ai-code-mode

Note: This skill requires familiarity with ai-core and ai-core/chat-experience. Code Mode is always used on top of a chat experience.

Setup

Complete Code Mode setup with Node.js isolate driver:

import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { createCodeModeTool } from '@tanstack/ai-code-mode'
import { createNodeIsolateDriver } from '@tanstack/ai-isolate-node'
import { toolDefinition } from '@tanstack/ai'
import { z } from 'zod'

// Define a tool that code can call
const fetchWeather = toolDefinition({
  name: 'fetchWeather',
  description: 'Get current weather for a city',
  inputSchema: z.object({ city: z.string() }),
  outputSchema: z.object({ temp: z.number(), condition: z.string() }),
}).server(async ({ city }) => {
  const res = await fetch(`https://api.weather.com/${city}`)
  return res.json()
})

// Create code mode tool with Node isolate
const codeModeTool = createCodeModeTool({
  driver: createNodeIsolateDriver({
    memoryLimit: 128,
    timeout: 30000,
  }),
  tools: [fetchWeather],
})

// Use in chat
const stream = chat({
  adapter: openaiText('gpt-5.2'),
  messages,
  tools: [codeModeTool],
})

return toServerSentEventsResponse(stream)

The recommended higher-level entry point is createCodeMode(), which returns both the tool and a matching system prompt:

import { chat } from '@tanstack/ai'
import { createCodeMode } from '@tanstack/ai-code-mode'
import { createNodeIsolateDriver } from '@tanstack/ai-isolate-node'
import { openaiText } from '@tanstack/ai-openai'

const { tool, systemPrompt } = createCodeMode({
  driver: createNodeIsolateDriver(),
  tools: [fetchWeather],
  timeout: 30_000,
})

const stream = chat({
  adapter: openaiText('gpt-4o'),
  systemPrompts: ['You are a helpful assistant.', systemPrompt],
  tools: [tool],
  messages,
})

createCodeMode calls createCodeModeTool and createCodeModeSystemPrompt internally. The system prompt includes generated TypeScript type stubs for each tool so the LLM writes correct calls.

Core Patterns

1. Choosing an Isolate Driver

Three drivers implement the IsolateDriver interface. All are interchangeable.

Node.js (createNodeIsolateDriver) -- Full V8 with JIT. Fastest option. Requires isolated-vm native C++ addon.

import { createNodeIsolateDriver } from '@tanstack/ai-isolate-node'

const driver = createNodeIsolateDriver({
  memoryLimit: 128, // MB, default 128
  timeout: 30_000, // ms, default 30000
  // skipProbe: false -- set true only after verifying compatibility
})

QuickJS (createQuickJSIsolateDriver) -- WASM-based, no native deps. Works in Node.js, browsers, Deno, Bun, and edge runtimes. Slower (interpreted, no JIT). Limited stdlib (no File I/O).

import { createQuickJSIsolateDriver } from '@tanstack/ai-isolate-quickjs'

const driver = createQuickJSIsolateDriver({
  memoryLimit: 128, // MB, default 128
  timeout: 30_000, // ms, default 30000
  maxStackSize: 524288, // bytes, default 512 KiB
})

Cloudflare (createCloudflareIsolateDriver) -- Edge execution via a deployed Cloudflare Worker. Requires a workerUrl pointing to your deployed worker. Network latency on each tool call.

import { createCloudflareIsolateDriver } from '@tanstack/ai-isolate-cloudflare'

const driver = createCloudflareIsolateDriver({
  workerUrl: 'https://my-code-mode-worker.my-account.workers.dev',
  authorization: process.env.CODE_MODE_WORKER_SECRET,
  timeout: 30_000, // ms, default 30000
  maxToolRounds: 10, // max tool-call/result cycles, default 10
})

2. Adding Persistent Skills with codeModeWithSkills()

Skills let the LLM save reusable code snippets. On future requests, relevant skills are loaded and exposed as callable tools.

import { chat, maxIterations } from '@tanstack/ai'
import { createNodeIsolateDriver } from '@tanstack/ai-isolate-node'
import { codeModeWithSkills } from '@tanstack/ai-code-mode-skills'
import { createFileSkillStorage } from '@tanstack/ai-code-mode-skills/storage'
import {
  createDefaultTrustStrategy,
  createAlwaysTrustedStrategy,
  createCustomTrustStrategy,
} from '@tanstack/ai-code-mode-skills'
import { openaiText } from '@tanstack/ai-openai'

// Trust strategies control how skills earn trust through executions
// Default: untrusted -> provisional (10+ runs, >=90%) -> trusted (100+ runs, >=95%)
// Relaxed: untrusted -> provisional (3+ runs, >=80%) -> trusted (10+ runs, >=90%)
// Always trusted: immediately trusted (dev/testing)
// Custom: configurable thresholds
const trustStrategy = createDefaultTrustStrategy()

// Storage options: file system (production) or memory (testing)
const storage = createFileSkillStorage({
  directory: './.skills',
  trustStrategy,
})

const driver = createNodeIsolateDriver()

