Chat Agents

Build AI-powered chat interfaces with AIChatAgent and useAgentChat. Messages are automatically persisted to SQLite, streams resume on disconnect, and tool calls work across server and client.

Overview

@cloudflare/ai-chat provides two main exports:

Export	Import	Purpose
`AIChatAgent`	`@cloudflare/ai-chat`	Server-side agent class with message persistence and streaming
`useAgentChat`	`@cloudflare/ai-chat/react`	React hook for building chat UIs

Built on the AI SDK and Cloudflare Durable Objects, you get:

Automatic message persistence — conversations stored in SQLite, survive restarts
Resumable streaming — disconnected clients resume mid-stream without data loss
Real-time sync — messages broadcast to all connected clients via WebSocket
Tool support — server-side, client-side, and human-in-the-loop tool patterns
Data parts — attach typed JSON (citations, progress, usage) to messages alongside text
Row size protection — automatic compaction when messages approach SQLite limits

Quick Start

Install dependencies

npm install @cloudflare/ai-chat agents ai workers-ai-provider

Create server agent

import { AIChatAgent } from "@cloudflare/ai-chat";
import { createWorkersAI } from "workers-ai-provider";
import { streamText, convertToModelMessages } from "ai";

export class ChatAgent extends AIChatAgent {
  async onChatMessage() {
    const workersai = createWorkersAI({ binding: this.env.AI });

    const result = streamText({
      model: workersai("@cf/zai-org/glm-4.7-flash"),
      messages: await convertToModelMessages(this.messages)
    });

    return result.toUIMessageStreamResponse();
  }
}

Create client UI

import { useAgent } from "agents/react";
import { useAgentChat } from "@cloudflare/ai-chat/react";

function Chat() {
  const agent = useAgent({ agent: "ChatAgent" });
  const { messages, sendMessage, status } = useAgentChat({ agent });

  return (
    <div>
      {messages.map((msg) => (
        <div key={msg.id}>
          <strong>{msg.role}:</strong>
          {msg.parts.map((part, i) =>
            part.type === "text" ? <span key={i}>{part.text}</span> : null
          )}
        </div>
      ))}

      <form
        onSubmit={(e) => {
          e.preventDefault();
          const input = e.currentTarget.elements.namedItem(
            "input"
          ) as HTMLInputElement;
          sendMessage({ text: input.value });
          input.value = "";
        }}
      >
        <input name="input" placeholder="Type a message..." />
        <button type="submit" disabled={status === "streaming"}>
          Send
        </button>
      </form>
    </div>
  );
}

Configure Wrangler

wrangler.jsonc

{
  "ai": { "binding": "AI" },
  "durable_objects": {
    "bindings": [{ "name": "ChatAgent", "class_name": "ChatAgent" }]
  },
  "migrations": [{ "tag": "v1", "new_sqlite_classes": ["ChatAgent"] }]
}

The new_sqlite_classes migration is required — AIChatAgent uses SQLite for message persistence and stream chunk buffering.

How It Works

┌──────────┐               WebSocket                ┌──────────────┐
│  Client  │ ◀──────────────────────────────────▶   │ AIChatAgent  │
│          │                                        │              │
│ useAgent │   CF_AGENT_USE_CHAT_REQUEST ──────▶    │ onChatMessage│
│    Chat  │                                        │              │
│          │   ◀────── CF_AGENT_USE_CHAT_RESPONSE   │  streamText  │
│          │          (UIMessageChunk stream)       │              │
│          │                                        │   SQLite     │
│          │   ◀────── CF_AGENT_CHAT_MESSAGES       │  (messages,  │
│          │          (broadcast to all clients)    │   chunks)    │
└──────────┘                                        └──────────────┘

Client sends message

The client sends a message via WebSocket

Agent persists and calls handler

AIChatAgent persists messages to SQLite and calls your onChatMessage method

Stream response

Your method returns a streaming Response (typically from streamText)

