Codemode (Experimental)

Let LLMs write and execute code to orchestrate tools in isolated Worker sandboxes

Experimental — this feature may have breaking changes in future releases. Use with caution in production.

Codemode lets LLMs write and execute code that orchestrates your tools, instead of calling them one at a time. Inspired by CodeAct, it works because LLMs are better at writing code than making individual tool calls — they have seen millions of lines of real-world TypeScript but only contrived tool-calling examples.

The @cloudflare/codemode package converts your tools into typed TypeScript APIs, gives the LLM a single “write code” tool, and executes the generated code in a secure, isolated Worker sandbox.

When to use Codemode

Codemode is most useful when the LLM needs to:

• Chain multiple tool calls with logic between them (conditionals, loops, error handling)
• Compose results from different tools before returning
• Work with MCP servers that expose many fine-grained operations
• Perform multi-step workflows that would require many round-trips with standard tool calling

For simple, single tool calls, standard AI SDK tool calling is simpler and sufficient.

Installation

npm install @cloudflare/codemode ai zod

Quick Start

1. Define your tools

   Use the standard AI SDK tool() function:

   import { tool } from "ai";
   import { z } from "zod";
   
   const tools = {
     getWeather: tool({
       description: "Get weather for a location",
       inputSchema: z.object({ location: z.string() }),
       execute: async ({ location }) => `Weather in ${location}: 72°F, sunny`
     }),
     sendEmail: tool({
       description: "Send an email",
       inputSchema: z.object({
         to: z.string(),
         subject: z.string(),
         body: z.string()
       }),
       execute: async ({ to, subject, body }) => `Email sent to ${to}`
     })
   };
   
2. Create the codemode tool

   createCodeTool takes your tools and an executor, and returns a single AI SDK tool:

   import { createCodeTool } from "@cloudflare/codemode/ai";
   import { DynamicWorkerExecutor } from "@cloudflare/codemode";
   
   const executor = new DynamicWorkerExecutor({
     loader: env.LOADER
   });
   
   const codemode = createCodeTool({ tools, executor });
   
3. Use with streamText

   Pass the codemode tool to streamText or generateText like any other tool:

   import { streamText } from "ai";
   
   const result = streamText({
     model,
     system: "You are a helpful assistant.",
     messages,
     tools: { codemode }
   });
   

What the LLM writes

When the LLM decides to use codemode, it writes an async arrow function like:

async () => {
  const weather = await codemode.getWeather({ location: "London" });
  if (weather.includes("sunny")) {
    await codemode.sendEmail({
      to: "[e-mail]",
      subject: "Nice day!",
      body: `It's ${weather}`
    });
  }
  return { weather, notified: true };
};

The code runs in an isolated Worker sandbox, tool calls are dispatched back to the host via Workers RPC, and the result is returned to the LLM.

Configuration

Wrangler bindings

Add a worker_loaders binding to your wrangler.jsonc. This is the only binding required:

wrangler.jsonc

{
  "worker_loaders": [{ "binding": "LOADER" }],
  "compatibility_flags": ["nodejs_compat"]
}

Vite configuration

If you use zod-to-ts (which codemode depends on), add a __filename define to your Vite config:

vite.config.ts

export default defineConfig({
  plugins: [react(), cloudflare(), tailwindcss()],
  define: {
    __filename: "'index.ts'"
  }
});

How it works

┌─────────────┐        ┌──────────────────────────────────────┐
│             │        │  Dynamic Worker (isolated sandbox)   │
│  Host       │  RPC   │                                      │
│  Worker     │◄──────►│  LLM-generated code runs here        │
│             │        │  codemode.myTool() → dispatcher.call()│
│  ToolDispatcher      │                                      │
│  holds tool fns      │  fetch() blocked by default          │
└─────────────┘        └──────────────────────────────────────┘

1. Type generation

   createCodeTool generates TypeScript type definitions from your tools and builds a description the LLM can read

2. LLM writes code

   The LLM writes an async arrow function that calls codemode.toolName(args)

3. Code normalization

   The code is normalized via AST parsing (acorn) and sent to the executor

4. Sandbox execution

   DynamicWorkerExecutor spins up an isolated Worker via WorkerLoader

5. Tool dispatch

   Inside the sandbox, a Proxy intercepts codemode.* calls and routes them back to the host via Workers RPC (ToolDispatcher extends RpcTarget)

6. Console capture

   Console output (console.log, console.warn, console.error) is captured and returned in the result

Network isolation

External fetch() and connect() are blocked by default — enforced at the Workers runtime level via globalOutbound: null. Sandboxed code can only interact with the host through codemode.* tool calls.

