TypeScript

Install the TypeScript/Node.js SDK and start building AI applications with semantic memory.

Build AI applications with TypeScript or JavaScript using the Graphlit SDK.

New to Graphlit? Complete the Quickstart tutorial for a hands-on introduction.

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Installation

Install the Graphlit client with npm or yarn:

npm

npm install graphlit-client

yarn

pnpm

Requirements:

• Node.js 18 or higher

• Graphlit account with API credentials

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Quick Start

TypeScript

import { Graphlit } from 'graphlit-client';

async function main() {
  const graphlit = new Graphlit();

  const response = await graphlit.ingestText(
    'Our AI agent needs persistent memory across sessions...',
    'Product Requirements',
  );

  console.log(`✅ Memory created: ${response.ingestText.id}`);
}

main().catch((error) => {
  console.error(error);
  process.exit(1);
});

JavaScript (ESM)

JavaScript (CommonJS)

That's it! You now have semantic memory for your AI application.

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Configuration Options

Environment Variables (Recommended)

import { Graphlit } from 'graphlit-client';

const graphlit = new Graphlit();

Security: Never commit credentials to git. Use environment variables or secrets management in production.

Alternative: Explicit Configuration

Only use if you need to override environment variables:

import { Graphlit } from 'graphlit-client';

const graphlit = new Graphlit({
  organizationId: process.env.GRAPHLIT_ORGANIZATION_ID,
  environmentId: process.env.GRAPHLIT_ENVIRONMENT_ID,
  jwtSecret: process.env.GRAPHLIT_JWT_SECRET,
});

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Common Patterns

Ingest Content

// From URL
const pdf = await graphlit.ingestUri(
  'https://example.com/document.pdf',
  'Product Brief',
  undefined,
  true,
);

console.log(`📄 PDF ready: ${pdf.ingestUri.id}`);

// From text
const notes = await graphlit.ingestText(
  'Discussion about Q4 planning...',
  'Meeting Notes',
);

console.log(`📝 Notes ready: ${notes.ingestText.id}`);

Search Memory

const response = await graphlit.queryContents({
  search: 'quarterly planning'
});

for (const content of response.contents?.results ?? []) {
  console.log(`📄 ${content.name}`);
}

Chat with Context

// Create conversation
const conversation = await graphlit.createConversation({
  name: 'AI Assistant',
});

// Ask questions
const answer = await graphlit.promptConversation(
  'What did we discuss about Q4 planning?',
  conversation.createConversation.id,
);

console.log(answer.promptConversation.message?.message);

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Next Steps

Quickstarts:

• Quickstart: Your First Agent - Build a streaming agent in 7 minutes

• AI Agents - Create agents with persistent memory

• MCP Integration - Connect to your IDE

Examples:

• Next.js Applications - Full-stack chat apps

• MCP Server - Production MCP implementation

Resources:

• TypeScript SDK on GitHub

• Use Case Library - 100+ code examples

• Ask Graphlit - AI code assistant

• Join Discord - Get help from the community