x

Craft AI-driven interfaces effortlessly 🤖

Stars: 2202

Visit

Ant Design X is a tool for crafting AI-driven interfaces effortlessly. It is built on the best practices of enterprise-level AI products, offering flexible and diverse atomic components for various AI dialogue scenarios. The tool provides out-of-the-box model integration with inference services compatible with OpenAI standards. It also enables efficient management of conversation data flows, supports rich template options, complete TypeScript support, and advanced theme customization. Ant Design X is designed to enhance development efficiency and deliver exceptional AI interaction experiences.

README:

Ant Design X

Craft AI-driven interfaces effortlessly.

Changelog · Report Bug · Request Feature · English · 中文

✨ Features

🌈 Derived from Best Practices of Enterprise-Level AI Products: Built on the RICH interaction paradigm, delivering an exceptional AI interaction experience.
🧩 Flexible and Diverse Atomic Components: Covers most AI dialogue scenarios, empowering you to quickly build personalized AI interaction interfaces.
⚡ Out-of-the-Box Model Integration: Easily connect with inference services compatible with OpenAI standards.
🔄 Efficient Management of Conversation Data Flows: Provides powerful tools for managing data flows, enhancing development efficiency.
📦 Rich Template Support: Offers multiple templates for quickly starting LUI application development.
🛡 Complete TypeScript Support: Developed with TypeScript, ensuring robust type coverage to improve the development experience and reliability.
🎨 Advanced Theme Customization: Supports fine-grained style adjustments to meet diverse use cases and personalization needs.

📦 Installation

npm install @ant-design/x --save

yarn add @ant-design/x

pnpm add @ant-design/x

🖥️ Import in Browser

Add script and link tags in your browser and use the global variable antd.

We provide antdx.js, antdx.min.js, and antdx.min.js.map in the dist directory of the npm package.

🧩 Atomic Components

Based on the RICH interaction paradigm, we provide numerous atomic components for various stages of interaction to help you flexibly build your AI dialogue applications:

Below is an example of using atomic components to create a simple chatbot interface:

import React from 'react';
import {
  // Message bubble
  Bubble,
  // Input box
  Sender,
} from '@ant-design/x';

const messages = [
  {
    content: 'Hello, Ant Design X!',
    role: 'user',
  },
];

const App = () => (
  <>
    <Bubble.List items={messages} />
    <Sender />
  </>
);

export default App;

⚡️ Integrating Model Inference Service

We help you integrate standard model inference services out of the box by providing runtime tools like useXAgent, XRequest, etc.

Here is an example of integrating Qwen:

Note: 🔥 dangerouslyApiKey has security risks, more details can be found in the documentation.

import { useXAgent, Sender, XRequest } from '@ant-design/x';
import React from 'react';

const { create } = XRequest({
  baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1',
  dangerouslyApiKey: process.env['DASHSCOPE_API_KEY'],
  model: 'qwen-plus',
});

const Component: React.FC = () => {
  const [agent] = useXAgent({
    request: async (info, callbacks) => {
      const { messages, message } = info;
      const { onUpdate } = callbacks;

      // current message
      console.log('message', message);
      // messages list
      console.log('messages', messages);

      let content: string = '';

      try {
        create(
          {
            messages: [{ role: 'user', content: message }],
            stream: true,
          },
          {
            onSuccess: (chunks) => {
              console.log('sse chunk list', chunks);
            },
            onError: (error) => {
              console.log('error', error);
            },
            onUpdate: (chunk) => {
              console.log('sse object', chunk);
              const data = JSON.parse(chunk.data);
              content += data?.choices[0].delta.content;
              onUpdate(content);
            },
          },
        );
      } catch (error) {
        // handle error
      }
    },
  });

  const onSubmit = (message: string) => {
    agent.request(
      { message },
      {
        onUpdate: () => {},
        onSuccess: () => {},
        onError: () => {},
      },
    );
  };

  return <Sender onSubmit={onSubmit} />;
};

🔄 Efficient Data Flow Management

We help you efficiently manage the data flow of AI chat applications out of the box by providing the useXChat runtime tool:

Here is an example of integrating OpenAI:

import { useXAgent, useXChat, Sender, Bubble } from '@ant-design/x';
import OpenAI from 'openai';
import React from 'react';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'],
  dangerouslyAllowBrowser: true,
});

const Demo: React.FC = () => {
  const [agent] = useXAgent({
    request: async (info, callbacks) => {
      const { messages, message } = info;

      const { onSuccess, onUpdate, onError } = callbacks;

      // current message
      console.log('message', message);

      // history messages
      console.log('messages', messages);

      let content: string = '';

      try {
        const stream = await client.chat.completions.create({
          model: 'gpt-4o',
          // if chat context is needed, modify the array
          messages: [{ role: 'user', content: message }],
          // stream mode
          stream: true,
        });

        for await (const chunk of stream) {
          content += chunk.choices[0]?.delta?.content || '';
          onUpdate(content);
        }

        onSuccess(content);
      } catch (error) {
        // handle error
        // onError();
      }
    },
  });

  const {
    // use to send message
    onRequest,
    // use to render messages
    messages,
  } = useXChat({ agent });

  const items = messages.map(({ message, id }) => ({
    // key is required, used to identify the message
    key: id,
    content: message,
  }));

  return (
    <>
      <Bubble.List items={items} />
      <Sender onSubmit={onRequest} />
    </>
  );
};

export default Demo;

Use modularized antd

@ant-design/x supports ES modules tree shaking by default.