Real-time chunks

Chunks stream back over WebSocket in real-time

Broadcast final message

When the stream completes, the final message is persisted and broadcast to all connections

Server API

AIChatAgent

Extends Agent from the agents package. Manages conversation state, persistence, and streaming.

import { AIChatAgent } from "@cloudflare/ai-chat";

export class ChatAgent extends AIChatAgent {
  // Access current messages
  // this.messages: UIMessage[]

  // Limit stored messages (optional)
  maxPersistedMessages = 200;

  async onChatMessage(onFinish?, options?) {
    // onFinish: optional callback for streamText (cleanup is automatic)
    // options.abortSignal: cancel signal
    // options.body: custom data from client
    // Return a Response (streaming or plain text)
  }
}

onChatMessage

This is the main method you override. It receives the conversation context and should return a Response.

async onChatMessage() {
  const workersai = createWorkersAI({ binding: this.env.AI });

  const result = streamText({
    model: workersai("@cf/zai-org/glm-4.7-flash"),
    system: "You are a helpful assistant.",
    messages: await convertToModelMessages(this.messages)
  });

  return result.toUIMessageStreamResponse();
}

this.messages

The current conversation history, loaded from SQLite. This is an array of UIMessage objects from the AI SDK. Messages are automatically persisted after each interaction.

maxPersistedMessages

Cap the number of messages stored in SQLite. When the limit is exceeded, the oldest messages are deleted. This controls storage only — it does not affect what is sent to the LLM.

export class ChatAgent extends AIChatAgent {
  maxPersistedMessages = 200;
}

To control what is sent to the model, use the AI SDK’s pruneMessages():

import { streamText, convertToModelMessages, pruneMessages } from "ai";

async onChatMessage() {
  const workersai = createWorkersAI({ binding: this.env.AI });

  const result = streamText({
    model: workersai("@cf/zai-org/glm-4.7-flash"),
    messages: pruneMessages({
      messages: await convertToModelMessages(this.messages),
      reasoning: "before-last-message",
      toolCalls: "before-last-2-messages"
    })
  });

  return result.toUIMessageStreamResponse();
}

waitForMcpConnections

Controls whether AIChatAgent waits for MCP server connections to settle before calling onChatMessage. This ensures this.mcp.getAITools() returns the full set of tools, especially after Durable Object hibernation when connections are being restored in the background.

Value	Behavior
`{ timeout: 10_000 }`	Wait up to 10 seconds (default)
`{ timeout: N }`	Wait up to `N` milliseconds
`true`	Wait indefinitely until all connections ready
`false`	Do not wait (old behavior before 0.2.0)

export class ChatAgent extends AIChatAgent {
  // Default — waits up to 10 seconds
  // waitForMcpConnections = { timeout: 10_000 };

  // Wait forever
  waitForMcpConnections = true;

  // Disable waiting
  waitForMcpConnections = false;
}

Request Cancellation

When a user clicks “stop” in the chat UI, the client sends a CF_AGENT_CHAT_REQUEST_CANCEL message. The server propagates this to the abortSignal in options:

async onChatMessage(_onFinish, options) {
  const result = streamText({
    model: workersai("@cf/zai-org/glm-4.7-flash"),
    messages: await convertToModelMessages(this.messages),
    abortSignal: options?.abortSignal // Pass through for cancellation
  });

  return result.toUIMessageStreamResponse();
}

If you do not pass abortSignal to streamText, the LLM call will continue running in the background even after the user cancels. Always forward it when possible.

