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ai-elements

Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.

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价格未确认★ 69 GitHub Stars目录更新于 · 2026年9月5日agent-skill

概览

Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

AI Elements

AI Elements is a component library and custom registry built on top of shadcn/ui to help you build AI-native applications faster. It provides pre-built components like conversations, messages and more.

Installing AI Elements is straightforward and can be done in a couple of ways. You can use the dedicated CLI command for the fastest setup, or integrate via the standard shadcn/ui CLI if you've already adopted shadcn's workflow.

IMPORTANT: Run all CLI commands using the project's package runner: npx ai-elements@latest, pnpm dlx ai-elements@latest, or bunx --bun ai-elements@latest — based on the project's packageManager. Examples below use npx ai-elements@latest but substitute the correct runner for the project.

Prerequisites

Before installing AI Elements, make sure your environment meets the following requirements:

  • Node.js, version 18 or later
  • A Next.js project with the AI SDK installed.
  • shadcn/ui installed in your project. If you don't have it installed, running any install command will automatically install it for you.
  • We also highly recommend using the AI Gateway and adding AI_GATEWAY_API_KEY to your env.local so you don't have to use an API key from every provider. AI Gateway also gives $5 in usage per month so you can experiment with models. You can obtain an API key here.

Installing Components

You can install AI Elements components using either the AI Elements CLI or the shadcn/ui CLI. Both achieve the same result: adding the selected component’s code and any needed dependencies to your project.

The CLI will download the component’s code and integrate it into your project’s directory (usually under your components folder). By default, AI Elements components are added to the @/components/ai-elements/ directory (or whatever folder you’ve configured in your shadcn components settings).

After running the command, you should see a confirmation in your terminal that the files were added. You can then proceed to use the component in your code.

Usage

Once an AI Elements component is installed, you can import it and use it in your application like any other React component. The components are added as part of your codebase (not hidden in a library), so the usage feels very natural.

Example

After installing AI Elements components, you can use them in your application like any other React component. For example:

"use client";

import {
  Message,
  MessageContent,
  MessageResponse,
} from "@/components/ai-elements/message";
import { useChat } from "@ai-sdk/react";

const Example = () => {
  const { messages } = useChat();

  return (
    <>
      {messages.map(({ role, parts }, index) => (
        <Message from={role} key={index}>
          <MessageContent>
            {parts.map((part, i) => {
              switch (part.type) {
                case "text":
                  return (
                    <MessageResponse key={`${role}-${i}`}>
                      {part.text}
                    </MessageResponse>
                  );
              }
            })}
          </MessageContent>
        </Message>
      ))}
    </>
  );
};

export default Example;

In the example above, we import the Message component from our AI Elements directory and include it in our JSX. Then, we compose the component with the MessageContent and MessageResponse subcomponents. You can style or configure the component just as you would if you wrote it yourself – since the code lives in your project, you can even open the component file to see how it works or make custom modifications.

Extensibility

All AI Elements components take as many primitive attributes as possible. For example, the Message component extends HTMLAttributes<HTMLDivElement>, so you can pass any props that a div supports. This makes it easy to extend the component with your own styles or functionality.

Customization

After installation, no additional setup is needed. The component’s styles (Tailwind CSS classes) and scripts are already integrated. You can start interacting with the component in your app immediately.

For example, if you'd like to remove the rounding on Message, you can go to components/ai-elements/message.tsx and remove rounded-lg as follows:

export const MessageContent = ({
  children,
  className,
  ...props
}: MessageContentProps) => (
  <div
    className={cn(
      "flex flex-col gap-2 text-sm text-foreground",
      "group-[.is-user]:bg-primary group-[.is-user]:text-primary-foreground group-[.is-user]:px-4 group-[.is-user]:py-3",
      className
    )}
    {...props}
  >
    <div className="is-user:dark">{children}</div>
  </div>
);

Troubleshooting

Why are my components not styled?

Make sure your project is configured correctly for shadcn/ui in Tailwind 4 - this means having a globals.css file that imports Tailwind and includes the shadcn/ui base styles.

I ran the AI Elements CLI but nothing was added to my project

Double-check that:

  • Your current working directory is the root of your project (where package.json lives).
  • Your components.json file (if using shadcn-style config) is set up correctly.
  • You’re using the latest version of the AI Elements CLI:
npx ai-elements@latest

If all else fails, feel free to open an issue on GitHub.

Theme switching doesn’t work — my app stays in light mode

Ensure your app is using the same data-theme system that shadcn/ui and AI Elements expect. The default implementation toggles a data-theme attribute on the <html> element. Make sure your tailwind.config.js is using class or data- selectors accordingly.