To allow controlled outbound access, pass a Fetcher:

const executor = new DynamicWorkerExecutor({
  loader: env.LOADER,
  globalOutbound: null // default — fully isolated
  // globalOutbound: env.MY_OUTBOUND_SERVICE  // route through a Fetcher
});

Using with an Agent

The typical pattern is to create the executor and codemode tool inside an Agent’s message handler:

import { Agent } from "agents";
import { createCodeTool } from "@cloudflare/codemode/ai";
import { DynamicWorkerExecutor } from "@cloudflare/codemode";
import { streamText, convertToModelMessages, stepCountIs } from "ai";

export class MyAgent extends Agent<Env, State> {
  async onChatMessage() {
    const executor = new DynamicWorkerExecutor({
      loader: this.env.LOADER
    });

    const codemode = createCodeTool({
      tools: myTools,
      executor
    });

    const result = streamText({
      model,
      system: "You are a helpful assistant.",
      messages: await convertToModelMessages(this.state.messages),
      tools: { codemode },
      stopWhen: stepCountIs(10)
    });

    // Stream response back to client...
  }
}

With MCP tools

MCP tools work the same way — merge them into the tool set:

const codemode = createCodeTool({
  tools: {
    ...myTools,
    ...this.mcp.getAITools()
  },
  executor
});

Tool names with hyphens or dots (common in MCP) are automatically sanitized to valid JavaScript identifiers (e.g., my-server.list-items becomes my_server_list_items).

API Reference

createCodeTool(options)

Returns an AI SDK compatible Tool.

Option	Type	Default	Description
tools	ToolSet | ToolDescriptors	required	Your tools (AI SDK tool() or raw descriptors)
executor	Executor	required	Where to run the generated code
description	string	auto-generated	Custom tool description. Use {{types}} for type defs

DynamicWorkerExecutor

Executes code in an isolated Cloudflare Worker via WorkerLoader.

Option	Type	Default	Description
loader	WorkerLoader	required	Worker Loader binding from env.LOADER
timeout	number	30000	Execution timeout in ms
globalOutbound	Fetcher | null	null	Network access control. null = blocked, Fetcher = routed

generateTypes(tools)

Generates TypeScript type definitions from your tools. Used internally by createCodeTool but exported for custom use (e.g., displaying types in a frontend).

import { generateTypes } from "@cloudflare/codemode";

const types = generateTypes(myTools);
// Returns:
// type CreateProjectInput = { name: string; description?: string }
// declare const codemode: { createProject: (input: CreateProjectInput) => Promise<unknown>; }

sanitizeToolName(name)

Converts tool names into valid JavaScript identifiers.

import { sanitizeToolName } from "@cloudflare/codemode";

sanitizeToolName("get-weather"); // "get_weather"
sanitizeToolName("3d-render"); // "_3d_render"
sanitizeToolName("delete"); // "delete_"

The Executor interface

The Executor interface is deliberately minimal — implement it to run code in any sandbox:

interface Executor {
  execute(
    code: string,
    fns: Record<string, (...args: unknown[]) => Promise<unknown>>
  ): Promise<ExecuteResult>;
}

interface ExecuteResult {
  result: unknown;
  error?: string;
  logs?: string[];
}

DynamicWorkerExecutor is the built-in Cloudflare Workers implementation. You can build your own for Node VM, QuickJS, containers, or any other sandbox.

Security Considerations

• Code runs in isolated Worker sandboxes — each execution gets its own Worker instance
• External network access (fetch, connect) is blocked by default at the runtime level
• Tool calls are dispatched via Workers RPC, not network requests
• Execution has a configurable timeout (default 30 seconds)
• Console output is captured separately and does not leak to the host

Current Limitations

Tool approval (needsApproval) is not supported yet. Tools with needsApproval: true execute immediately inside the sandbox without pausing for approval. Support for approval flows within codemode is planned. For now, do not pass approval-required tools to createCodeTool — use them through standard AI SDK tool calling instead.

• Requires Cloudflare Workers environment for DynamicWorkerExecutor
• Limited to JavaScript execution
• The zod-to-ts dependency bundles the TypeScript compiler, which increases Worker size
• LLM code quality depends on prompt engineering and model capability

Example

See the codemode example for a full working example — a project management assistant that uses codemode to orchestrate tasks, sprints, and comments via SQLite.

Related Documentation

• Chat Agents — Full AIChatAgent reference
• MCP Integration — Using Model Context Protocol servers with agents