat: 37.77056°NLon: 122.42694°WElev: 150.0ft.","score":0.94261247,"raw_content":null}]

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: {
    input: "what is the current weather in SF?",
  },
  id: "1",
  name: tool.name,
  type: "tool_call",
};

await tool.invoke(modelGeneratedToolCall);
ToolMessage {
  "content": "[{\"title\":\"Weather in San Francisco\",\"url\":\"https://www.weatherapi.com/\",\"content\":\"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1722967498, 'localtime': '2024-08-06 11:04'}, 'current': {'last_updated_epoch': 1722967200, 'last_updated': '2024-08-06 11:00', 'temp_c': 18.4, 'temp_f': 65.2, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.9, 'wind_kph': 4.7, 'wind_degree': 275, 'wind_dir': 'W', 'pressure_mb': 1015.0, 'pressure_in': 29.97, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 64, 'cloud': 2, 'feelslike_c': 18.5, 'feelslike_f': 65.2, 'windchill_c': 18.5, 'windchill_f': 65.2, 'heatindex_c': 18.4, 'heatindex_f': 65.2, 'dewpoint_c': 11.7, 'dewpoint_f': 53.1, 'vis_km': 10.0, 'vis_miles': 6.0, 'uv': 5.0, 'gust_mph': 4.3, 'gust_kph': 7.0}}\",\"score\":0.9983156,\"raw_content\":null},{\"title\":\"Weather in San Francisco in June 2024 - Detailed Forecast\",\"url\":\"https://www.easeweather.com/north-america/united-states/california/city-and-county-of-san-francisco/san-francisco/june\",\"content\":\"Until now, June 2024 in San Francisco is slightly cooler than the historical average by -0.6 ° C.. The forecast for June 2024 in San Francisco predicts the temperature to closely align with the historical average at 17.7 ° C. 17.7 ° C.\",\"score\":0.9905143,\"raw_content\":null}]",
  "name": "tavily_search_results_json",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_call_id": "1"
}

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/openai

Add environment variables

OPENAI_API_KEY=your-api-key

Instantiate the model

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  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(
  "what is the current weather in sf?"
);
const { tool_calls, content } = toolChainResult;

console.log(
  "AIMessage",
  JSON.stringify(
    {
      tool_calls,
      content,
    },
    null,
    2
  )
);
AIMessage {
  "tool_calls": [],
  "content": "The current weather in San Francisco is as follows:\n\n- **Condition:** Sunny\n- **Temperature:** 18.4°C (65.2°F)\n- **Wind:** 2.9 mph (4.7 kph) from the west\n- **Humidity:** 64%\n- **Visibility:** 10 km (6 miles)\n- **UV Index:** 5\n\n![Sunny](//cdn.weatherapi.com/weather/64x64/day/113.png)\n\nFor more detailed information, you can visit [WeatherAPI](https://www.weatherapi.com/)."
}

Agents​

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

API reference​

For detailed documentation of all TavilySearchResults features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_tools_tavily_search.TavilySearchResults.html

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