The component imports fail with “module not found”

Check the file exists. If it does, make sure your tsconfig.json has a proper paths alias for @/ i.e.

{
  "compilerOptions": {
    "baseUrl": ".",
    "paths": {
      "@/*": ["./*"]
    }
  }
}
My AI coding assistant can't access AI Elements components
  1. Verify your config file syntax is valid JSON.
  2. Check that the file path is correct for your AI tool.
  3. Restart your coding assistant after making changes.
  4. Ensure you have a stable internet connection.
Still stuck?

If none of these answers help, open an issue on GitHub and someone will be happy to assist.

Available Components

See the references/ folder for detailed documentation on each component.

查看来源 ↗
文件元数据
name: ai-elements
description: Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.
查看原始文本
---
name: ai-elements
description: Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.
---

# AI Elements

[AI Elements](https://www.npmjs.com/package/ai-elements) is a component library and custom registry built on top of [shadcn/ui](https://ui.shadcn.com/) to help you build AI-native applications faster. It provides pre-built components like conversations, messages and more.

Installing AI Elements is straightforward and can be done in a couple of ways. You can use the dedicated CLI command for the fastest setup, or integrate via the standard shadcn/ui CLI if you've already adopted shadcn's workflow.

> **IMPORTANT:** Run all CLI commands using the project's package runner: `npx ai-elements@latest`, `pnpm dlx ai-elements@latest`, or `bunx --bun ai-elements@latest` — based on the project's `packageManager`. Examples below use `npx ai-elements@latest` but substitute the correct runner for the project.

## Prerequisites

Before installing AI Elements, make sure your environment meets the following requirements:

- [Node.js](https://nodejs.org/en/download/), version 18 or later
- A [Next.js](https://nextjs.org/) project with the [AI SDK](https://ai-sdk.dev/) installed.
- [shadcn/ui](https://ui.shadcn.com/) installed in your project. If you don't have it installed, running any install command will automatically install it for you.
- We also highly recommend using the [AI Gateway](https://vercel.com/docs/ai-gateway) and adding `AI_GATEWAY_API_KEY` to your `env.local` so you don't have to use an API key from every provider. AI Gateway also gives $5 in usage per month so you can experiment with models. You can obtain an API key [here](https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai%2Fapi-keys&title=Get%20your%20AI%20Gateway%20key).

## Installing Components

You can install AI Elements components using either the AI Elements CLI or the shadcn/ui CLI. Both achieve the same result: adding the selected component’s code and any needed dependencies to your project.

The CLI will download the component’s code and integrate it into your project’s directory (usually under your components folder). By default, AI Elements components are added to the `@/components/ai-elements/` directory (or whatever folder you’ve configured in your shadcn components settings).

After running the command, you should see a confirmation in your terminal that the files were added. You can then proceed to use the component in your code.

## Usage

Once an AI Elements component is installed, you can import it and use it in your application like any other React component. The components are added as part of your codebase (not hidden in a library), so the usage feels very natural.

## Example

After installing AI Elements components, you can use them in your application like any other React component. For example:

```tsx title="conversation.tsx"
"use client";

import {
  Message,
  MessageContent,
  MessageResponse,
} from "@/components/ai-elements/message";
import { useChat } from "@ai-sdk/react";

const Example = () => {
  const { messages } = useChat();

  return (
    <>
      {messages.map(({ role, parts }, index) => (
        <Message from={role} key={index}>
          <MessageContent>
            {parts.map((part, i) => {
              switch (part.type) {
                case "text":
                  return (
                    <MessageResponse key={`${role}-${i}`}>
                      {part.text}
                    </MessageResponse>
                  );
              }
            })}
          </MessageContent>
        </Message>
      ))}
    </>
  );
};

export default Example;
```

In the example above, we import the `Message` component from our AI Elements directory and include it in our JSX. Then, we compose the component with the `MessageContent` and `MessageResponse` subcomponents. You can style or configure the component just as you would if you wrote it yourself – since the code lives in your project, you can even open the component file to see how it works or make custom modifications.

## Extensibility

All AI Elements components take as many primitive attributes as possible. For example, the `Message` component extends `HTMLAttributes<HTMLDivElement>`, so you can pass any props that a `div` supports. This makes it easy to extend the component with your own styles or functionality.

## Customization

After installation, no additional setup is needed. The component’s styles (Tailwind CSS classes) and scripts are already integrated. You can start interacting with the component in your app immediately.

