Intelligent Browser Automation with LLMs

Overview

Hyperagent is Playwright supercharged with AI. No more brittle scripts, just powerful natural language commands. Just looking for scalable headless browsers or scraping infra? Go to Hyperbrowser to get started for free!

Features

🤖 AI Commands: Simple APIs like page.ai(), page.extract() and executeTask() for any AI automation
⚡ Fallback to Regular Playwright: Use regular Playwright when AI isn't needed
🥷 Stealth Mode – Avoid detection with built-in anti-bot patches
☁️ Cloud Ready – Instantly scale to hundreds of sessions via Hyperbrowser
🔌 MCP Client – Connect to tools like Composio for full workflows (e.g. writing web data to Google Sheets)

Quick Start

Installation

# Using npm
npm install @hyperbrowser/agent

# Using yarn
yarn add @hyperbrowser/agent

CLI

$ npx @hyperbrowser/agent -c "Find a route from Miami to New Orleans, and provide the detailed route information."

The CLI supports options for debugging or using hyperbrowser instead of a local browser

-d, --debug                       Enable debug mode
-c, --command <task description>  Command to run
--hyperbrowser                    Use Hyperbrowser for the browser provider

Library

import { HyperAgent } from "@hyperbrowser/agent";
import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

// Initialize the agent
const agent = new HyperAgent({
  llm: new ChatOpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    modelName: "gpt-4o",
  }),
});

// Execute a task
const result = await agent.executeTask(
  "Navigate to amazon.com, search for 'laptop', and extract the prices of the first 5 results"
);
console.log(result.output);

// Use page.ai and page.extract
const page = await agent.newPage();
await page.goto("https://flights.google.com", { waitUntil: "load" });
await page.ai("search for flights from Rio to LAX from July 16 to July 22");
const res = await page.extract(
  "give me the flight options",
  z.object({
    flights: z.array(
      z.object({
        price: z.number(),
        departure: z.string(),
        arrival: z.string(),
      })
    ),
  })
);
console.log(res);

// Clean up
await agent.closeAgent();

☁️ Cloud

You can scale HyperAgent with cloud headless browsers using Hyperbrowser

Get a free api key from Hyperbrowser
Add it to your env as HYPERBROWSER_API_KEY
Set your browserProvider to "Hyperbrowser"

const agent = new HyperAgent({
  browserProvider: "Hyperbrowser",
});

const response = await agent.executeTask(
  "Go to hackernews, and list me the 5 most recent article titles"
);

console.log(response);
await agent.closeAgent();

Usage Guide

Multi-Page Management

// Create and manage multiple pages
const page1 = await agent.newPage();
const page2 = await agent.newPage();

// Execute tasks on specific pages
const page1Response = await page1.ai(
  "Go to google.com/travel/explore and set the starting location to New York. Then, return to me the first recommended destination that shows up. Return to me only the name of the location."
);
const page2Response = await page2.ai(
  `I want to plan a trip to ${page1Response.output}. Recommend me places to visit there.`
);

console.log(page2Response.output);

// Get all active pages
const pages = await agent.getPages();
await agent.closeAgent();

Customization

Output Schema Definition

HyperAgent can extract data in a specified schema. The schema can be passed in at a per-task level

import { z } from "zod";

const agent = new HyperAgent();
const agentResponse = await agent.executeTask(
  "Navigate to imdb.com, search for 'The Matrix', and extract the director, release year, and rating",
  {
    outputSchema: z.object({
      director: z.string().describe("The name of the movie director"),
      releaseYear: z.number().describe("The year the movie was released"),
      rating: z.string().describe("The IMDb rating of the movie"),
    }),
  }
);
console.log(agentResponse.output);
await agent.closeAgent();

{
  "director": "Lana Wachowski, Lilly Wachowski",
  "releaseYear": 1999,
  "rating": "8.7/10"
}

Using Different LLM Providers

Hyperagent supports multiple LLM providers. A provider can be anything that extends to the Langchain BaseChatModel class.

// Using OpenAI
const agent = new HyperAgent({
  llm: new ChatOpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    modelName: "gpt-4o",
  }),
});

// Using Anthropic's Claude
const agent = new HyperAgent({
  llm: new ChatAnthropic({
    anthropicApiKey: process.env.ANTHROPIC_API_KEY,
    modelName: "claude-3-7-sonnet-latest",
  }),
});

MCP Support

HyperAgent functions as a fully functional MCP client. For best results, we recommend using gpt-4o as your LLM.

Here is an example which reads from wikipedia, and inserts information into a google sheet using the composio Google Sheet MCP. For the full example, see here

const agent = new HyperAgent({
  llm: llm,
  debug: true,
});

await agent.initializeMCPClient({
  servers: [
    {
      command: "npx",
      args: [
        "@composio/mcp@latest",
        "start",
        "--url",
        "https://mcp.composio.dev/googlesheets/...",
      ],
      env: {
        npm_config_yes: "true",
      },
    },
  ],
});

const response = await agent.executeTask(
  "Go to https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population and get the data on the top 5 most populous states from the table. Then insert that data into a google sheet. You may need to first check if there is an active connection to google sheet, and if there isn't connect to it and present me with the link to sign in. "
);

console.log(response);
await agent.closeAgent();

Custom Actions

HyperAgent's capabilities can be extended with custom actions. Custom actions require 3 things:

type: Name of the action. Should be something descriptive about the action.
actionParams: A zod object describing the parameters that the action may consume.
run: A function that takes in a context, and the params for the action and produces a result based on the params.

Here is an example that performs a search using Exa

const exaInstance = new Exa(process.env.EXA_API_KEY);

export const RunSearchActionDefinition: AgentActionDefinition = {
  type: "perform_search",
  actionParams: z.object({
    search: z
      .string()
      .describe(
        "The search query for something you want to search about. Keep the search query concise and to-the-point."
      ),
  }).describe("Search and return the results for a given query.");,
  run: async function (
    ctx: ActionContext,
    params: z.infer<typeof searchSchema>
  ): Promise<ActionOutput> {
    const results = (await exaInstance.search(params.search, {})).results
      .map(
        (res) =>
          `title: ${res.title} || url: ${res.url} || relevance: ${res.score}`
      )
      .join("\n");

    return {
      success: true,
      message: `Succesfully performed search for query ${params.search}. Got results: \n${results}`,
    };
  },
};

const agent = new HyperAgent({
  "Search about the news for today in New York",
  customActions: [RunSearchActionDefinition],
});

Contributing

We welcome contributions to Hyperagent! Here's how you can help:

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Support

License

amiradel10/HyperAgent

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