This blog will show you how to give a local LLM access to real-time web data. We'll build a small chat app in TypeScript; the model will decide what to search for, your code will run the search, and the model will write an answer with the fresh data in hand. The inference runs on your hardware, and the only thing that leaves your local environment is the search query the model chose to issue. By the end of this blog, you'll understand the full request flow, you'll know how to keep a large search response from blowing past your context window, and you'll have a public repository you can clone and run against your own local model. Why LLMs Don't Have Access to Real-Time Data Every large language model is limited by its training data cutoff. Anything that happened after that date is invisible to the model. That's true whether you run the model locally or call a hosted API. Cloud chat products hide this from most users.…