---
title: Chat agent
description: Build a streaming AI chat agent with tools using Workers AI — no API keys required.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

> Documentation Index  
> Fetch the complete documentation index at: https://edgetunnel-b2h.pages.dev/agents/llms.txt  
> Use this file to discover all available pages before exploring further. 

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# Chat agent

Build a chat agent that streams AI responses, calls server-side tools, executes client-side tools in the browser, and asks for user approval before sensitive actions.

**What you will build:** A chat agent powered by Workers AI with three tool types — automatic, client-side, and approval-gated.

**Time:** \~15 minutes

This tutorial starts from a minimal Hello World Worker so you can see each moving part. If you want a complete starter app with the same core pieces already wired together, start with the [quick start](https://edgetunnel-b2h.pages.dev/agents/getting-started/quick-start/) and then return here to understand how the chat pieces fit together.

**Prerequisites:**

* Node.js 18+
* A Cloudflare account (free tier works)

## 1\. Create the project

```sh
npm create cloudflare@latest chat-agent
```

Select **"Hello World" Worker** when prompted. Then install the dependencies:

```sh
cd chat-agent
npm install agents @cloudflare/ai-chat ai workers-ai-provider zod
```

## 2\. Configure Wrangler

Replace your `wrangler.jsonc` with:

* [  wrangler.jsonc ](#tab-panel-5925)
* [  wrangler.toml ](#tab-panel-5926)

**JSONC**

```jsonc
{
  "name": "chat-agent",
  "main": "src/server.ts",
  // Set this to today's date
  "compatibility_date": "2026-07-20",
  "compatibility_flags": ["nodejs_compat"],
  "ai": { "binding": "AI" },
  "durable_objects": {
    "bindings": [{ "name": "ChatAgent", "class_name": "ChatAgent" }],
  },
  "migrations": [{ "tag": "v1", "new_sqlite_classes": ["ChatAgent"] }],
}
```

**TOML**

```toml
name = "chat-agent"
main = "src/server.ts"
# Set this to today's date
compatibility_date = "2026-07-20"
compatibility_flags = [ "nodejs_compat" ]


[ai]
binding = "AI"


[[durable_objects.bindings]]
name = "ChatAgent"
class_name = "ChatAgent"


[[migrations]]
tag = "v1"
new_sqlite_classes = [ "ChatAgent" ]
```

Key settings:

* `ai` binds Workers AI — no API key needed
* `durable_objects` registers your chat agent class
* `new_sqlite_classes` enables SQLite storage for message persistence

## 3\. Write the server

Create `src/server.ts`. This is where your agent lives:

* [  JavaScript ](#tab-panel-5927)
* [  TypeScript ](#tab-panel-5928)

