chore (ai): rename CoreMessage to ModelMessage

.changeset/young-dingos-march.md

@@ -0,0 +1,5 @@
+---
+'ai': major
+---
+
+chore (ai): rename CoreMessage to ModelMessage

content/cookbook/01-next/11-generate-text-with-chat-prompt.mdx

@@ -31,12 +31,12 @@ Let's start by creating a simple chat interface with an input field that sends t
 ```tsx filename='app/page.tsx'
 'use client';
 
-import { CoreMessage } from 'ai';
+import { ModelMessage } from 'ai';
 import { useState } from 'react';
 
 export default function Page() {
   const [input, setInput] = useState('');
-  const [messages, setMessages] = useState<CoreMessage[]>([]);
+  const [messages, setMessages] = useState<ModelMessage[]>([]);
 
   return (
     <div>
@@ -90,11 +90,11 @@ export default function Page() {
 Next, let's create the `/api/chat` endpoint that generates the assistant's response based on the conversation history.
 
 ```typescript filename='app/api/chat/route.ts'
-import { CoreMessage, generateText } from 'ai';
+import { ModelMessage, generateText } from 'ai';
 import { openai } from '@ai-sdk/openai';
 
 export async function POST(req: Request) {
-  const { messages }: { messages: CoreMessage[] } = await req.json();
+  const { messages }: { messages: ModelMessage[] } = await req.json();
 
   const { response } = await generateText({
     model: openai('gpt-4'),

content/cookbook/01-next/24-stream-text-multistep.mdx

@@ -55,7 +55,7 @@ export async function POST(req: Request) {
           'You are a helpful assistant with a different system prompt. Repeat the extract user goal in your answer.',
         // continue the workflow stream with the messages from the previous step:
         messages: [
-          ...convertToCoreMessages(messages),
+          ...convertToModelMessages(messages),
           ...(await result1.response).messages,
         ],
       });

content/cookbook/01-next/75-human-in-the-loop.mdx

@@ -336,7 +336,7 @@ The solution above is low-level and not very friendly to use in a production env
 import {
   formatDataStreamPart,
   Message,
-  convertToCoreMessages,
+  convertToModelMessages,
   DataStreamWriter,
   ToolExecutionOptions,
   ToolSet,
@@ -419,7 +419,7 @@ export async function processToolCalls<
         const toolInstance = executeFunctions[toolName];
         if (toolInstance) {
           result = await toolInstance(toolInvocation.args, {
-            messages: convertToCoreMessages(messages),
+            messages: convertToModelMessages(messages),
             toolCallId: toolInvocation.toolCallId,
           });
         } else {

content/docs/02-foundations/03-prompts.mdx

@@ -164,9 +164,9 @@ const messages = [
   AI SDK UI hooks like [`useChat`](/docs/reference/ai-sdk-ui/use-chat) return
   arrays of `UIMessage` objects, which do not support provider options. We
   recommend using the
-  [`convertToCoreMessages`](/docs/reference/ai-sdk-ui/convert-to-core-messages)
+  [`convertToModelMessages`](/docs/reference/ai-sdk-ui/convert-to-core-messages)
   function to convert `UIMessage` objects to
-  [`CoreMessage`](/docs/reference/ai-sdk-core/core-message) objects before
+  [`ModelMessage`](/docs/reference/ai-sdk-core/core-message) objects before
   applying or appending message(s) or message parts with `providerOptions`.
 </Note>
 

content/docs/02-getting-started/06-nodejs.mdx

@@ -76,7 +76,7 @@ Create an `index.ts` file in the root of your project and add the following code
 
 ```ts filename="index.ts"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, streamText } from 'ai';
+import { ModelMessage, streamText } from 'ai';
 import dotenv from 'dotenv';
 import * as readline from 'node:readline/promises';
 
@@ -87,7 +87,7 @@ const terminal = readline.createInterface({
   output: process.stdout,
 });
 
-const messages: CoreMessage[] = [];
+const messages: ModelMessage[] = [];
 
 async function main() {
   while (true) {
@@ -151,7 +151,7 @@ Modify your `index.ts` file to include the new weather tool:
 
