Documentation Index
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This notebook provides a quick overview for getting started with DuckDuckGoSearch tools. 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.
Overview
Integration details
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
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"
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 })
Invocation
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."}]
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"
}
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-5.4-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."
}
Agents
For guides on how to use LangChain tools in agents, see the LangGraph.js docs.
API reference
For detailed documentation of all DuckDuckGoSearch features and configurations head to the API reference