For example, if you'd like to remove the rounding on `Message`, you can go to `components/ai-elements/message.tsx` and remove `rounded-lg` as follows:

```tsx title="components/ai-elements/message.tsx" highlight="8"
export const MessageContent = ({
  children,
  className,
  ...props
}: MessageContentProps) => (
  <div
    className={cn(
      "flex flex-col gap-2 text-sm text-foreground",
      "group-[.is-user]:bg-primary group-[.is-user]:text-primary-foreground group-[.is-user]:px-4 group-[.is-user]:py-3",
      className
    )}
    {...props}
  >
    <div className="is-user:dark">{children}</div>
  </div>
);
```

## Troubleshooting

### Why are my components not styled?

Make sure your project is configured correctly for shadcn/ui in Tailwind 4 - this means having a `globals.css` file that imports Tailwind and includes the shadcn/ui base styles.

### I ran the AI Elements CLI but nothing was added to my project

Double-check that:

- Your current working directory is the root of your project (where `package.json` lives).
- Your components.json file (if using shadcn-style config) is set up correctly.
- You’re using the latest version of the AI Elements CLI:

```bash title="Terminal"
npx ai-elements@latest
```

If all else fails, feel free to open an [issue on GitHub](https://github.com/vercel/ai-elements/issues).

### Theme switching doesn’t work — my app stays in light mode

Ensure your app is using the same data-theme system that shadcn/ui and AI Elements expect. The default implementation toggles a data-theme attribute on the `<html>` element. Make sure your tailwind.config.js is using class or data- selectors accordingly.

### The component imports fail with “module not found”

Check the file exists. If it does, make sure your `tsconfig.json` has a proper paths alias for `@/` i.e.

```json title="tsconfig.json"
{
  "compilerOptions": {
    "baseUrl": ".",
    "paths": {
      "@/*": ["./*"]
    }
  }
}
```

### My AI coding assistant can't access AI Elements components

1. Verify your config file syntax is valid JSON.
2. Check that the file path is correct for your AI tool.
3. Restart your coding assistant after making changes.
4. Ensure you have a stable internet connection.

### Still stuck?

If none of these answers help, open an [issue on GitHub](https://github.com/vercel/ai-elements/issues) and someone will be happy to assist.

## Available Components

See the `references/` folder for detailed documentation on each component.

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许可证: Apache-2.0

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • SKILL.md does not explicitly list limitations or safe operating boundaries (e.g., when not to use this skill, potential pitfalls with component customization).
  • The SKILL.md excerpt appears truncated; ensure the full document is present and includes all sections (e.g., troubleshooting, version compatibility).
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 69 GitHub stars
  • Stars/forks activity: 69 stars, 13 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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从一个小任务开始

  1. 1阅读来源，确认输入、预期输出、依赖和权限。
  2. 2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
  3. 3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

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来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库
oomol-lab/wanta
许可证
Apache-2.0
版本
1.0.0
最近 GitHub 推送
2026年9月5日
目录更新于
2026年9月5日
技能指令路径
.claude/skills/ai-elements/SKILL.md @ fcb31946010d

版本来自目录元数据，使用前请核实来源发布记录。

质量

62/100

有潜力

信任

54/100

Do not auto-install

审计

70/100

需审查

打开完整审计查看评估报告
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • SKILL.md does not explicitly list limitations or safe operating boundaries (e.g., when not to use this skill, potential pitfalls with component customization).
  • The SKILL.md excerpt appears truncated; ensure the full document is present and includes all sections (e.g., troubleshooting, version compatibility).
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • GitHub adoption: 69 GitHub stars
  • Stars/forks activity: 69 stars, 13 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 70,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "SKILL.md does not explicitly list limitations or safe operating boundaries (e.g., when not to use this skill, potential pitfalls with component customization).",
      "The SKILL.md excerpt appears truncated; ensure the full document is present and includes all sections (e.g., troubleshooting, version compatibility).",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 69 GitHub stars",
      "Stars/forks activity: 69 stars, 13 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 62,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "SKILL.md does not explicitly list limitations or safe operating boundaries (e.g., when not to use this skill, potential pitfalls with component customization).",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The SKILL.md excerpt appears truncated; ensure the full document is present and includes all sections (e.g., troubleshooting, version compatibility).",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use ai-elements in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 62/100 Manual review",
      "Audit: 70/100 Needs review",
      "Safety: 30/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "oomol-lab-ai-elements (ai-elements)",
      "install_command": "npx skills add oomol-lab/wanta --skill ai-elements",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "oomol-lab-ai-elements",
      "task": "Use ai-elements in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/oomol-lab-ai-elements",
    "api": "https://www.openagentskill.com/api/agent/skills/oomol-lab-ai-elements",
    "audit": "https://www.openagentskill.com/skills/oomol-lab-ai-elements/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=oomol-lab-ai-elements&task=Use%20ai-elements%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-elements%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ai-elements%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/oomol-lab-ai-elements/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/oomol-lab-ai-elements"
  }
}

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