Installation

Quick Start

Install the core package:

npm i llamaindex

In most cases, you’ll also need an LLM provider and the Workflow package:

npm i @llamaindex/openai @llamaindex/workflow

Environment Setup

API Keys

Most LLM providers require API keys. Set your OpenAI key (or other provider):

export OPENAI_API_KEY=your-api-key

Or use a .env file:

echo "OPENAI_API_KEY=your-api-key" > .env

Caution

Never commit API keys to your repository.

Loading Environment Variables

For Node.js applications:

node --env-file .env your-script.js

For other environments, see the deployment-specific guides below.

TypeScript Configuration

LlamaIndex.TS is built with TypeScript and provides excellent type safety. Add these settings to your tsconfig.json:

{
  "compilerOptions": {
    // Essential for module resolution
    "moduleResolution": "bundler", // or "nodenext" | "node16" | "node"

    // Required for Web Stream API support
    "lib": ["DOM.AsyncIterable"],

    // Recommended for better compatibility
    "target": "es2020",
    "module": "esnext"
  }
}

Running your first agent

Set up

If you don’t already have a project, you can create a new one in a new folder:

npm init
npm i -D typescript @types/node
npm i @llamaindex/openai @llamaindex/workflow llamaindex zod

Run the agent

Create the file example.ts. This code will:

• Create two tools for use by the agent:
  • A sumNumbers tool that adds two numbers
  • A divideNumbers tool that divides numbers
• Give an example of the data structure we wish to generate
• Prompt the LLM with instructions and the example, plus a sample transcript

../../examples/agents/agent/openai.ts

To run the code:

npx tsx example.ts

You should expect output something like:

{
  result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
  state: {
    memory: Memory {
      messages: [Array],
      tokenLimit: 30000,
      shortTermTokenLimitRatio: 0.7,
      memoryBlocks: [],
      memoryCursor: 0,
      adapters: [Object]
    },
    scratchpad: [],
    currentAgentName: 'Agent',
    agents: [ 'Agent' ],
    nextAgentName: null
  }
}
Done

Performance Optimization

Tokenization Speed

Install gpt-tokenizer for 60x faster tokenization (Node.js environments only):

npm i gpt-tokenizer

LlamaIndex will automatically use this when available.

Deployment Guides

Choose your deployment target:

Server APIs & Backends

Serverless Functions

Next.js Applications

Troubleshooting

LLM/Embedding Providers

Go to LLM APIs and Embedding APIs to find out how to use different LLM and embedding providers beyond OpenAI.

What’s Next?

Learn LlamaIndex.TS

Show me code examples