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:

https://github.com/user-attachments/assets/dd3edc36-4453-4416-91c2-d24326c6c167

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 Next.js-powered 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 3 back-ends:
  • 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.
  • Express: if you want a more traditional Node.js application you can generate an Express backend. This also uses LlamaIndex.TS.
  • Python FastAPI: if you select this option, you’ll get a backend powered by the llama-index Python package, which you can deploy to a service like Render or fly.io.
• The back-end has two endpoints (one streaming, the other one non-streaming) that allow you to send the state of your chat and receive additional responses
• You add arbitrary data sources to your chat, like local files, websites, or data retrieved from a database.
• Turn your chat into an AI agent by adding tools (functions called by the LLM).
• 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:

https://github.com/user-attachments/assets/d57af1a1-d99b-4e9c-98d9-4cbd1327eff8

Using your data

You can supply your own data; the app will index it and answer questions. Your generated app will have a folder called data (If you're using Express or Python and generate a frontend, it will be ./backend/data).

The app will ingest any supported files you put in this directory. Your Next.js and Express 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 or Express 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:

poetry run generate

Want a front-end?

Optionally generate a frontend if you've selected the Python or Express back-ends. If you do so, create-llama will generate two folders: frontend, for your Next.js-based frontend code, and backend containing your API.

Customizing the AI models

The app will default to OpenAI's gpt-4o-mini LLM and text-embedding-3-large embedding model.

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

You can also replace OpenAI 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
✔ Which template would you like to use? › Agentic RAG (single agent)
✔ Which framework would you like to use? › NextJS
✔ Would you like to set up observability? › No
✔ Please provide your OpenAI API key (leave blank to skip): …
✔ Which data source would you like to use? › Use an example PDF
✔ Would you like to add another data source? › No
✔ Would you like to use LlamaParse (improved parser for RAG - requires API key)? … no / yes
✔ Would you like to use a vector database? › No, just store the data in the file system
✔ Would you like to build an agent using tools? If so, select the tools here, otherwise just press enter › Weather
? 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)
   Generate code, install dependencies, and run the app (~2 min)

Running non-interactively

You can also pass command line arguments to set up a new project non-interactively. See create-llama --help:

create-llama <project-directory> [options]

Options:
  -V, --version                      output the version number

  --use-npm

    Explicitly tell the CLI to bootstrap the app using npm

  --use-pnpm

    Explicitly tell the CLI to bootstrap the app using pnpm

  --use-yarn

    Explicitly tell the CLI to bootstrap the app using Yarn

LlamaIndex Documentation

• TS/JS docs
• Python docs

Inspired by and adapted from create-next-app