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The easiest way to get started with LlamaIndex
| Date | Stars |
|---|---|
| 2026-07-31 | 1490 |
| 2026-08-01 | 1490 |
| 2026-08-04 | 1489 |
| 2026-08-06 | 1489 |
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# Create Llama
The easiest way to get started with [LlamaIndex](https://www.llamaindex.ai/) 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
```bash
npx create-llama@latest
```
to get started, or watch this video for a demo session:
<img src="https://github.com/user-attachments/assets/c4a7fe18-8e30-498a-96f8-78127dd706b9" width="100%">
Once your app is generated, run
```bash
npm run dev
```
to start the development server. You can then visit [http://localhost:3000](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](https://ui.shadcn.com/). 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](https://vercel.com/) in just a few clicks. This uses [LlamaIndex.TS](https://www.npmjs.com/package/llamaindex), our TypeScript library with [LlamaIndex Server for TS](https://npmjs.com/package/@llamaindex/server).
- **Python FastAPI**: if you select this option, you’ll get full-stack Python application powered by the [llama-index Python package](https://pypi.org/project/llama-index/) and [LlamaIndex Server for Python](https://pypi.org/project/llama-index-server/)
- 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
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:
```bash
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:
```bash
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](https://docs.llamaindex.ai/en/stable/module_guides/models/llms/modules.html).
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:
```bash
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:
```bash
>> 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)
GeneratExcerpt of 4,836 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8de8d233c4d60490, llm:description: 'The easiest way to get started with LlamaIndex'
matched fp:8de8d233c4d60490, llm:description: 'The easiest way to get started with LlamaIndex'
matched fp:8de8d233c4d60490, llm:description: 'The easiest way to get started with LlamaIndex'