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SerpAPI

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 supportPackage latest
SerpAPI@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 @langchain/core

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.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_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:

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 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: https://api.js.langchain.com/classes/\_langchain_community.tools_serpapi.SerpAPI.html

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