// High-level API: automatic LLM-based skill selection
const { toolsRegistry, systemPrompt, selectedSkills } =
  await codeModeWithSkills({
    config: {
      driver,
      tools: [myTool1, myTool2],
      timeout: 60_000,
      memoryLimit: 128,
    },
    adapter: openaiText('gpt-4o-mini'), // cheap model for skill selection
    skills: {
      storage,
      maxSkillsInContext: 5,
    },
    messages,
  })

const stream = chat({
  adapter: openaiText('gpt-4o'),
  tools: toolsRegistry.getTools(),
  messages,
  systemPrompts: ['You are a helpful assistant.', systemPrompt],
  agentLoopStrategy: maxIterations(15),
})

The registry includes: execute_typescript, search_skills, get_skill, register_skill, and one tool per selected skill.

Custom trust strategy example:

const strategy = createCustomTrustStrategy({
  initialLevel: 'untrusted',
  provisionalThreshold: { executions: 5, successRate: 0.85 },
  trustedThreshold: { executions: 50, successRate: 0.95 },
})

Storage implementations:

// File storage (production) -- persists skills as files on disk
import { createFileSkillStorage } from '@tanstack/ai-code-mode-skills/storage'
const fileStorage = createFileSkillStorage({ directory: './.skills' })

// Memory storage (testing) -- in-memory, lost on restart
import { createMemorySkillStorage } from '@tanstack/ai-code-mode-skills/storage'
const memStorage = createMemorySkillStorage()

3. Client-Side Execution Progress Display

Code Mode emits custom events during sandbox execution. Handle them in useChat via onCustomEvent.

Events emitted:

import { useCallback, useRef, useState } from 'react'
import { useChat, fetchServerSentEvents } from '@tanstack/ai-react'

interface VMEvent {
  id: string
  eventType: string
  data: unknown
  timestamp: number
}

export function CodeModeChat() {
  const [toolCallEvents, setToolCallEvents] = useState<
    Map<string, Array<VMEvent>>
  >(new Map())
  const eventIdCounter = useRef(0)

  const handleCustomEvent = useCallback(
    (
      eventType: string,
      data: unknown,
      context: { toolCallId?: string },
    ) => {
      const { toolCallId } = context
      if (!toolCallId) return

      const event: VMEvent = {
        id: `event-${eventIdCounter.current++}`,
        eventType,
        data,
        timestamp: Date.now(),
      }

      setToolCallEvents((prev) => {
        const next = new Map(prev)
        const events = next.get(toolCallId) || []
        next.set(toolCallId, [...events, event])
        return next
      })
    },
    [],
  )

  const { messages, sendMessage, isLoading } = useChat({
    connection: fetchServerSentEvents('/api/chat'),
    onCustomEvent: handleCustomEvent,
  })

  return (
    <div>
      {messages.map((message) => (
        <div key={message.id}>
          {message.parts.map((part) => {
            if (part.type === 'text') {
              return <p key={part.id}>{part.content}</p>
            }
            if (
              part.type === 'tool-call' &&
              part.name === 'execute_typescript'
            ) {
              const events = toolCallEvents.get(part.id) || []
              return (
                <div key={part.id}>
                  <pre>{JSON.parse(part.arguments)?.typescriptCode}</pre>
                  {events.map((evt) => (
                    <div key={evt.id}>
                      {evt.eventType}: {JSON.stringify(evt.data)}
                    </div>
                  ))}
                  {part.output && (
                    <pre>{JSON.stringify(part.output, null, 2)}</pre>
                  )}
                </div>
              )
            }
            return null
          })}
        </div>
      ))}
    </div>
  )
}

The onCustomEvent callback signature is identical across all framework integrations (@tanstack/ai-react, @tanstack/ai-solid, @tanstack/ai-vue, @tanstack/ai-svelte):

(eventType: string, data: unknown, context: { toolCallId?: string }) => void

Skill-specific events (when using codeModeWithSkills):

4. Lazy Tools

When a large tool catalog would bloat the execute_typescript system prompt, mark low-priority tools lazy: true. Lazy tools are kept out of the full type-stub documentation and listed in a compact "Discoverable APIs" catalog instead. All sandbox bindings are always injected — lazy defers documentation, not callability.

Marking a tool lazy:

import { toolDefinition } from '@tanstack/ai'
import { z } from 'zod'

const rarelyUsedTool = toolDefinition({
  name: 'fetchStocks',
  description: 'Get stock prices for a ticker. Returns a price quote.',
  inputSchema: z.object({ ticker: z.string() }),
  outputSchema: z.object({ price: z.number() }),
  lazy: true, // <-- opt out of full system-prompt documentation
}).server(async ({ ticker }) => {
  // ...
  return { price: 0 }
})

createCodeMode return shape:

createCodeMode() returns { tool, discoveryTool, tools, systemPrompt }. When lazy tools are present discoveryTool is a discover_tools server tool; otherwise it is null. Always spread tools (not just tool) into chat() so the discovery tool is registered:

import { chat } from '@tanstack/ai'
import { createCodeMode } from '@tanstack/ai-code-mode'
import { createNodeIsolateDriver } from '@tanstack/ai-isolate-node'
import { openaiText } from '@tanstack/ai-openai'

const { tools, systemPrompt } = createCodeMode({
  driver: createNodeIsolateDriver(),
  tools: [eagerTool, rarelyUsedTool], // rarelyUsedTool has lazy: true
})

const stream = chat({
  adapter: openaiText('gpt-5.5'),
  systemPrompts: ['You are a helpful assistant.', systemPrompt],
  tools: [...tools, ...otherTools], // spread tools, not just tool
  messages,
})

tools equals [tool] when there are no lazy tools (backward compatible) and [tool, discoveryTool] when lazy tools exist.

