Clawdbot is a personal AI assistant powered by Claude with persistent memory. It can browse the web, run shell commands, and manage files across any operating system.

You can use Clawdbot with Vercel AI Gateway to access hundreds of models from multiple providers through a single endpoint. AI Gateway provides unified API access across models without managing separate API keys.

Create an API key in the AI Gateway dashboard, then install Clawdbot:

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curl -fsSL https://install.clawdbot.com
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# or
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npm install -g clawdbot

Run the onboarding wizard:

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clawdbot onboard --install-daemon

Select Vercel AI Gateway as your provider and enter your AI Gateway API key.

You can then choose from hundreds of available models. Your AI assistant is now running and ready to help with tasks across your system.

See the AI Gateway docs for more details on Clawdbot and more integrations.

You can now customize error pages for platform errors on Vercel, replacing generic error pages with your own branded experiences. Custom error pages display when Vercel encounters uncaught errors like function invocation timeouts or other platform errors.

Link to headingHow it works

You can implement custom error pages using your framework’s conventions and Vercel will automatically locate them, for example with Next.js you can simply place a 500/page.tsx or static 500.html page in the public directory.

To enrich error pages with request-specific context, you can use the following metadata tokens:

• ::vercel:REQUEST_ID:: - Contains the Vercel request ID

• ::vercel:ERROR_CODE:: - The specific error code e.g. FUNCTION_INVOCATION_TIMEOUT

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export default function CustomErrorPage() {
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  return (
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    <div className="flex min-h-screen flex-col items-center justify-center">
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      <h1 className="text-4xl font-bold">500</h1>
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      <p className="mt-4 text-lg text-gray-600">Internal Server Error</p>
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      <p className="mt-2 text-sm text-gray-500">
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        Request ID: ::vercel:REQUEST_ID::
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      </p>
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      <p className="mt-2 text-sm text-gray-500">
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        Code: ::vercel:ERROR_CODE::
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      </p>
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      <p className="mt-2 text-sm text-gray-500">
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        Something went wrong on our end. Please try again later.
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      </p>
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      <a href="/" className="mt-6 text-blue-600 hover:underline">
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        Go back home
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      </a>
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    </div>
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  );
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}

This feature is available for Enterprise teams and enabled automatically across all projects. No additional configuration required.

See the documentation to get started or reference the following implementations: Custom error pages with App Router or Custom error pages with public directory.

Build and deployment settings can now be configured at the team level and applied across all projects, compared to the previous project-by-project setup.

Build Machines let you choose the compute resources for each build to optimize build times:

• Standard build machines with 4 vCPUs and 8 GB of memory

• Enhanced build machines with 8 vCPUs and 16 GB of memory

• Turbo build machines with 30 vCPUs and 60 GB of memory

On-Demand Concurrent Builds control how many builds can run in parallel and whether builds skip the queue.

You can now apply configurations to all projects at once, or make targeted changes across multiple projects from a single interface.

Get started with team-level settings.

Function uploads are now skipped when code hasn't changed, reducing build times by 400-600ms on average and up to 5 seconds for larger builds.

Previously, deployment-specific environment variables like VERCEL_DEPLOYMENT_ID were included in the function payload, making every deployment unique even with identical code. These variables are now injected at runtime, allowing Vercel to recognize unchanged functions and skip redundant uploads.

This optimization applies to Vercel Functions without a framework, and projects using Python, Go, Ruby, and Rust. Next.js projects will receive the same improvement soon.

The optimization is applied automatically to all deployments with no configuration required.

Learn more about functions and builds in our documentation.

A redesign of the navigation in the dashboard is now available as an opt-in experience. This new navigation maintains full functionality while streamlining access to your most-used features.

• New Sidebar — Moved horizontal tabs to a resizable sidebar that can be hidden when not needed

• Consistent Tabs — Unified sidebar navigation with consistent links across team and project levels

• Improved Order — Reordered navigation items to prioritize the most common developer workflows

• Projects as Filters — Switch between team and project versions of the same page in one click

• Optimized for Mobile — New mobile navigation featuring a floating bottom bar optimized for one-handed use

Try the new navigation today before it rolls out to all users.