 ```ts filename="index.ts" highlight="2,4,25-38"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, streamText, tool } from 'ai';
+import { ModelMessage, streamText, tool } from 'ai';
 import dotenv from 'dotenv';
 import { z } from 'zod';
 import * as readline from 'node:readline/promises';
@@ -163,7 +163,7 @@ const terminal = readline.createInterface({
   output: process.stdout,
 });
 
-const messages: CoreMessage[] = [];
+const messages: ModelMessage[] = [];
 
 async function main() {
   while (true) {
@@ -221,7 +221,7 @@ Notice the blank "assistant" response? This is because instead of generating a t
 
 ```typescript highlight="47-48"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, streamText, tool } from 'ai';
+import { ModelMessage, streamText, tool } from 'ai';
 import dotenv from 'dotenv';
 import { z } from 'zod';
 import * as readline from 'node:readline/promises';
@@ -233,7 +233,7 @@ const terminal = readline.createInterface({
   output: process.stdout,
 });
 
-const messages: CoreMessage[] = [];
+const messages: ModelMessage[] = [];
 
 async function main() {
   while (true) {
@@ -291,7 +291,7 @@ Modify your `index.ts` file to include the `maxSteps` option:
 
 ```ts filename="index.ts" highlight="39-42"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, streamText, tool } from 'ai';
+import { ModelMessage, streamText, tool } from 'ai';
 import dotenv from 'dotenv';
 import { z } from 'zod';
 import * as readline from 'node:readline/promises';
@@ -303,7 +303,7 @@ const terminal = readline.createInterface({
   output: process.stdout,
 });
 
-const messages: CoreMessage[] = [];
+const messages: ModelMessage[] = [];
 
 async function main() {
   while (true) {
@@ -364,7 +364,7 @@ Update your `index.ts` file to add a new tool to convert the temperature from Ce
 
 ```ts filename="index.ts" highlight="38-49"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, streamText, tool } from 'ai';
+import { ModelMessage, streamText, tool } from 'ai';
 import dotenv from 'dotenv';
 import { z } from 'zod';
 import * as readline from 'node:readline/promises';
@@ -376,7 +376,7 @@ const terminal = readline.createInterface({
   output: process.stdout,
 });
 
-const messages: CoreMessage[] = [];
+const messages: ModelMessage[] = [];
 
 async function main() {
   while (true) {

content/docs/02-guides/03-slackbot.mdx

@@ -318,10 +318,10 @@ Here's how to implement it:
 
 ```typescript filename="lib/generate-response.ts"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, generateText } from 'ai';
+import { ModelMessage, generateText } from 'ai';
 
 export const generateResponse = async (
-  messages: CoreMessage[],
+  messages: ModelMessage[],
   updateStatus?: (status: string) => void,
 ) => {
   const { text } = await generateText({
@@ -349,12 +349,12 @@ The real power of the AI SDK comes from tools that enable your bot to perform ac
 
 ```typescript filename="lib/generate-response.ts"
 import { openai } from '@ai-sdk/openai';
-import { CoreMessage, generateText, tool } from 'ai';
+import { ModelMessage, generateText, tool } from 'ai';
 import { z } from 'zod';
 import { exa } from './utils';
 
 export const generateResponse = async (
-  messages: CoreMessage[],
+  messages: ModelMessage[],
   updateStatus?: (status: string) => void,
 ) => {
   const { text } = await generateText({

content/docs/03-ai-sdk-core/15-tools-and-tool-calling.mdx

@@ -191,12 +191,12 @@ Both `generateText` and `streamText` have a `response.messages` property that yo
 add the assistant and tool messages to your conversation history.
 It is also available in the `onFinish` callback of `streamText`.
 
-The `response.messages` property contains an array of `CoreMessage` objects that you can add to your conversation history:
+The `response.messages` property contains an array of `ModelMessage` objects that you can add to your conversation history:
 
 ```ts
 import { generateText } from 'ai';
 
-const messages: CoreMessage[] = [
+const messages: ModelMessage[] = [
   // ...
 ];
 

content/docs/04-ai-sdk-ui/03-chatbot-message-persistence.mdx

@@ -124,14 +124,14 @@ We have enabled the `sendExtraMessageFields` option to send the id and createdAt
 meaning that we store messages in the `useChat` message format.
 
