Loading video player...
š In this groundbreaking tutorial, I'll show you how to build an AI-powered web browsing agent using Python, LangChain, and the Model Context Protocol (MCP) with Microsoft Playwright! š Playwright MCP Server: https://glama.ai/mcp/servers/@microsoft/playwright-mcp šÆ What You'll Learn: ā Understand the Model Context Protocol (MCP) and its role in AI tooling ā Set up a MultiServer MCP client in Python ā Integrate Playwright MCP server for browser automation ā Create LangChain agents with real web browsing capabilities ā Build an AI that can navigate websites and describe what it sees ā Implement checkpointing for state management š» Code Features: ⨠MultiServer MCP client configuration for multiple tools ⨠Seamless integration with Playwright browser automation ⨠LangChain agent creation with GPT-4o-mini ⨠Async/await patterns for efficient execution ⨠In-memory checkpointing for conversation state š ļø Technologies Used: ⢠Python š ⢠LangChain & LangGraph š¤ ⢠Model Context Protocol (MCP) š ⢠Microsoft Playwright š ⢠OpenAI GPT-4o-mini š§ ⢠Async programming ā” š Real-World Applications: ⢠Automated website testing and monitoring ⢠AI-powered web research assistants ⢠Content extraction and analysis ⢠Accessibility testing automation ⢠Competitive intelligence gathering š Perfect For: ⢠Developers wanting to integrate AI with browser automation ⢠Python programmers exploring MCP and LangChain ⢠AI enthusiasts interested in practical agent applications ⢠Anyone looking to automate web tasks with intelligence ⢠DevOps engineers needing automated website checks š Resources & Links: https://glama.ai/mcp/servers/@microsoft/playwright-mcp š¬ Got questions about MCP or browser automation? Ask in the comments! š If this helps you build smarter AI agents, please like and subscribe! š Turn on notifications for more LangChain and AI automation tutorials! šØ Important Note: Always use web automation responsibly and respect website terms of service.