What is an AI framework, like PyTorch or LangChain?

In plain English

Nobody builds AI from scratch. Developers use frameworks: free toolkits of ready-made code for the hard, repetitive parts. There are two main kinds, and the names come up constantly.

Frameworks for building and training models. PyTorch, started at Meta and now run by the PyTorch Foundation under the non-profit Linux Foundation, is the most widely used in research and powers many of today’s big models. TensorFlow, released by Google in 2015, and Google’s newer JAX are the other big names. They handle the maths of neural networks and spread the work across many GPUs.

Frameworks for building apps with models. LangChain and LlamaIndex help developers connect a ready-made model (through an API or an open-weight model) to documents, databases and tools, which is how they build RAG systems and AI agents without writing all the plumbing themselves.

Alongside them sits Hugging Face, a website that works like a library for AI: more than a million models, plus datasets, that anyone can download, and tools to use them.

Why it matters

Frameworks are a big reason AI moves so fast. A small team can download an open model from Hugging Face, adapt it with PyTorch and turn it into an app with LangChain in days, standing on years of shared work. If you work near AI teams, these are the names you will hear in meetings.

An example

A start-up wants a support bot that answers from its help pages. Its developers use LlamaIndex to split the pages into passages and store them in a vector database, call a model through an API to write the answers, and never touch the underlying maths at all.

What to watch out for

Frameworks change quickly, and app frameworks in particular add layers that can make problems hard to trace. Many teams start with one to move fast, then replace parts with simpler code of their own. Using a framework doesn’t remove the need to test: the AI can still give wrong answers, however polished the plumbing.