 <Note>
-  The `useChat` message format is different from the `CoreMessage` format. The
+  The `useChat` message format is different from the `ModelMessage` format. The
   `useChat` message format is designed for frontend display, and contains
   additional fields such as `id` and `createdAt`. We recommend storing the
   messages in the `useChat` message format.
 </Note>
 
 Storing messages is done in the `onFinish` callback of the `streamText` function.
-`onFinish` receives the messages from the AI response as a `CoreMessage[]`,
+`onFinish` receives the messages from the AI response as a `ModelMessage[]`,
 and we use the [`appendResponseMessages`](/docs/reference/ai-sdk-ui/append-response-messages)
 helper to append the AI response messages to the chat messages.
 

content/docs/05-ai-sdk-rsc/10-migrating-to-ui.mdx

@@ -483,12 +483,12 @@ With AI SDK UI, you will save chats using the `onFinish` callback function of `s
 ```ts filename="@/app/api/chat/route.ts"
 import { openai } from '@ai-sdk/openai';
 import { saveChat } from '@/utils/queries';
-import { streamText, convertToCoreMessages } from 'ai';
+import { streamText, convertToModelMessages } from 'ai';
 
 export async function POST(request) {
   const { id, messages } = await request.json();
 
-  const coreMessages = convertToCoreMessages(messages);
+  const coreMessages = convertToModelMessages(messages);
 
   const result = streamText({
     model: openai('gpt-4o'),

content/docs/07-reference/01-ai-sdk-core/01-generate-text.mdx

@@ -333,7 +333,7 @@ To see `generateText` in action, check out [these examples](#examples).
                     },
                     {
                       name: 'messages',
-                      type: 'CoreMessage[]',
+                      type: 'ModelMessage[]',
                       description:
                         'Messages that were sent to the language model to initiate the response that contained the tool call. The messages do not include the system prompt nor the assistant response that contained the tool call.',
                     },
@@ -533,7 +533,7 @@ To see `generateText` in action, check out [these examples](#examples).
             },
             {
               name: 'messages',
-              type: 'CoreMessage[]',
+              type: 'ModelMessage[]',
               description: 'The messages in the current generation step.',
             },
             {

content/docs/07-reference/01-ai-sdk-core/02-stream-text.mdx

@@ -334,7 +334,7 @@ To see `streamText` in action, check out [these examples](#examples).
                     },
                     {
                       name: 'messages',
-                      type: 'CoreMessage[]',
+                      type: 'ModelMessage[]',
                       description:
                         'Messages that were sent to the language model to initiate the response that contained the tool call. The messages do not include the system prompt nor the assistant response that contained the tool call.',
                     },
@@ -577,7 +577,7 @@ To see `streamText` in action, check out [these examples](#examples).
             },
             {
               name: 'messages',
-              type: 'CoreMessage[]',
+              type: 'ModelMessage[]',
               description: 'The messages in the current generation step.',
             },
             {

content/docs/07-reference/01-ai-sdk-core/20-tool.mdx

@@ -77,7 +77,7 @@ export const weatherTool = tool({
                     },
                     {
                         name: "messages",
-                        type: "CoreMessage[]",
+                        type: "ModelMessage[]",
                         description: "Messages that were sent to the language model to initiate the response that contained the tool call. The messages do not include the system prompt nor the assistant response that contained the tool call."
                       },
                       {

content/docs/07-reference/01-ai-sdk-core/30-core-message.mdx

@@ -1,16 +1,16 @@
 ---
-title: CoreMessage
+title: ModelMessage
 description: Message types for AI SDK Core (API Reference)
 ---
 
-# `CoreMessage`
+# `ModelMessage`
 
-`CoreMessage` represents the fundamental message structure used with AI SDK Core functions.
+`ModelMessage` represents the fundamental message structure used with AI SDK Core functions.
 It encompasses various message types that can be used in the `messages` field of any AI SDK Core functions.
 
-You can access the Zod schema for `CoreMessage` with the `coreMessageSchema` export.
+You can access the Zod schema for `ModelMessage` with the `coreMessageSchema` export.
 
-## `CoreMessage` Types
+## `ModelMessage` Types
 
 ### `CoreSystemMessage`
 
@@ -75,7 +75,7 @@ type ToolContent = Array<ToolResultPart>;
 
 You can access the Zod schema for `CoreToolMessage` with the `coreToolMessageSchema` export.
 
-## `CoreMessage` Parts
+## `ModelMessage` Parts
 
 ### `TextPart`
 

content/docs/07-reference/02-ai-sdk-ui/31-convert-to-core-messages.mdx → content/docs/07-reference/02-ai-sdk-ui/31-convert-to-model-messages.mdx

@@ -1,27 +1,27 @@
 ---
-title: convertToCoreMessages
-description: Convert useChat messages to CoreMessages for AI core functions (API Reference)
+title: convertToModelMessages
+description: Convert useChat messages to ModelMessages for AI functions (API Reference)
 ---
 
-# `convertToCoreMessages()`
+# `convertToModelMessages()`
 
 <Note title="warning">
-  The `convertToCoreMessages` function is no longer required. The AI SDK now
-  automatically converts the incoming messages to the `CoreMessage` format.
+  The `convertToModelMessages` function is no longer required. The AI SDK now
+  automatically converts the incoming messages to the `ModelMessage` format.
 </Note>
 
-The `convertToCoreMessages` function is used to transform an array of UI messages from the `useChat` hook into an array of `CoreMessage` objects. These `CoreMessage` objects are compatible with AI core functions like `streamText`.
+The `convertToModelMessages` function is used to transform an array of UI messages from the `useChat` hook into an array of `ModelMessage` objects. These `ModelMessage` objects are compatible with AI core functions like `streamText`.
 
