Create Llama

The easiest way to get started with LlamaIndex is by using create-llama. This CLI tool enables you to quickly start building a new LlamaIndex application, with everything set up for you.

Get started

Just run

npx create-llama@latest

to get started, or watch this video for a demo session:

Once your app is generated, run

npm run dev

to start the development server. You can then visit http://localhost:3000 to see your app.

What you'll get

• A set of pre-configured use cases to get you started, e.g. Agentic RAG, Data Analysis, Report Generation, etc.
• A front-end using components from shadcn/ui. The app is set up as a chat interface that can answer questions about your data or interact with your agent
• Your choice of two frameworks:
  • Next.js: if you select this option, you’ll have a full-stack Next.js application that you can deploy to a host like Vercel in just a few clicks. This uses LlamaIndex.TS, our TypeScript library with LlamaIndex Server for TS.
  • Python FastAPI: if you select this option, you’ll get full-stack Python application powered by the llama-index Python package and LlamaIndex Server for Python
• The app uses OpenAI by default, so you'll need an OpenAI API key, or you can customize it to use any of the dozens of LLMs we support.

Here's how it looks like:

generated-app.mp4

Using your data

Optionally, you can supply your own data; the app will index it and make use of it, e.g. to answer questions. Your generated app will have a folder called data.

The app will ingest any supported files you put in this directory. Your Next.js apps use LlamaIndex.TS, so they will be able to ingest any PDF, text, CSV, Markdown, Word and HTML files. The Python backend can read even more types, including video and audio files.

Before you can use your data, you need to index it. If you're using the Next.js apps, run:

npm run generate

Then re-start your app. Remember you'll need to re-run generate if you add new files to your data folder.

If you're using the Python backend, you can trigger indexing of your data by calling:

uv run generate

Customizing the AI models

The app will default to OpenAI's gpt-4.1 LLM and text-embedding-3-large embedding model.

If you want to use different models, add the --ask-models CLI parameter.

You can also replace one of the default models with one of our dozens of other supported LLMs.

To do so, you have to manually change the generated code (edit the settings.ts file for Typescript projects or the settings.py file for Python projects)

Example

The simplest thing to do is run create-llama in interactive mode:

npx create-llama@latest
# or
npm create llama@latest
# or
yarn create llama
# or
pnpm create llama@latest

You will be asked for the name of your project, along with other configuration options, something like this:

>> npm create llama@latest
Need to install the following packages:
  create-llama@latest
Ok to proceed? (y) y
✔ What is your project named? … my-app
✔ What use case do you want to build? › Agentic RAG
✔ What language do you want to use? › Python (FastAPI)
✔ Do you want to use LlamaCloud services? … No / Yes
✔ Please provide your LlamaCloud API key (leave blank to skip): …
? How would you like to proceed? › - Use arrow-keys. Return to submit.
    Just generate code (~1 sec)
❯   Start in VSCode (~1 sec)
    Generate code and install dependencies (~2 min)

Running non-interactively

You can also pass command line arguments to set up a new project non-interactively. For a list of the latest options, call create-llama --help.

LlamaIndex Documentation

• TS/JS docs
• Python docs

LlamaIndex Server

The generated code is using the LlamaIndex Server, which serves LlamaIndex Workflows and Agent Workflows via an API server. See the following docs for more information:

• LlamaIndex Server For TypeScript
• LlamaIndex Server For Python

Inspired by and adapted from create-next-app