Vercel Sandbox now supports filesystem snapshots to capture your state. You can capture a Sandbox's complete filesystem state as a snapshot and launch new Sandboxes from that snapshot using the Sandbox API.

This eliminates repeated setup when working with expensive operations like dependency installation, builds, or fixture creation. Create the environment once, snapshot it, then reuse that exact filesystem state across multiple isolated runs.

Link to headingHow snapshots work

Snapshots capture the entire filesystem of a running Sandbox. New Sandboxes can launch from that snapshot, providing immediate access to pre-installed dependencies and configured environments.

Link to headingKey capabilities

• Create a snapshot from any running Sandbox with sandbox.snapshot()

• Launch new Sandboxes from snapshots via source: { type: 'snapshot', snapshotId }

• Reuse the same snapshot with multiple Sandboxes for parallel testing and experimentation

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import { Sandbox } from '@vercel/sandbox';
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const sandbox = await Sandbox.create();
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await sandbox.writeFiles([{
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  path: '/vercel/sandbox/hello',
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  content: Buffer.from('Hello Vercel Sandbox and Snapshots'),
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}]);
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const snapshot = await sandbox.snapshot();
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const newSandbox = await Sandbox.create({
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  source: { type: 'snapshot', snapshotId: snapshot.snapshotId },
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});
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for await (const chunk of await newSandbox.readFile({ path: '/vercel/sandbox/hello' })) {
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  process.stdout.write(chunk);
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};

See the documentation to get started with snapshots.

You can now view the Cron Jobs from your application in the Deployment Summary section of Deployments.

Try it out by deploying a Vercel Cron Job template. Once you deploy, Vercel automatically registers your cron jobs. Learn more in the Cron Jobs documentation.

Today we're releasing a brand new set of components designed to help you build the next generation of IDEs, coding apps and background agents.

Link to heading<Agent />

A composable component for displaying an AI SDK ToolLoopAgent configuration with model, instructions, tools, and output schema.

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npx ai-elements add agent

Link to heading<CodeBlock />

Building on what we've learned from Streamdown, we massively improved the code block component with support for a header, icon, filename, multiple languages and a more performant renderer.

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npx ai-elements add code-block

Link to heading<Commit />

The Commit component displays commit details including hash, message, author, timestamp, and changed files.

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npx ai-elements add commit

Link to heading<EnvironmentVariables />

The EnvironmentVariables component displays environment variables with value masking, visibility toggle, and copy functionality.

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npx ai-elements add environment-variables

Link to heading<FileTree />

The FileTree component displays a hierarchical file system structure with expandable folders and file selection.

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npx ai-elements add file-tree

Link to heading<PackageInfo />

The PackageInfo component displays package dependency information including version changes and change type badges.

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npx ai-elements add package-info

Link to heading<Sandbox />

The Sandbox component provides a structured way to display AI-generated code alongside its execution output in chat conversations. It features a collapsible container with status indicators and tabbed navigation between code and output views.

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npx ai-elements add sandbox

Link to heading<SchemaDisplay />

The SchemaDisplay component visualizes REST API endpoints with HTTP methods, paths, parameters, and request/response schemas.

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npx ai-elements add schema-display

Link to heading<Snippet />

The Snippet component provides a lightweight way to display terminal commands and short code snippets with copy functionality. Built on top of shadcn/ui InputGroup, it's designed for brief code references in text.

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npx ai-elements add snippet

Link to heading<StackTrace />

The StackTrace component displays formatted JavaScript/Node.js error stack traces with clickable file paths, internal frame dimming, and collapsible content.

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npx ai-elements add stack-trace

Link to heading<Terminal />

The Terminal component displays console output with ANSI color support, streaming indicators, and auto-scroll functionality.

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npx ai-elements add terminal

Link to heading<TestResults />

The TestResults component displays test suite results (like Vitest) including summary statistics, progress, individual tests, and error details.

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npx ai-elements add test-results

Link to headingBonus: <Attachments />

Not code related, but since attachment were being used in Message, PromptInput and more, we broke it out into its own component - a flexible, composable attachment component for displaying files, images, videos, audio, and source documents.

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npx ai-elements add attachments