HyperAgent

AI Browser Automation

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HyperAgent is a powerful tool for automating repetitive tasks in web scraping and data extraction. It provides a user-friendly interface to create custom web scraping scripts without the need for extensive coding knowledge. With HyperAgent, users can easily extract data from websites, transform it into structured formats, and save it for further analysis. The tool supports various data formats and offers scheduling options for automated data extraction at regular intervals. HyperAgent is suitable for individuals and businesses looking to streamline their data collection processes and improve efficiency in extracting information from the web.

README:

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!

View HyperAgent docs here: https://www.hyperbrowser.ai/docs/hyperagent/introduction

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)
📼 Action Caching – Record and replay workflows deterministically without LLM calls

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 { z } from "zod";

// Initialize the agent
const agent = new HyperAgent({
  llm: {
    provider: "openai",
    model: "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, page.perform, 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");
await page.perform("click the search button");
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();

Two Modes of Operation

HyperAgent provides two complementary APIs optimized for different use cases:

🎯 `page.perform()` - Single Granular Actions

page.aiAction() is deprecated and remains available as an alias; prefer page.perform() going forward.

Best for: Single, specific actions like "click login", "fill email with [email protected]"

Advantages:

⚡ Fast - Uses accessibility tree (no screenshots)
💰 Cheap - Single LLM call per action
🎯 Reliable - Direct element finding and execution
📊 Efficient - Text-based DOM analysis with automatic ad-frame filtering

Example:

const page = await agent.newPage();
await page.goto("https://example.com/login");

// Fast, reliable single actions
await page.perform("fill email with [email protected]");
await page.perform("fill password with mypassword");
await page.perform("click the login button");

🧠 `page.ai()` - Complex Multi-Step Tasks

Best for: Complex workflows requiring multiple steps and visual context

Advantages:

🖼️ Visual Understanding - Can use screenshots with element overlays
🎭 Complex Tasks - Handles multi-step workflows automatically
🧠 Context-Aware - Better at understanding page layout and relationships
🔄 Adaptive - Can adjust strategy based on page state

Parameters:

useDomCache (boolean): Reuse DOM snapshots for speed
enableVisualMode (boolean): Enable screenshots and overlays (default: false)

Example:

const page = await agent.newPage();
await page.goto("https://flights.google.com");

// Complex task with multiple steps handled automatically
await page.ai("search for flights from Miami to New Orleans on July 16", {
  useDomCache: true,
});

🎨 Mix and Match

Combine both APIs for optimal performance:

// Use perform for fast, reliable individual actions
await page.perform("click the search button");
await page.perform("type laptop into search");

// Use ai() for complex, multi-step workflows
await page.ai("filter results by price under $1000 and sort by rating");

// Extract structured data
const products = await page.extract(
  "get the top 5 products",
  z.object({
    products: z.array(z.object({ name: z.string(), price: z.number() }))
  })
);

☁️ 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 with native SDKs for better performance and reliability.

// Using OpenAI
const agent = new HyperAgent({
  llm: {
    provider: "openai",
    model: "gpt-4o",
  },
});

// Using Anthropic's Claude
const agent = new HyperAgent({
  llm: {
    provider: "anthropic",
    model: "claude-sonnet-4-0",
  },
});

// Using Google Gemini
const agent = new HyperAgent({
  llm: {
    provider: "gemini",
    model: "gemini-2.5-flash",
  },
});

// Using DeepSeek
const agent = new HyperAgent({
  llm: {
    provider: "deepseek",
    model: "deepseek-chat",
  },
});

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: { search: string }
  ): 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: `Successfully performed search for query ${params.search}. Got results: \n${results}`,
    };
  },
};

const agent = new HyperAgent({
  customActions: [RunSearchActionDefinition],
});

const result = await agent.executeTask(
  "Search about the news for today in New York"
);

📼 Action Caching

HyperAgent automatically records every action during page.ai() runs, capturing XPaths, frame indices, and execution details. This enables deterministic replay without LLM calls—perfect for regression testing, CI pipelines, and cost optimization.