**JavaScript**

```js
import { AIChatAgent } from "@cloudflare/ai-chat";
import { routeAgentRequest } from "agents";
import { createWorkersAI } from "workers-ai-provider";
import {
  streamText,
  convertToModelMessages,
  pruneMessages,
  tool,
  stepCountIs,
} from "ai";
import { z } from "zod";


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


    const result = streamText({
      model: workersai("@cf/meta/llama-4-scout-17b-16e-instruct"),
      system:
        "You are a helpful assistant. You can check the weather, " +
        "get the user's timezone, and run calculations.",
      messages: pruneMessages({
        messages: await convertToModelMessages(this.messages),
        toolCalls: "before-last-2-messages",
      }),
      tools: {
        // Server-side tool: runs automatically on the server
        getWeather: tool({
          description: "Get the current weather for a city",
          inputSchema: z.object({
            city: z.string().describe("City name"),
          }),
          execute: async ({ city }) => {
            // Replace with a real weather API in production
            const conditions = ["sunny", "cloudy", "rainy"];
            const temp = Math.floor(Math.random() * 30) + 5;
            return {
              city,
              temperature: temp,
              condition:
                conditions[Math.floor(Math.random() * conditions.length)],
            };
          },
        }),


        // Client-side tool: no execute function — the browser handles it
        getUserTimezone: tool({
          description: "Get the user's timezone from their browser",
          inputSchema: z.object({}),
        }),


        // Approval tool: requires user confirmation before executing
        calculate: tool({
          description:
            "Perform a math calculation with two numbers. " +
            "Requires user approval for large numbers.",
          inputSchema: z.object({
            a: z.coerce.number().describe("First number"),
            b: z.coerce.number().describe("Second number"),
            operator: z
              .enum(["+", "-", "*", "/", "%"])
              .describe("Arithmetic operator"),
          }),
          needsApproval: async ({ a, b }) =>
            Math.abs(a) > 1000 || Math.abs(b) > 1000,
          execute: async ({ a, b, operator }) => {
            const ops = {
              "+": (x, y) => x + y,
              "-": (x, y) => x - y,
              "*": (x, y) => x * y,
              "/": (x, y) => x / y,
              "%": (x, y) => x % y,
            };
            if (operator === "/" && b === 0) {
              return { error: "Division by zero" };
            }
            return {
              expression: `${a} ${operator} ${b}`,
              result: ops[operator](a, b),
            };
          },
        }),
      },
      stopWhen: stepCountIs(5),
    });


    return result.toUIMessageStreamResponse();
  }
}


export default {
  async fetch(request, env) {
    return (
      (await routeAgentRequest(request, env)) ||
      new Response("Not found", { status: 404 })
    );
  },
};
```

**TypeScript**

```ts
import { AIChatAgent } from "@cloudflare/ai-chat";
import { routeAgentRequest } from "agents";
import { createWorkersAI } from "workers-ai-provider";
import {
  streamText,
  convertToModelMessages,
  pruneMessages,
  tool,
  stepCountIs,
} from "ai";
import { z } from "zod";


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


    const result = streamText({
      model: workersai("@cf/meta/llama-4-scout-17b-16e-instruct"),
      system:
        "You are a helpful assistant. You can check the weather, " +
        "get the user's timezone, and run calculations.",
      messages: pruneMessages({
        messages: await convertToModelMessages(this.messages),
        toolCalls: "before-last-2-messages",
      }),
      tools: {
        // Server-side tool: runs automatically on the server
        getWeather: tool({
          description: "Get the current weather for a city",
          inputSchema: z.object({
            city: z.string().describe("City name"),
          }),
          execute: async ({ city }) => {
            // Replace with a real weather API in production
            const conditions = ["sunny", "cloudy", "rainy"];
            const temp = Math.floor(Math.random() * 30) + 5;
            return {
              city,
              temperature: temp,
              condition:
                conditions[Math.floor(Math.random() * conditions.length)],
            };
          },
        }),


        // Client-side tool: no execute function — the browser handles it
        getUserTimezone: tool({
          description: "Get the user's timezone from their browser",
          inputSchema: z.object({}),
        }),


        // Approval tool: requires user confirmation before executing
        calculate: tool({
          description:
            "Perform a math calculation with two numbers. " +
            "Requires user approval for large numbers.",
          inputSchema: z.object({
            a: z.coerce.number().describe("First number"),
            b: z.coerce.number().describe("Second number"),
            operator: z
              .enum(["+", "-", "*", "/", "%"])
              .describe("Arithmetic operator"),
          }),
          needsApproval: async ({ a, b }) =>
            Math.abs(a) > 1000 || Math.abs(b) > 1000,
          execute: async ({ a, b, operator }) => {
            const ops: Record<string, (x: number, y: number) => number> = {
              "+": (x, y) => x + y,
              "-": (x, y) => x - y,
              "*": (x, y) => x * y,
              "/": (x, y) => x / y,
              "%": (x, y) => x % y,
            };
            if (operator === "/" && b === 0) {
              return { error: "Division by zero" };
            }
            return {
              expression: `${a} ${operator} ${b}`,
              result: ops[operator](a, b),
            };
          },
        }),
      },
      stopWhen: stepCountIs(5),
    });


    return result.toUIMessageStreamResponse();
  }
}


export default {
  async fetch(request: Request, env: Env) {
    return (
      (await routeAgentRequest(request, env)) ||
      new Response("Not found", { status: 404 })
    );
  },
} satisfies ExportedHandler<Env>;
```