Client API

useAgentChat

React hook that connects to an AIChatAgent over WebSocket. Wraps the AI SDK’s useChat with a native WebSocket transport.

import { useAgent } from "agents/react";
import { useAgentChat } from "@cloudflare/ai-chat/react";

function Chat() {
  const agent = useAgent({ agent: "ChatAgent" });
  const {
    messages,
    sendMessage,
    clearHistory,
    addToolOutput,
    addToolApprovalResponse,
    setMessages,
    status
  } = useAgentChat({ agent });

  // ...
}

Options

Option	Type	Default	Description
`agent`	`ReturnType<typeof useAgent>`	Required	Agent connection from `useAgent`
`onToolCall`	`({ toolCall, addToolOutput }) => void`	—	Handle client-side tool execution
`autoContinueAfterToolResult`	`boolean`	`true`	Auto-continue conversation after client tool results and approvals
`resume`	`boolean`	`true`	Enable automatic stream resumption on reconnect
`body`	`object \| () => object`	—	Custom data sent with every request
`prepareSendMessagesRequest`	`(options) => { body?, headers? }`	—	Advanced per-request customization
`tools`	`Record<string, AITool>`	—	Dynamic client-defined tools for SDK/platform use cases. Schemas are sent to the server automatically
`getInitialMessages`	`(options) => Promise<UIMessage[]>` or `null`	—	Custom initial message loader. Set to `null` to skip the HTTP fetch entirely (useful when providing `messages` directly)

Return Values

Property	Type	Description
`messages`	`UIMessage[]`	Current conversation messages
`sendMessage`	`(message) => void`	Send a message
`clearHistory`	`() => void`	Clear conversation (client and server)
`addToolOutput`	`({ toolCallId, output }) => void`	Provide output for a client-side tool
`addToolApprovalResponse`	`({ id, approved }) => void`	Approve or reject a tool requiring approval
`setMessages`	`(messages \| updater) => void`	Set messages directly (syncs to server)
`status`	`string`	`"idle"`, `"submitted"`, `"streaming"`, or `"error"`

Tools

AIChatAgent supports three tool patterns, all using the AI SDK’s tool() function:

Pattern	Where it runs	When to use
Server-side	Server (automatic)	API calls, database queries, computations
Client-side	Browser (via `onToolCall`)	Geolocation, clipboard, camera, local storage
Approval	Server (after user approval)	Payments, deletions, external actions

Server-Side Tools

Tools with an execute function run automatically on the server:

import { streamText, convertToModelMessages, tool, stepCountIs } from "ai";
import { z } from "zod";

async onChatMessage() {
  const workersai = createWorkersAI({ binding: this.env.AI });

  const result = streamText({
    model: workersai("@cf/zai-org/glm-4.7-flash"),
    messages: await convertToModelMessages(this.messages),
    tools: {
      getWeather: tool({
        description: "Get weather for a city",
        inputSchema: z.object({ city: z.string() }),
        execute: async ({ city }) => {
          const data = await fetchWeather(city);
          return { temperature: data.temp, condition: data.condition };
        }
      })
    },
    stopWhen: stepCountIs(5)
  });

  return result.toUIMessageStreamResponse();
}

Client-Side Tools

Define a tool on the server without execute, then handle it on the client with onToolCall. Use this for tools that need browser APIs:

tools: {
  getLocation: tool({
    description: "Get the user's location from the browser",
    inputSchema: z.object({})
    // No execute — the client handles it
  });
}

When the LLM invokes getLocation, the stream pauses. The onToolCall callback fires, your code provides the output, and the conversation continues.

Dynamic Client Tools

For SDKs and platforms where tools are defined dynamically by the embedding application at runtime, use the tools option on useAgentChat and createToolsFromClientSchemas() on the server:

import { createToolsFromClientSchemas } from "@cloudflare/ai-chat";

async onChatMessage(_onFinish, options) {
  const result = streamText({
    model: workersai("@cf/zai-org/glm-4.7-flash"),
    messages: await convertToModelMessages(this.messages),
    tools: createToolsFromClientSchemas(options?.clientTools)
  });
  return result.toUIMessageStreamResponse();
}

For most apps, server-side tools with tool() and onToolCall are simpler and provide full Zod type safety. Use dynamic client tools when the server does not know the tool surface at deploy time.

