SerpAPI allows you to integrate search engine results into your LLM apps This guide provides a quick overview for getting started with the SerpAPI tool. For detailed documentation of all SerpAPI features and configurations head to the API reference.

​
Overview

​
Integration details

ClassPackagePY supportVersion
SerpAPI@langchain/community✅NPM - Version

​
Setup

The integration lives in the @langchain/community package, which you can install as shown below: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/community @langchain/core
</Npm2Yarn>

​
Credentials

Set up an API key here and set it as an environment variable named SERPAPI_API_KEY. 
process.env.SERPAPI_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 SerpAPI tool like this: 
import { SerpAPI } from "@langchain/community/tools/serpapi";

const tool = new SerpAPI();

​
Invocation

​
Invoke directly with args

You can invoke the tool directly like this: 
await tool.invoke({
  input: "what is the current weather in SF?"
});

{"type":"weather_result","temperature":"63","unit":"Fahrenheit","precipitation":"3%","humidity":"91%","wind":"5 mph","location":"San Francisco, CA","date":"Sunday 9:00 AM","weather":"Mostly cloudy"}

​
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": "{\"type\":\"weather_result\",\"temperature\":\"63\",\"unit\":\"Fahrenheit\",\"precipitation\":\"3%\",\"humidity\":\"91%\",\"wind\":\"5 mph\",\"location\":\"San Francisco, CA\",\"date\":\"Sunday 9:00 AM\",\"weather\":\"Mostly cloudy\"}",
  "name": "search",
  "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: 
<ChatModelTabs customVarName="llm" />

// @lc-docs-hide-cell

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 mostly cloudy, with a temperature of 64°F. The humidity is at 90%, there is a 3% chance of precipitation, and the wind is blowing at 5 mph."
}

​
Agents

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

​
API reference

For detailed documentation of all SerpAPI features and configurations head to the API reference.