### What each tool type does

| Tool            | execute? | needsApproval?      | Behavior                                        |
| --------------- | -------- | ------------------- | ----------------------------------------------- |
| getWeather      | Yes      | No                  | Runs on the server automatically                |
| getUserTimezone | No       | No                  | Sent to the client; browser provides the result |
| calculate       | Yes      | Yes (large numbers) | Pauses for user approval, then runs on server   |

## 4\. Write the client

Create `src/client.tsx`:

* [  JavaScript ](#tab-panel-5929)
* [  TypeScript ](#tab-panel-5930)

**JavaScript**

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


function Chat() {
  const agent = useAgent({ agent: "ChatAgent" });


  const {
    messages,
    sendMessage,
    clearHistory,
    addToolApprovalResponse,
    status,
  } = useAgentChat({
    agent,
    // Handle client-side tools (tools with no server execute function)
    onToolCall: async ({ toolCall, addToolOutput }) => {
      if (toolCall.toolName === "getUserTimezone") {
        addToolOutput({
          toolCallId: toolCall.toolCallId,
          output: {
            timezone: Intl.DateTimeFormat().resolvedOptions().timeZone,
            localTime: new Date().toLocaleTimeString(),
          },
        });
      }
    },
  });


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


              // Render approval UI for tools that need confirmation
              if (part.state === "approval-requested") {
                const approval = getToolApproval(part);
                if (!approval) return null;
                return (
                  <div key={part.toolCallId}>
                    <p>
                      Approve <strong>{part.toolName}</strong>?
                    </p>
                    <pre>{JSON.stringify(part.input, null, 2)}</pre>
                    <button
                      onClick={() =>
                        addToolApprovalResponse({
                          id: approval.id,
                          approved: true,
                        })
                      }
                    >
                      Approve
                    </button>
                    <button
                      onClick={() =>
                        addToolApprovalResponse({
                          id: approval.id,
                          approved: false,
                        })
                      }
                    >
                      Reject
                    </button>
                  </div>
                );
              }


              // Show completed tool results
              if (part.state === "output-available") {
                return (
                  <details key={part.toolCallId}>
                    <summary>{part.toolName} result</summary>
                    <pre>{JSON.stringify(part.output, null, 2)}</pre>
                  </details>
                );
              }


              return null;
            })}
          </div>
        ))}
      </div>


      <form
        onSubmit={(e) => {
          e.preventDefault();
          const input = e.currentTarget.elements.namedItem("message");
          sendMessage({ text: input.value });
          input.value = "";
        }}
      >
        <input name="message" placeholder="Try: What's the weather in Paris?" />
        <button type="submit" disabled={status === "streaming"}>
          Send
        </button>
      </form>