Tool Approval (Human-in-the-Loop)

Use needsApproval for tools that require user confirmation before executing:

tools: {
  processPayment: tool({
    description: "Process a payment",
    inputSchema: z.object({
      amount: z.number(),
      recipient: z.string()
    }),
    needsApproval: async ({ amount }) => amount > 100,
    execute: async ({ amount, recipient }) => charge(amount, recipient)
  });
}

Data Parts

Data parts let you attach typed JSON to messages alongside text — progress indicators, source citations, token usage, or any structured data your UI needs.

Writing Data Parts (Server)

Use createUIMessageStream with writer.write() to send data parts from the server:

import {
  streamText,
  convertToModelMessages,
  createUIMessageStream,
  createUIMessageStreamResponse
} from "ai";

export class ChatAgent extends AIChatAgent {
  async onChatMessage() {
    const workersai = createWorkersAI({ binding: this.env.AI });

    const stream = createUIMessageStream({
      execute: async ({ writer }) => {
        const result = streamText({
          model: workersai("@cf/zai-org/glm-4.7-flash"),
          messages: await convertToModelMessages(this.messages)
        });

        // Merge the LLM stream
        writer.merge(result.toUIMessageStream());

        // Write a data part — persisted to message.parts
        writer.write({
          type: "data-sources",
          id: "src-1",
          data: { query: "agents", status: "searching", results: [] }
        });

        // Later: update the same part in-place (same type + id)
        writer.write({
          type: "data-sources",
          id: "src-1",
          data: {
            query: "agents",
            status: "found",
            results: ["Agents SDK docs", "Durable Objects guide"]
          }
        });
      }
    });

    return createUIMessageStreamResponse({ stream });
  }
}

Three Patterns

Pattern	How	Persisted?	Use case
Reconciliation	Same `type` + `id` → updates in-place	Yes	Progressive state (searching → found)
Append	No `id`, or different `id` → appends	Yes	Log entries, multiple citations
Transient	`transient: true` → not added to `message.parts`	No	Ephemeral status (thinking indicator)

Reading Data Parts (Client)

Non-transient data parts appear in message.parts. Use the UIMessage generic to type them:

import { useAgentChat } from "@cloudflare/ai-chat/react";
import type { UIMessage } from "ai";

type ChatMessage = UIMessage<
  unknown,
  {
    sources: { query: string; status: string; results: string[] };
    usage: { model: string; inputTokens: number; outputTokens: number };
  }
>;

const { messages } = useAgentChat<unknown, ChatMessage>({ agent });

// Typed access — no casts needed
for (const msg of messages) {
  for (const part of msg.parts) {
    if (part.type === "data-sources") {
      console.log(part.data.results); // string[]
    }
  }
}

Resumable Streaming

Streams automatically resume when a client disconnects and reconnects. No configuration is needed — it works out of the box. When streaming is active:

All chunks are buffered in SQLite as they are generated
If the client disconnects, the server continues streaming and buffering
When the client reconnects, it receives all buffered chunks and resumes live streaming

Disable with resume: false:

const { messages } = useAgentChat({ agent, resume: false });

For more details, see Resumable Streaming.

Storage Management

Row Size Protection

SQLite rows have a maximum size of 2 MB. When a message approaches this limit (for example, a tool returning a very large output), AIChatAgent automatically compacts the message:

Tool output compaction — Large tool outputs are replaced with an LLM-friendly summary that instructs the model to suggest re-running the tool
Text truncation — If the message is still too large after tool compaction, text parts are truncated with a note

Compacted messages include metadata.compactedToolOutputs so clients can detect and display this gracefully.

