Quickstart

`@gram-ai/elements`

Elements is a library built for the agentic age. We provide customizable and elegant UI primitives for building chat-like experiences for MCP Servers. Works best with Gram MCP, but also supports connecting any MCP Server.

Setup

Frontend Setup

The easiest way to install Elements and all its peer dependencies:


npx @gram-ai/elements install

This will automatically detect your package manager and install everything you need.

Manual installation

If you prefer to install manually, first install the peer dependencies:


pnpm add react react-dom motion remark-gfm zustand vega shiki

Then install Elements:


pnpm add @gram-ai/elements

Backend Setup

If you’re only using the server handlers (@gram-ai/elements/server), you can install without needing to install any of the React peer dependencies:


pnpm add @gram-ai/elements

Your package manager may show peer dependency warnings for React packages. These warnings are safe to ignore when using only /server exports, as React dependencies are marked as optional.

Setting up your backend

At the moment, we provide a set of handlers via the @gram-ai/elements/server package that you can use to automatically setup your backend for usage with Gram Elements. The example below demonstrates the setup for Express:


import { createElementsServerHandlers } from '@gram-ai/elements/server'
import express from 'express'
 
const handlers = createElementsServerHandlers()
const app = express()
 
app.use(express.json())
 
app.post('/chat/session', (req, res) =>
  handlers.session(req, res, {
    // The origin from which the token will be used
    embedOrigin: 'http://localhost:3000',
    // A free-form user identifier
    userIdentifier: 'user-123',
    // Token expiration in seconds (max / default 3600)
    expiresAfter: 3600,
  })
)

You will need to add an environment variable to your backend and make it available to the process:


GRAM_API_KEY=xxx

This will enable your backend chat endpoint to talk to our servers securely.

Setting up your frontend

@gram-ai/elements requires that you wrap your React tree with our context provider:


import { GramElementsProvider, Chat, type ElementsConfig } from '@gram-ai/elements'
 
// Please fill out projectSlug and mcp
const config: ElementsConfig = {
  projectSlug: 'xxx',
  mcp: 'https://app.getgram.ai/mcp/{your_slug}',
  variant: 'widget',
  welcome: {
    title: 'Hello!',
    subtitle: 'How can I help you today?',
  },
  composer: {
    placeholder: 'Ask me anything...',
  },
  modal: {
    defaultOpen: true,
  },
}
 
export const App = () => {
  return (
    <GramElementsProvider config={config}>
      <Chat />
    </GramElementsProvider>
  )
}

Custom Session Endpoint

By default, Elements expects the session endpoint on your backend to be located at /chat/session. If you’ve mounted your session endpoint at a different path, you can provide a custom getSession function in the ElementsProvider:


import { GramElementsProvider, Chat, type ElementsConfig, type GetSessionFn } from '@gram-ai/elements'
 
const config: ElementsConfig = {
  projectSlug: 'xxx',
  mcp: 'https://app.getgram.ai/mcp/{your_slug}',
  // ... other config
}
 
const customGetSession: GetSessionFn = async () => {
  const response = await fetch('/api/custom-session-endpoint', {
    method: 'POST',
  })
  const data = await response.json()
  return data.client_token
}
 
export const App = () => {
  return (
    <GramElementsProvider config={config} getSession={customGetSession}>
      <Chat />
    </GramElementsProvider>
  )
}

Configuration

For complete configuration options and TypeScript type definitions, see the API documentation.

Quick Configuration Example


import { GramElementsProvider, Chat, type ElementsConfig } from '@gram-ai/elements'
 
const config: ElementsConfig = {
  projectSlug: 'your-project',
  mcp: 'https://app.getgram.ai/mcp/your-mcp-slug',
  variant: 'widget', // 'widget' | 'sidecar' | 'standalone'
  welcome: {
    title: 'Hello!',
    subtitle: 'How can I help you today?',
  },
}
 
export const App = () => {
  return (
    <GramElementsProvider config={config}>
      <Chat />
    </GramElementsProvider>
  )
}

Plugins

Plugins extend the Gram Elements library with custom rendering capabilities for specific content types. They allow you to transform markdown code blocks into rich, interactive visualizations and components.

How Plugins Work

When you add a plugin:

The plugin extends the system prompt with instructions for the LLM
The LLM returns code blocks marked with the plugin’s language identifier
The plugin’s custom component renders the code block content

For example, the built-in chart plugin instructs the LLM to return Vega specifications for visualizations, which are then rendered as interactive charts.

