mcpserver.so LogoMCPFly
Submit
AgentKit Browser Automation logo

AgentKit Browser Automation

by tmahesh

Automationbrowser automationmulti-agent systemweb navigationtask executionAgentKitPlaywright

A sophisticated browser automation framework built with AgentKit, featuring a multi-agent system for intelligent web navigation and task execution. It implements a multi-agent system where different agents work together to plan, navigate, execute, and validate web tasks.

View on GitHub

Last updated: N/A

AgentKit Browser Automation

A sophisticated browser automation framework built with AgentKit, featuring a multi-agent system for intelligent web navigation and task execution.

Overview

This project implements a multi-agent system for browser automation, where different agents work together to:

  • Plan and break down tasks
  • Navigate web pages
  • Execute browser actions
  • Validate results

Architecture (TODO)

The system consists of four specialized agents:

  1. Planning Agent

    • Breaks down tasks into actionable steps
    • Creates detailed execution plans
    • Determines task completion criteria

  2. Navigator Agent

    • Determines the next actions to take
    • Manages state transitions
    • Handles action execution
    • Provides detailed logging and feedback

  3. Browser Agent

    • Executes browser automation actions
    • Interacts with web elements
    • Handles page navigation
    • Manages browser state

  4. Validation Agent

    • Validates task completion
    • Verifies results
    • Handles error cases
    • Provides feedback on success/failure

Features

  • Intelligent Task Planning: Breaks down complex tasks into manageable steps
  • State Management: Tracks browser state and action results
  • Error Handling: Robust error handling and recovery mechanisms
  • Event System: Comprehensive event logging and monitoring
  • Flexible Action System: Extensible action registry for custom behaviors
  • Validation Framework: Built-in validation for task completion
  • Memory Management: Maintains context and history of actions

Getting Started

Prerequisites

  • Node.js (v14 or higher)
  • npm or yarn
  • OpenAI API key (for GPT models)

Installation

  1. Clone the repository:
git clone https://github.com/tmahesh/playwright-agent.git
cd playwright-agent
  1. Install dependencies:
npm install
  1. Set up environment variables:
cp .env.sample .env
# Edit .env with your OpenAI API key and other configurations
  1. run these commands on diff terminals: index.ts, playwright-mcp, inngest-cli
npx @playwright/mcp@latest --port 8931

npx tsx index.ts

npx inngest-cli@latest dev --no-discovery -u http://localhost:3000/api/inngest -v

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

Acknowledgments