      <button onClick={clearHistory}>Clear history</button>
    </div>
  );
}


export default function App() {
  return <Chat />;
}
```

**TypeScript**

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


function Chat() {
  const agent = useAgent({ agent: "ChatAgent" });


  const { messages, sendMessage, clearHistory, addToolApprovalResponse, status } =
    useAgentChat({
      agent,
      // Handle client-side tools (tools with no server execute function)
      onToolCall: async ({ toolCall, addToolOutput }) => {
        if (toolCall.toolName === "getUserTimezone") {
          addToolOutput({
            toolCallId: toolCall.toolCallId,
            output: {
              timezone: Intl.DateTimeFormat().resolvedOptions().timeZone,
              localTime: new Date().toLocaleTimeString(),
            },
          });
        }
      },
    });


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


              // Render approval UI for tools that need confirmation
              if (part.state === "approval-requested") {
                const approval = getToolApproval(part);
                if (!approval) return null;
                return (
                  <div key={part.toolCallId}>
                    <p>
                      Approve <strong>{part.toolName}</strong>?
                    </p>
                    <pre>{JSON.stringify(part.input, null, 2)}</pre>
                    <button
                      onClick={() =>
                        addToolApprovalResponse({
                          id: approval.id,
                          approved: true,
                        })
                      }
                    >
                      Approve
                    </button>
                    <button
                      onClick={() =>
                        addToolApprovalResponse({
                          id: approval.id,
                          approved: false,
                        })
                      }
                    >
                      Reject
                    </button>
                  </div>
                );
              }


              // Show completed tool results
              if (part.state === "output-available") {
                return (
                  <details key={part.toolCallId}>
                    <summary>{part.toolName} result</summary>
                    <pre>{JSON.stringify(part.output, null, 2)}</pre>
                  </details>
                );
              }


              return null;
            })}
          </div>
        ))}
      </div>


      <form
        onSubmit={(e) => {
          e.preventDefault();
          const input = e.currentTarget.elements.namedItem(
            "message",
          ) as HTMLInputElement;
          sendMessage({ text: input.value });
          input.value = "";
        }}
      >
        <input name="message" placeholder="Try: What's the weather in Paris?" />
        <button type="submit" disabled={status === "streaming"}>
          Send
        </button>
      </form>


      <button onClick={clearHistory}>Clear history</button>
    </div>
  );
}


export default function App() {
  return <Chat />;
}
```

### Key client concepts

* **`useAgent`** connects to your `ChatAgent` over WebSocket
* **`useAgentChat`** manages the chat lifecycle (messages, streaming, tools)
* **`onToolCall`** handles client-side tools — when the LLM calls `getUserTimezone`, the browser provides the result and the conversation auto-continues
* **`addToolApprovalResponse`** approves or rejects tools that have `needsApproval`
* Messages, streaming, and resumption are all handled automatically

## 5\. Run locally

Generate types and start the dev server:

```sh
npx wrangler types
npm run dev
```

Try these prompts:

* **"What is the weather in Tokyo?"** — calls the server-side `getWeather` tool
* **"What timezone am I in?"** — calls the client-side `getUserTimezone` tool (the browser provides the answer)
* **"What is 5000 times 3?"** — triggers the approval UI before executing (numbers over 1000)

## 6\. Deploy

```sh
npx wrangler deploy
```

Your agent is now live on Cloudflare's global network. Messages persist in SQLite, streams resume on disconnect, and the agent hibernates when idle to save resources.

## What you built

Your chat agent has:

* **Streaming AI responses** via Workers AI (no API keys)
* **Message persistence** in SQLite — conversations survive restarts
* **Server-side tools** that execute automatically
* **Client-side tools** that run in the browser and feed results back to the LLM
* **Human-in-the-loop approval** for sensitive operations
* **Resumable streaming** — if a client disconnects mid-stream, it picks up where it left off

## Next steps

[ Chat agents API reference ](https://edgetunnel-b2h.pages.dev/agents/communication-channels/chat/chat-agents/) Full reference for AIChatAgent and useAgentChat — providers, storage, advanced patterns. 

[ Store and sync state ](https://edgetunnel-b2h.pages.dev/agents/runtime/lifecycle/state/) Add real-time state beyond chat messages. 

[ Callable methods ](https://edgetunnel-b2h.pages.dev/agents/runtime/lifecycle/callable-methods/) Expose agent methods as typed RPC for your client. 

[ Human-in-the-loop ](https://edgetunnel-b2h.pages.dev/agents/concepts/agentic-patterns/human-in-the-loop/) Deeper patterns for approval flows and manual intervention.

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```