How It Works

Every page.ai() run produces an actionCache containing the exact sequence of actions performed:

const page = await agent.newPage();
const { actionCache } = await page.ai(
  "Go to flights.google.com and search for flights from Rio to LAX"
);

// actionCache contains the recorded steps
console.log(actionCache);

Example cache entry:

{
  "taskId": "86d13abe-b9f3-4ca3-a9bb-bdeddf234cd1",
  "createdAt": "2025-12-06T05:44:52.257Z",
  "status": "completed",
  "steps": [
    {
      "stepIndex": 0,
      "instruction": "Click on the departure field",
      "elementId": "0-138",
      "method": "click",
      "arguments": [],
      "frameIndex": 0,
      "xpath": "/html[1]/body[1]/div[1]/input[1]",
      "actionType": "actElement",
      "success": true
    }
  ]
}

Replaying Cached Actions

Replay a recorded session using runFromActionCache(). It attempts XPath-based execution first (no LLM calls), falling back to LLM only if the page structure has changed:

import { ActionCacheOutput } from "@hyperbrowser/agent";
import fs from "fs";

// Load a previously saved action cache
const cache: ActionCacheOutput = JSON.parse(
  fs.readFileSync("action-cache.json", "utf-8")
);

const agent = new HyperAgent();
const page = await agent.newPage();

// Replay the cached actions
const replay = await page.runFromActionCache(cache, {
  maxXPathRetries: 3,  // Retry XPath resolution up to 3 times before LLM fallback
  debug: true,
});

console.log(replay);
// {
//   replayId: "...",
//   sourceTaskId: "86d13abe-...",
//   status: "completed",
//   steps: [{ stepIndex: 0, usedXPath: true, fallbackUsed: false, success: true }]
// }

await agent.closeAgent();

Generating Replay Scripts

Generate a standalone TypeScript script from an action cache for easy integration into your test suite:

const { actionCache } = await page.ai("search for flights from Miami to NYC");

// Generate a replay script
const script = agent.createScriptFromActionCache(actionCache.steps);
console.log(script);

This produces a script using typed helper methods like performClick(), performType(), etc., that can be run independently.

Use Cases

Regression Testing: Record a workflow once, replay it in CI without LLM costs
Flaky Test Debugging: Compare XPath-based replay vs LLM-driven execution
Cost Optimization: Cache expensive multi-step workflows and replay deterministically
Workflow Templates: Save common flows (login, checkout) and replay across environments

CDP First

HyperAgent speaks Chrome DevTools Protocol natively. Element lookup, scrolling, typing, frame management, and screenshots all go through CDP so every action has exact coordinates, execution contexts, and browser events. This allows for more custom commands and deep iframe tracking.

HyperAgent integrates seamlessly with Playwright, so you can still use familiar commands, while the actions take full advantage of native CDP protocol with fast locators and advanced iframe tracking.

Key Features:

Auto-Ad Filtering: Automatically filters out ad and tracking iframes to keep context clean
Deep Iframe Support: Tracking across nested and cross-origin iframes (OOPIFs)
Exact Coordinates: Actions use precise CDP coordinates for reliability

Keep in mind that CDP is still experimental, and stability is not guaranteed. If you'd like the agent to use Playwright's native locators/actions instead, set cdpActions: false when you create the agent and it will fall back automatically.

The CDP layer is still evolving—expect rapid polish (and the occasional sharp edge). If you hit something quirky you can toggle CDP off for that workflow and drop us a bug report.

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

For Tasks:

Click tags to check more tools for each tasks

extract data automate tasks analyze information scrape websites transform data

For Jobs:

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quadratic

Quadratic is a modern multiplayer spreadsheet application that integrates Python, AI, and SQL functionalities. It aims to streamline team collaboration and data analysis by enabling users to pull data from various sources and utilize popular data science tools. The application supports building dashboards, creating internal tools, mixing data from different sources, exploring data for insights, visualizing Python workflows, and facilitating collaboration between technical and non-technical team members. Quadratic is built with Rust + WASM + WebGL to ensure seamless performance in the browser, and it offers features like WebGL Grid, local file management, Python and Pandas support, Excel formula support, multiplayer capabilities, charts and graphs, and team support. The tool is currently in Beta with ongoing development for additional features like JS support, SQL database support, and AI auto-complete.

github

: 3.8k

HyperAgent

README:

Overview

Features

Quick Start

Installation

CLI

Library

Two Modes of Operation

🎯 page.perform() - Single Granular Actions

🧠 page.ai() - Complex Multi-Step Tasks

🎨 Mix and Match

☁️ Cloud

Usage Guide

Multi-Page Management

Customization

Output Schema Definition

Using Different LLM Providers

MCP Support

Custom Actions

📼 Action Caching

How It Works

Replaying Cached Actions

Generating Replay Scripts

Use Cases

CDP First

Contributing

Support

For Tasks:

For Jobs:

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quadratic

🎯 `page.perform()` - Single Granular Actions

🧠 `page.ai()` - Complex Multi-Step Tasks