Using Recommended Plugins

Gram Elements includes a set of recommended plugins that you can use out of the box:


import { GramElementsProvider, Chat, type ElementsConfig } from '@gram-ai/elements'
import { recommended } from '@gram-ai/elements/plugins'
 
const config: ElementsConfig = {
  projectSlug: 'my-project',
  mcp: 'https://app.getgram.ai/mcp/my-mcp-slug',
  welcome: {
    title: 'Hello!',
    subtitle: 'How can I help you today?',
  },
  // Add all recommended plugins
  plugins: recommended,
}
 
export const App = () => {
  return (
    <GramElementsProvider config={config}>
      <Chat />
    </GramElementsProvider>
  )
}

Available Recommended Plugins

chart - Renders Vega chart specifications as interactive visualizations

Using Individual Plugins

You can also import and use plugins individually:


import { chart } from '@gram-ai/elements/plugins'
 
const config: ElementsConfig = {
  // ... other config
  plugins: [chart],
}

Using Custom Plugins

You can create your own custom plugins to add specialized rendering capabilities:


import { GramElementsProvider, Chat, type ElementsConfig } from '@gram-ai/elements'
import { chart } from '@gram-ai/elements/plugins'
import { myCustomPlugin } from './plugins/myCustomPlugin'
 
const config: ElementsConfig = {
  projectSlug: 'my-project',
  mcp: 'https://app.getgram.ai/mcp/my-mcp-slug',
  welcome: {
    title: 'Hello!',
    subtitle: 'How can I help you today?',
  },
  // Combine built-in and custom plugins
  plugins: [chart, myCustomPlugin],
}
 
export const App = () => {
  return (
    <GramElementsProvider config={config}>
      <Chat />
    </GramElementsProvider>
  )
}

Creating Custom Plugins

To create your own plugins, see the comprehensive Plugin Development Guide. The guide covers:

Plugin architecture and interface
Step-by-step tutorial for creating plugins
Best practices and common patterns
Real-world examples
Troubleshooting tips

Replay

Replay lets you play back a pre-recorded conversation with streaming animations — no auth, MCP, or network calls required. This is useful for marketing demos, documentation, and testing.

Recording a cassette

A cassette is a JSON file that captures a conversation. To record one from a live chat session, enable the built-in recorder by setting this environment variable:


VITE_ELEMENTS_ENABLE_CASSETTE_RECORDING=true

This adds a record button to the composer. Click it to open the recorder popover, then start recording. Chat normally, and when you’re done, click Stop & Download to save the cassette as a .cassette.json file.

You can also record programmatically using the useRecordCassette hook:


import {
  GramElementsProvider,
  Chat,
  useRecordCassette,
} from '@gram-ai/elements'
 
function RecordableChat() {
  const { isRecording, startRecording, download } = useRecordCassette()
  return (
    <>
      <Chat />
      <button onClick={startRecording}>Record</button>
      <button onClick={() => download('demo')}>Save</button>
    </>
  )
}

Playing back a cassette

Use the <Replay> component, which replaces GramElementsProvider entirely:


import { Replay, Chat } from '@gram-ai/elements'
import cassette from './demo.cassette.json'
 
function MarketingDemo() {
  return (
    <Replay cassette={cassette} config={{ variant: 'standalone' }}>
      <Chat />
    </Replay>
  )
}

<Replay> accepts optional timing props to control the playback speed:

Prop	Default	Description
`typingSpeed`	`15`	Milliseconds per character for streaming
`userMessageDelay`	`800`	Milliseconds before each user message appears
`assistantStartDelay`	`400`	Milliseconds before the assistant starts typing
`onComplete`	—	Callback when replay finishes

React Compatibility

@gram-ai/elements supports React 16.8+, React 17, React 18, and React 19.

React 18 and 19 work out of the box. For React 16 or 17, you need to configure your bundler to shim newer React APIs (useSyncExternalStore, useId, useInsertionEffect) that are used by transitive dependencies like zustand and @assistant-ui/react.

React 16 and React 17 are not regularly tested. If you run into any issues using Elements with these versions, please reach out to us for support.

Vite Setup (React 16/17)

Add the compatibility plugin to your Vite config:


import react from '@vitejs/plugin-react'
import { reactCompat } from '@gram-ai/elements/compat'
import { defineConfig } from 'vite'
 
export default defineConfig({
  plugins: [react(), reactCompat()],
})

This automatically shims React 18 APIs (useSyncExternalStore, useId, useInsertionEffect) so that Elements and its dependencies work correctly on older React versions.

Contributing

We welcome pull requests to Elements. Please read the contributing guide.

Last updated on January 13, 2026