discover_tools flow:

When the model encounters a lazy tool it has not seen before, it calls discover_tools with the bare name (no external_ prefix). The tool returns each requested tool's TypeScript type stub and description. The model then writes correctly-typed external_<name> calls inside execute_typescript.

Model sees: "Discoverable APIs: external_fetchStocks"
Model calls: discover_tools({ toolNames: ["fetchStocks"] })
Response:    { tools: [{ name: "external_fetchStocks", description: "...", typeStub: "declare function external_fetchStocks(...)" }] }
Model writes inside execute_typescript: const result = await external_fetchStocks({ ticker: "AAPL" })

lazyToolsConfig.includeDescription:

Control how much of each lazy tool's description appears in the Discoverable APIs catalog (the pre-discovery list):

const { tools, systemPrompt } = createCodeMode({
  driver: createNodeIsolateDriver(),
  tools: [eagerTool, rarelyUsedTool],
  lazyToolsConfig: { includeDescription: 'first-sentence' },
})

The same lazyToolsConfig option is accepted by plain chat() for its own lazy-tool discovery catalog (see ai-core/tool-calling/SKILL.md).

Common Mistakes

CRITICAL: Passing API keys or secrets to the sandbox environment

Code Mode executes LLM-generated code. Any secrets available in the sandbox context are accessible to generated code, which could exfiltrate them via tool calls. Never pass API keys, database credentials, or tokens into the sandbox. Keep secrets in your tool server implementations, which run in the host process outside the sandbox.

Wrong:

const codeModeTool = createCodeModeTool({
  driver,
  tools: [
    toolDefinition({
      name: 'callApi',
      inputSchema: z.object({ url: z.string(), apiKey: z.string() }),
      outputSchema: z.any(),
    }).server(async ({ url, apiKey }) =>
      fetch(url, {
        headers: { Authorization: apiKey },
      }),
    ),
  ],
})

Right:

const codeModeTool = createCodeModeTool({
  driver,
  tools: [
    toolDefinition({
      name: 'callApi',
      inputSchema: z.object({ url: z.string() }),
      outputSchema: z.any(),
    }).server(async ({ url }) =>
      fetch(url, {
        headers: { Authorization: process.env.API_KEY }, // secret stays in host
      }),
    ),
  ],
})

Source: docs/code-mode/code-mode.md

HIGH: Not setting timeout for code execution

LLM-generated code may contain infinite loops. The default timeout is 30s, but developers may override to 0 (no timeout). Always set an explicit, finite timeout.

Wrong:

const driver = createNodeIsolateDriver({ timeout: 0 })

Right:

const driver = createNodeIsolateDriver({ timeout: 30_000 })

Source: ai-code-mode source (default timeout in CodeModeToolConfig)

HIGH: Using Node isolated-vm driver without checking platform compatibility

isolated-vm requires native module compilation. An incompatible build (wrong Node.js version, missing build tools) causes segfaults that no JS error handling can catch. The driver runs a subprocess probe by default. Never set skipProbe: true unless you have independently verified compatibility. Use probeIsolatedVm() to check before creating the driver.

import {
  createNodeIsolateDriver,
  probeIsolatedVm,
} from '@tanstack/ai-isolate-node'

const probe = probeIsolatedVm()
if (!probe.compatible) {
  console.error('isolated-vm not compatible:', probe.error)
  // Fall back to QuickJS
}

// Never do this unless you verified compatibility yourself:
// const driver = createNodeIsolateDriver({ skipProbe: true })

Source: ai-isolate-node source (probeIsolatedVm implementation)

MEDIUM: Expecting identical behavior across isolate drivers

The three drivers have different capabilities. Same code may work in Node but fail elsewhere.

• Node: Full V8 support, JIT compilation, configurable memory limit
• QuickJS: Interpreted, limited stdlib (no File I/O), configurable stack size, asyncified execution (serialized through global queue)
• Cloudflare: Network latency per tool call round-trip, maxToolRounds limit (default 10), requires deployed worker with UNSAFE_EVAL or eval unsafe binding

Test generated code against your target driver. If you need portability, target QuickJS's subset.

Source: docs/code-mode/code-mode-isolates.md

Cross-References

• See also: ai-core/tool-calling/SKILL.md -- Code Mode is an alternative to standard tool calling for complex multi-step operations
• See also: ai-core/chat-experience/SKILL.md -- Code Mode requires handling custom events in useChat