Jan 16
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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: āØ MultiServer MCP client configuration for multiple tools āØ Seamless integration with Playwright browser automation āØ LangChain agent creation with GPT-4o-mini āØ Async/await patterns for efficient execution āØ In-memory checkpointing for conversation state š ļø Technologies Used: ā¢ Python š ā¢ LangChain & LangGraph š¤ ā¢ Model Context Protocol (MCP) š ā¢ Microsoft Playwright š ā¢ OpenAI GPT-4o-mini š§ ā¢ Async programming ā” š Real-World Applications: ā¢ Automated website testing and monitoring ā¢ AI-powered web research assistants ā¢ Content extraction and analysis ā¢ Accessibility testing automation ā¢ Competitive intelligence gathering š Perfect For: ā¢ Developers wanting to integrate AI with browser automation ā¢ Python programmers exploring MCP and LangChain ā¢ AI enthusiasts interested in practical agent applications ā¢ Anyone looking to automate web tasks with intelligence ā¢ 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.