Introduction
How to Integrate Open-Source LLMs Into Existing React Applications: Adding AI to a React application can sound like the kind of project that requires rebuilding everything from scratch.
It does not.
If you already have a working React application, you can introduce an open-source large language model (LLM) without throwing away your existing components, routing, state management, or user interface. The trick is deciding where the model should run and how your application should communicate with it.
There are two practical approaches: connect your React frontend to an LLM running on a server, or run a smaller model directly inside the browser.
For most production applications, the first approach is easier to manage. For lightweight, privacy-focused experiences, browser-based inference can be surprisingly useful.
Here is how to approach both.
What You Need Before Adding an Open-Source LLM
Before touching your React code, decide what you actually want the AI feature to accomplish.
An application might use an LLM for:
- Customer-support chat
- Text summarization
- Document analysis
- Writing assistance
- Search and question answering
- Content classification
- Data extraction
- Coding assistance
This matters because an LLM should solve a specific problem rather than simply become another feature on the dashboard.
You also need to decide where inference will happen.
A useful architecture looks like this:
React UI → Backend/API → Open-source LLM → Response → React UI
This keeps the model infrastructure separate from the frontend and gives you more control over authentication, rate limits, logging, and sensitive information.