 ```ts filename="app/api/chat/route.ts"
 import { openai } from '@ai-sdk/openai';
-import { convertToCoreMessages, streamText } from 'ai';
+import { convertToModelMessages, streamText } from 'ai';
 
 export async function POST(req: Request) {
   const { messages } = await req.json();
 
   const result = streamText({
     model: openai('gpt-4o'),
-    messages: convertToCoreMessages(messages),
+    messages: convertToModelMessages(messages),
   });
 
   return result.toDataStreamResponse();
@@ -30,7 +30,7 @@ export async function POST(req: Request) {
 
 ## Import
 
-<Snippet text={`import { convertToCoreMessages } from "ai"`} prompt={false} />
+<Snippet text={`import { convertToModelMessages } from "ai"`} prompt={false} />
 
 ## API Signature
 
@@ -55,21 +55,21 @@ export async function POST(req: Request) {
 
 ### Returns
 
-An array of [`CoreMessage`](/docs/reference/ai-sdk-core/core-message) objects.
+An array of [`ModelMessage`](/docs/reference/ai-sdk-core/core-message) objects.
 
 <PropertiesTable
   content={[
     {
-      name: 'CoreMessage[]',
+      name: 'ModelMessage[]',
       type: 'Array',
-      description: 'An array of CoreMessage objects',
+      description: 'An array of ModelMessage objects',
     },
   ]}
 />
 
 ## Multi-modal Tool Responses
 
-The `convertToCoreMessages` function supports tools that can return multi-modal content. This is useful when tools need to return non-text content like images.
+The `convertToModelMessages` function supports tools that can return multi-modal content. This is useful when tools need to return non-text content like images.
 
 ```ts
 import { tool } from 'ai';
@@ -83,7 +83,7 @@ const screenshotTool = tool({
 
 const result = streamText({
   model: openai('gpt-4'),
-  messages: convertToCoreMessages(messages, {
+  messages: convertToModelMessages(messages, {
     tools: {
       screenshot: screenshotTool,
     },