Grok Build

The Grok Build adapter runs xAI's Grok Build coding agent as a chat backend. Unlike HTTP provider adapters, this is a harness adapter: Grok Build runs its own agent loop and executes its own tools — shell commands, file edits, search — by spawning the grok CLI inside a sandbox. Each chat() call runs one full harness turn; the harness's tool activity streams back as already-resolved tool-call events your UI can render.

Requires a sandbox. grok-build declares requires: [SandboxCapability], so chat() errors at the call site unless you provide a sandbox with withSandbox(...) middleware. The sandbox — your laptop, a Docker container, or a cloud VM — is the filesystem and safety boundary the agent runs in. See the Sandboxes overview for the full picture.

Installation#

npm install @tanstack/ai-grok-build @tanstack/ai-sandbox

npm install @tanstack/ai-grok-build @tanstack/ai-sandbox

You also need a sandbox provider (e.g. @tanstack/ai-sandbox-docker) and the grok CLI available inside the sandbox image.

Authentication#

Grok Build resolves credentials the same way the grok CLI does:

• the XAI_API_KEY environment variable (headless / sandbox — inject it as a workspace secret), or
• an existing grok.com browser login on the machine (local dev).

The two auth modes expose the model under slightly different ids; the adapter maps the short alias for you (see Models).

Basic Usage#

import { chat } from '@tanstack/ai'
import { grokBuildText } from '@tanstack/ai-grok-build'
import {
  createSecrets,
  defineSandbox,
  defineWorkspace,
  githubRepo,
  withSandbox,
} from '@tanstack/ai-sandbox'
import { dockerSandbox } from '@tanstack/ai-sandbox-docker'
import { messages, threadId } from './chat-context'

const sandbox = defineSandbox({
  id: 'grok-build-agent',
  provider: dockerSandbox({ image: 'node:22' }),
  workspace: defineWorkspace({
    source: githubRepo({ repo: 'owner/app' }),
    setup: ['corepack enable', 'pnpm install'],
    secrets: createSecrets({ XAI_API_KEY: process.env.XAI_API_KEY ?? '' }),
  }),
})

const stream = chat({
  threadId,
  adapter: grokBuildText('grok-build'),
  messages,
  middleware: [withSandbox(sandbox)],
})

import { chat } from '@tanstack/ai'
import { grokBuildText } from '@tanstack/ai-grok-build'
import {
  createSecrets,
  defineSandbox,
  defineWorkspace,
  githubRepo,
  withSandbox,
} from '@tanstack/ai-sandbox'
import { dockerSandbox } from '@tanstack/ai-sandbox-docker'
import { messages, threadId } from './chat-context'

const sandbox = defineSandbox({
  id: 'grok-build-agent',
  provider: dockerSandbox({ image: 'node:22' }),
  workspace: defineWorkspace({
    source: githubRepo({ repo: 'owner/app' }),
    setup: ['corepack enable', 'pnpm install'],
    secrets: createSecrets({ XAI_API_KEY: process.env.XAI_API_KEY ?? '' }),
  }),
})

const stream = chat({
  threadId,
  adapter: grokBuildText('grok-build'),
  messages,
  middleware: [withSandbox(sandbox)],
})

Models#

Grok Build accepts any xAI model id its backend supports; the known ids get autocomplete (any string is allowed):

Pass any of these to grokBuildText(...) — the adapter resolves grok-build to the CLI's grok-build-0.1 automatically, so the same code works under both auth modes.

Configuration#

Adapter config (second argument to grokBuildText):

Per-call overrides go through modelOptions:

Stateful Sessions#

Grok Build sessions are stateful — the harness keeps the working context (files read, commands run, conclusions reached) between turns. The adapter surfaces the session id of every fresh run as a custom stream event named grok-build.session-id; thread it back via modelOptions.sessionId to resume. When resuming, only the latest user message is sent — the harness already holds the prior context.

import { chat, chatParamsFromRequest, toServerSentEventsResponse } from '@tanstack/ai'
import { grokBuildText } from '@tanstack/ai-grok-build'
import { withSandbox } from '@tanstack/ai-sandbox'
import { sandbox } from './sandbox'

export async function POST(request: Request) {
  const params = await chatParamsFromRequest(request)
  const sessionId =
    typeof params.forwardedProps.sessionId === 'string'
      ? params.forwardedProps.sessionId
      : undefined

  const stream = chat({
    adapter: grokBuildText('grok-build'),
    messages: params.messages,
    middleware: [withSandbox(sandbox)],
    modelOptions: { sessionId },
  })

  return toServerSentEventsResponse(stream)
}

import { chat, chatParamsFromRequest, toServerSentEventsResponse } from '@tanstack/ai'
import { grokBuildText } from '@tanstack/ai-grok-build'
import { withSandbox } from '@tanstack/ai-sandbox'
import { sandbox } from './sandbox'

export async function POST(request: Request) {
  const params = await chatParamsFromRequest(request)
  const sessionId =
    typeof params.forwardedProps.sessionId === 'string'
      ? params.forwardedProps.sessionId
      : undefined

  const stream = chat({
    adapter: grokBuildText('grok-build'),
    messages: params.messages,
    middleware: [withSandbox(sandbox)],
    modelOptions: { sessionId },
  })

  return toServerSentEventsResponse(stream)
}

Tools#

Two kinds of tools flow through this adapter:

1. Built-in harness tools are executed by Grok Build itself (shell, file edits, search) and stream back as tool-call events with results already attached. Your code never executes them.
2. Your TanStack tools are bridged into the harness over an authenticated MCP tool-proxy: define them with toolDefinition().server() and pass them to chat({ tools }). Tool-call events come back under the names you registered. Because the harness runs in a sandbox, see Sandbox tools for how the bridge reaches your host across providers (local/Docker vs cloud).

Client-side and approval-gated tools are not supported — the harness runs tools inside a live process and can't pause across an HTTP round-trip. A tool without a server execute() (or marked needsApproval) fails fast; run those with a regular provider adapter.

Limitations#

• Requires a sandbox. Always run it under withSandbox(...); see the Sandboxes overview.
• Server-only (Node). The harness spawns the grok CLI in a sandbox.
• The harness owns the agent loop. TanStack's agent-loop strategies and per-iteration middleware don't apply inside a harness turn.
• No sampling controls. temperature-style options don't exist here.
• Cold starts. Each call runs a full harness turn; expect higher first-token latency than HTTP adapters.