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This notebook provides a quick overview for getting started with DuckDuckGoSearch. For detailed documentation of all DuckDuckGoSearch features and configurations head to the API reference. DuckDuckGoSearch offers a privacy-focused search API designed for LLM Agents. It provides seamless integration with a wide range of data sources, prioritizing user privacy and relevant search results.

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Overview

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Integration details

ClassPackagePY supportVersion
DuckDuckGoSearch@langchain/community✅NPM - Version

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Setup

The integration lives in the @langchain/community package, along with the duck-duck-scrape dependency: 
npm install @langchain/community @langchain/core duck-duck-scrape

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Credentials

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 instantiate an instance of the DuckDuckGoSearch tool like this: 
import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search"

const tool = new DuckDuckGoSearch({ maxResults: 1 })

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Invocation

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Invoke directly with args

await tool.invoke("what is the current weather in sf?")

[{"title":"San Francisco, CA Current Weather | AccuWeather","link":"https://www.accuweather.com/en/us/san-francisco/94103/current-weather/347629","snippet":"<b>Current</b> <b>weather</b> <b>in</b> San Francisco, CA. Check <b>current</b> conditions in San Francisco, CA with radar, hourly, and more."}]

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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: {
    input: "what is the current weather in sf?"
  },
  id: "tool_call_id",
  name: tool.name,
  type: "tool_call",
}
await tool.invoke(modelGeneratedToolCall)

ToolMessage {
  "content": "[{\"title\":\"San Francisco, CA Weather Conditions | Weather Underground\",\"link\":\"https://www.wunderground.com/weather/us/ca/san-francisco\",\"snippet\":\"San Francisco <b>Weather</b> Forecasts. <b>Weather</b> Underground provides local & long-range <b>weather</b> forecasts, weatherreports, maps & tropical <b>weather</b> conditions for the San Francisco area.\"}]",
  "name": "duckduckgo-search",
  "additional_kwargs": {},
  "response_metadata": {},
  "tool_call_id": "tool_call_id"
}

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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-mini",
})

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("how many people have climbed mount everest?");

const { tool_calls, content } = toolChainResult;

console.log("AIMessage", JSON.stringify({
  tool_calls,
  content,
}, null, 2));

AIMessage {
  "tool_calls": [],
  "content": "As of December 2023, a total of 6,664 different people have reached the summit of Mount Everest."
}

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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 DuckDuckGoSearch features and configurations head to the API reference
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