> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/run-llama/LlamaIndexTS/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction to LlamaIndex.TS
> Data framework for building LLM applications with your own data
# Welcome to LlamaIndex.TS
**LlamaIndex.TS** is a data framework for building production-ready LLM applications in TypeScript and JavaScript. Use it to integrate large language models with your own data through powerful patterns like RAG (Retrieval-Augmented Generation), agents, and workflows.
Get started in minutes with your first RAG application
Understand the key concepts behind LlamaIndex.TS
Explore comprehensive examples for all features
Browse the complete API documentation
## What Makes LlamaIndex.TS Special?
LlamaIndex.TS is designed to be **lightweight, flexible, and production-ready** for building LLM applications across any JavaScript runtime.
### Multi-Runtime Support
Run your LLM applications anywhere JavaScript runs:
* **Node.js** >= 20 ✅
* **Deno** ✅
* **Bun** ✅
* **Nitro** ✅
* **Vercel Edge Runtime** ✅ (with some limitations)
* **Cloudflare Workers** ✅ (with some limitations)
Browser support is currently limited due to the lack of support for [AsyncLocalStorage-like APIs](https://github.com/tc39/proposal-async-context).
### Key Features
Build powerful retrieval-augmented generation systems with simple, composable APIs for indexing, querying, and chat.
Create sophisticated agentic systems with the modern `@llamaindex/workflow` package for multi-step reasoning and tool usage.
Install only what you need. Provider packages for LLMs, embeddings, and vector stores keep your bundle size minimal.
Works with OpenAI, Anthropic, Groq, Gemini, Llama, Mistral, and many more providers out of the box.
## Supported LLM Providers
LlamaIndex.TS integrates with all major LLM providers:
* OpenAI (GPT-4, GPT-3.5)
* Anthropic (Claude)
* Google (Gemini)
* Groq
* Llama2, Llama3, Llama3.1
* MistralAI
* Fireworks
* DeepSeek
* ReplicateAI
* TogetherAI
* HuggingFace
* DeepInfra
## Use Cases
LlamaIndex.TS powers a wide range of LLM applications:
Build systems that answer questions over your documents, knowledge bases, or databases using RAG.
Create chatbots and assistants that can use tools, access your data, and maintain context across conversations.
Extract insights from PDFs, emails, transcripts, and other unstructured data sources.
Orchestrate multi-step AI workflows with agents that can reason, plan, and execute complex tasks.
## Community and Support
Join our growing community of developers building with LlamaIndex.TS:
Star the repo, report issues, and contribute
Join our Discord community for support and discussions
Follow @llama\_index for updates and announcements
Try LlamaIndex.TS interactively in your browser
## Open Source
LlamaIndex.TS is fully open source and MIT licensed. We welcome contributions from the community!
Check out our [contributing guide](https://github.com/run-llama/LlamaIndexTS/blob/main/CONTRIBUTING.md) to get started with contributing.
## Next Steps
Set up LlamaIndex.TS for your runtime environment
Build your first RAG application in 5 minutes