Get started with the Anthropic Claude Messages API

The Anthropic Claude Messages API allows you to build an AI agent with Anthropic's models.

Code demo available

This guide includes a code demo to help you get started. See the GitHub repository.

Client-side setup

First, install the AI Agent extension.

npm install @tiptap-pro/extension-ai-agent

Then, import the extension and configure it with the AiAgentProvider class.

import { Editor } from '@tiptap/core'
import StarterKit from '@tiptap/starter-kit'
import AiAgent, { AiAgentProvider } from '@tiptap-pro/extension-ai-agent'

const provider = new AiAgentProvider()

const editor = new Editor({
  extensions: [
    StarterKit,
    AiAgent.configure({
      provider,
    }),
  ],
})

Inside AiAgentProvider, define a resolver function that calls your backend.

import AiAgent, { AiAgentProvider } from '@tiptap-pro/extension-ai-agent'

const provider = new AiAgentProvider({
  // The `chatMessages` property contains the chat messages of the conversation
  resolver: async ({ chatMessages, schemaAwarenessData }) => {
    // Call the API endpoint of your backend
    const response = await fetch('/api-endpoint', {
      method: 'POST',
      body: JSON.stringify({ chatMessages, schemaAwarenessData }),
    })
    return await response.json()
  },
})

In the next section, we'll see how to implement the API endpoint that returns the response in the correct format.

Server-side setup

First, install the AI Agent extension and Anthropic server libraries.

npm install @tiptap-pro/extension-ai-agent-server @anthropic-ai/sdk

Get the chat messages and schema awareness data from the request parameters.

// Code inside your API endpoint. Code depends on your backend framework
const { chatMessages, schemaAwarenessData } = request

Then, inside your API endpoint, create an AiAgentToolkit instance. It lets you configure the tools that will be available to the AI model.

import { AiAgentToolkit, anthropicMessagesAdapter } from '@tiptap-pro/extension-ai-agent-server'

const toolkit = new AiAgentToolkit({
  adapter: anthropicMessagesAdapter,
  schemaAwarenessData,
})

Also define a ChatMessagesFormatter instance. It lets you convert the chat messages to the format expected by the Anthropic Claude Messages API.

import {
  AiAgentToolkit,
  anthropicMessagesAdapter,
  ChatMessagesFormatter,
} from '@tiptap-pro/extension-ai-agent-server'

const formatter = new ChatMessagesFormatter({
  initialMessages: chatMessages,
  adapter: anthropicMessagesAdapter,
})

After creating the toolkit and the formatter, send the request to the Anthropic Claude Messages API.

import {
  AiAgentToolkit,
  anthropicMessagesAdapter,
  ChatMessagesFormatter,
} from '@tiptap-pro/extension-ai-agent-server'
import Anthropic from '@anthropic-ai/sdk'

const { chatMessages, schemaAwarenessData } = request

const toolkit = new AiAgentToolkit({
  adapter: anthropicMessagesAdapter,
  schemaAwarenessData,
})

const formatter = new ChatMessagesFormatter({
  // Get the chat messages from the request body
  initialMessages: chatMessages,
  adapter: anthropicMessagesAdapter,
})

// Initialize the Anthropic client
const anthropic = new Anthropic()

// Call the Anthropic Claude Messages API
const response = await anthropic.messages.create({
  model: 'claude-sonnet-4-0',
  max_tokens: 2048,
  system: `
<Your system prompt>
${toolkit.getSystemPrompt()}
`,
  messages: formatter.format(),
  // Provide the tools that the AI model can call
  tools: toolkit.format(),
})

At the end of the system prompt, include the system prompt generated by the AiAgentToolkit instance, like this: toolkit.getSystemPrompt(). This contains instructions on how to use the tools.

To write the system prompt, see the system prompt guide. It includes an example system prompt that you can use as a starting point.

Finally, use the formatter to convert the response to the format expected by the AI Agent extension.

formatter.addAiResponse(response)

const response = formatter.getResolverResponse()

The value returned from formatter.getResolverResponse() should be the response of your API endpoint and the return value of the resolver function.

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