Controlling LLM Context vs Storage

Storage (maxPersistedMessages) and LLM context are independent:

Concern	Control	Scope
How many messages SQLite stores	`maxPersistedMessages`	Persistence
What the model sees	`pruneMessages()`	LLM context
Row size limits	Automatic compaction	Per-message

export class ChatAgent extends AIChatAgent {
  maxPersistedMessages = 200; // Storage limit

  async onChatMessage() {
    const result = streamText({
      model: workersai("@cf/zai-org/glm-4.7-flash"),
      messages: pruneMessages({
        // LLM context limit
        messages: await convertToModelMessages(this.messages),
        reasoning: "before-last-message",
        toolCalls: "before-last-2-messages"
      })
    });

    return result.toUIMessageStreamResponse();
  }
}

Using Different AI Providers

AIChatAgent works with any AI SDK-compatible provider. The server code determines which model to use — the client does not need to change.

import { createWorkersAI } from "workers-ai-provider";

const workersai = createWorkersAI({ binding: this.env.AI });
const result = streamText({
  model: workersai("@cf/zai-org/glm-4.7-flash"),
  messages: await convertToModelMessages(this.messages)
});

Multi-Client Sync

When multiple clients connect to the same agent instance, messages are automatically broadcast to all connections. If one client sends a message, all other connected clients receive the updated message list.

Client A ──── sendMessage("Hello") ────▶ AIChatAgent
                                              │
                                        persist + stream
                                              │
Client A ◀── CF_AGENT_USE_CHAT_RESPONSE ──────┤
Client B ◀── CF_AGENT_CHAT_MESSAGES ──────────┘

The originating client receives the streaming response. All other clients receive the final messages via a CF_AGENT_CHAT_MESSAGES broadcast.

Resumable Streaming — How stream resumption works
Client Tools Continuation — Advanced client-side tool patterns
Codemode — Let LLMs write code to orchestrate tools

Get Started

Core Concepts

Client SDK

AI Integration

MCP (Model Context Protocol)

Background Processing

Communication Channels

Advanced

Migration Guides

Overview

Quick Start

How It Works

Server API

AIChatAgent

onChatMessage

this.messages

maxPersistedMessages

waitForMcpConnections

Request Cancellation

Client API

useAgentChat

Options

Return Values

Tools

Server-Side Tools

Client-Side Tools

Dynamic Client Tools

Tool Approval (Human-in-the-Loop)

Data Parts

Writing Data Parts (Server)

Three Patterns

Reading Data Parts (Client)

Resumable Streaming

Storage Management

Row Size Protection

Controlling LLM Context vs Storage

Using Different AI Providers

Multi-Client Sync

Build docs developers (and LLMs) love

Get Started

Core Concepts

Client SDK

AI Integration

MCP (Model Context Protocol)

Background Processing

Communication Channels

Advanced

Migration Guides

Documentation Index

​Overview

​Quick Start

​How It Works

​Server API

​AIChatAgent

​onChatMessage

​this.messages

​maxPersistedMessages

​waitForMcpConnections

​Request Cancellation

​Client API

​useAgentChat

​Options

​Return Values

​Tools

​Server-Side Tools

​Client-Side Tools

​Dynamic Client Tools

​Tool Approval (Human-in-the-Loop)

​Data Parts

​Writing Data Parts (Server)

​Three Patterns

​Reading Data Parts (Client)

​Resumable Streaming

​Storage Management

​Row Size Protection

​Controlling LLM Context vs Storage

​Using Different AI Providers

​Multi-Client Sync

​Related Documentation

Build docs developers (and LLMs) love

Overview

Quick Start

How It Works

Server API

AIChatAgent

onChatMessage

this.messages

maxPersistedMessages

waitForMcpConnections

Request Cancellation

Client API

useAgentChat

Options

Return Values

Tools

Server-Side Tools

Client-Side Tools

Dynamic Client Tools

Tool Approval (Human-in-the-Loop)

Data Parts

Writing Data Parts (Server)

Three Patterns

Reading Data Parts (Client)

Resumable Streaming

Storage Management

Row Size Protection

Controlling LLM Context vs Storage

Using Different AI Providers

Multi-Client Sync

Related Documentation