Introduction

The Divinci AI SDK is a family of TypeScript packages for building on the Divinci platform — chat, retrieval-augmented generation (RAG), fine-tuning, evaluation, and pay-per-call tools. Every package shares one type system (@divinci-ai/types) and the same authentication model, so what you learn in one carries over to the rest.

The platform in one paragraph

You configure an AI assistant once as a Release — its model, prompt, RAG knowledge base, theme, and access rules. A Release lives inside a Workspace (also called a white‑label). Your end users chat against a Release; your backend manages Workspaces, Releases, and knowledge bases. The SDK packages split cleanly along that line: client-side packages talk to a Release, the server package administers the platform.

Which package do I need?

@divinci-ai/client

A headless, browser‑safe TypeScript SDK. You own the UI; the client owns chat threads, streaming, realtime WebSockets, and per‑user RAG. Reach for this when building a custom chat experience in React, Vue, Svelte, or vanilla JS.

Embed script

A zero‑build <script> tag that mounts a fully‑rendered Divinci chat widget on any page. No bundler, no npm. Reach for this when you want chat live in minutes and don’t need to control the UI.

@divinci-ai/server

A Node.js SDK with full platform access — Workspaces, Releases, RAG ingestion, API keys, fine‑tuning, QA suites, and x402 payments. Holds your secret API key, so it runs only on a trusted backend.

@divinci-ai/mcp

A Model Context Protocol client. Connects MCP‑compatible assistants (Claude Desktop, Cursor, your own agent) to Divinci tools and resources, with optional x402 pay‑per‑call.

Rule of thumb

If the code runs in a browser, use @divinci-ai/client (or the embed script). If it runs on a server with a secret API key, use @divinci-ai/server. If it bridges an AI assistant to Divinci tools, use @divinci-ai/mcp.

How they fit together

A typical production app uses two packages at once:

• Your backend uses @divinci-ai/server to create a Release, upload RAG documents, and mint short‑lived user tokens.
• Your frontend uses @divinci-ai/client with that token to run the chat, streaming responses directly to the browser.

The @divinci-ai/types package is the shared vocabulary — ChatMessage, Release, RagDocument, and friends. It ships as a dependency of the other packages, so you rarely install it directly, but you can import its types for your own function signatures.

Next steps

Installation Install the package you need with npm, pnpm, or yarn.

Quick Start Send your first message and run your first server operation.

Authentication API keys, user tokens, and external users — and where each belongs.