Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Learn to build a local AI application with full web access capabilities using Python, LangChain, and the Model Context Protocol (MCP) in this comprehensive 38-minute tutorial. Discover how to create your own language model that can search the internet and answer real-time questions, similar to ChatGPT but running entirely on your local system with complete control. Master the implementation of MCP, a new open protocol that connects Large Language Models to real-time data from the internet and external resources including files, applications, databases, and web pages. Build a complete Python application featuring a local LLM via Ollama that can browse the live web, an asynchronous MCP client with access to tools like web_data_reddit_posts and web_data_linkedin_person_profile, and a Streamlit GUI with user input, real-time answers, and automatic tool-switching. Utilize Bright Data MCP to handle CAPTCHAs, JavaScript rendering, proxies, and more advanced web scraping challenges. Work with a modern tech stack including Python 3.12, LangChain with MCP adapters and Ollama LLM, Ollama running Gemma3 locally, Bright Data MCP for scraping and browser API functionality, and Streamlit for rapid GUI development. Understand what MCP is and how it brings real-time data into LLMs without requiring training or fine-tuning, structure MCP clients in Python with LangChain's async tooling, run everything locally without OpenAI keys or cloud dependencies, cache responses, route URLs to tools, and maintain clean prompts. Perfect for developers who want to build AI agents with direct control over context, need LLMs that can reason over live external data, and prefer local, hackable AI solutions over SaaS restrictions.
