AgentKit Browser Automation
agentkit for playwright-mcp server
tmahesh
README
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:
-
Planning Agent
- Breaks down tasks into actionable steps
- Creates detailed execution plans
- Determines task completion criteria
-
Navigator Agent
- Determines the next actions to take
- Manages state transitions
- Handles action execution
- Provides detailed logging and feedback
-
Browser Agent
- Executes browser automation actions
- Interacts with web elements
- Handles page navigation
- Manages browser state
-
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
- Clone the repository:
git clone https://github.com/tmahesh/playwright-agent.git
cd playwright-agent
- Install dependencies:
npm install
- Set up environment variables:
cp .env.sample .env
# Edit .env with your OpenAI API key and other configurations
- 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
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
Acknowledgments
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Playwright MCP Server
Provides a server utilizing Model Context Protocol to enable human-like browser automation with Playwright, allowing control over browser actions such as navigation, element interaction, and scrolling.
@kazuph/mcp-fetch
Model Context Protocol server for fetching web content and processing images. This allows Claude Desktop (or any MCP client) to fetch web content and handle images appropriately.
DuckDuckGo MCP Server
A Model Context Protocol (MCP) server that provides web search capabilities through DuckDuckGo, with additional features for content fetching and parsing.
YouTube Transcript MCP Server
This server retrieves transcripts for given YouTube video URLs, enabling integration with Goose CLI or Goose Desktop for transcript extraction and processing.
serper-search-scrape-mcp-server
This Serper MCP Server supports search and webpage scraping, and all the most recent parameters introduced by the Serper API, like location.
The Verge News MCP Server
Provides tools to fetch and search news from The Verge's RSS feed, allowing users to get today's news, retrieve random articles from the past week, and search for specific keywords in recent Verge content.
Tavily MCP Server
Provides AI-powered web search capabilities using Tavily's search API, enabling LLMs to perform sophisticated web searches, get direct answers to questions, and search recent news articles.
mcp-pinterest
A Pinterest Model Context Protocol (MCP) server for image search and information retrieval

Crawlab MCP Server