TypeScript

@ant-design/x provides a built-in ts definition.

Non-React Implementations

Welcome to contribute!

Companies using antdx

Ant Design X is widely used in AI-driven user interfaces within Ant Group. If your company and products use Ant Design X, feel free to leave a comment here.

Contributing

Please read our CONTRIBUTING.md first.

If you'd like to help us improve antd, just create a Pull Request. Feel free to report bugs and issues here.

If you're new to posting issues, we ask that you read How To Ask Questions The Smart Way and How to Ask a Question in Open Source Community and How to Report Bugs Effectively prior to posting. Well written bug reports help us help you!

Need Help?

If you encounter any issues while using Ant Design X, you can seek help through the following channels. We also encourage experienced users to assist newcomers via these platforms.

When asking questions on GitHub Discussions, it's recommended to use the Q&A tag.

For Tasks:

Click tags to check more tools for each tasks

build ai dialogue integrate model services manage data flows customize themes create personalized interfaces

For Jobs:

ui/ux designer frontend developer ai engineer product manager data scientist

Alternative AI tools for x

Similar Open Source Tools

x

github

: 2.2k

CopilotKit

CopilotKit is an open-source framework for building, deploying, and operating fully custom AI Copilots, including in-app AI chatbots, AI agents, and AI Textareas. It provides a set of components and entry points that allow developers to easily integrate AI capabilities into their applications. CopilotKit is designed to be flexible and extensible, so developers can tailor it to their specific needs. It supports a variety of use cases, including providing app-aware AI chatbots that can interact with the application state and take action, drop-in replacements for textareas with AI-assisted text generation, and in-app agents that can access real-time application context and take action within the application.

github

: 16.2k

chrome-ai

Chrome AI is a Vercel AI provider for Chrome's built-in model (Gemini Nano). It allows users to create language models using Chrome's AI capabilities. The tool is under development and may contain errors and frequent changes. Users can install the ChromeAI provider module and use it to generate text, stream text, and generate objects. To enable AI in Chrome, users need to have Chrome version 127 or greater and turn on specific flags. The tool is designed for developers and researchers interested in experimenting with Chrome's built-in AI features.

github

: 245

DB-GPT

DB-GPT is a personal database administrator that can solve database problems by reading documents, using various tools, and writing analysis reports. It is currently undergoing an upgrade. **Features:** * **Online Demo:** * Import documents into the knowledge base * Utilize the knowledge base for well-founded Q&A and diagnosis analysis of abnormal alarms * Send feedbacks to refine the intermediate diagnosis results * Edit the diagnosis result * Browse all historical diagnosis results, used metrics, and detailed diagnosis processes * **Language Support:** * English (default) * Chinese (add "language: zh" in config.yaml) * **New Frontend:** * Knowledgebase + Chat Q&A + Diagnosis + Report Replay * **Extreme Speed Version for localized llms:** * 4-bit quantized LLM (reducing inference time by 1/3) * vllm for fast inference (qwen) * Tiny LLM * **Multi-path extraction of document knowledge:** * Vector database (ChromaDB) * RESTful Search Engine (Elasticsearch) * **Expert prompt generation using document knowledge** * **Upgrade the LLM-based diagnosis mechanism:** * Task Dispatching -> Concurrent Diagnosis -> Cross Review -> Report Generation * Synchronous Concurrency Mechanism during LLM inference * **Support monitoring and optimization tools in multiple levels:** * Monitoring metrics (Prometheus) * Flame graph in code level * Diagnosis knowledge retrieval (dbmind) * Logical query transformations (Calcite) * Index optimization algorithms (for PostgreSQL) * Physical operator hints (for PostgreSQL) * Backup and Point-in-time Recovery (Pigsty) * **Continuously updated papers and experimental reports** This project is constantly evolving with new features. Don't forget to star ⭐ and watch 👀 to stay up to date.

github

: 480

educhain

Educhain is a powerful Python package that leverages Generative AI to create engaging and personalized educational content. It enables users to generate multiple-choice questions, create lesson plans, and support various LLM models. Users can export questions to JSON, PDF, and CSV formats, customize prompt templates, and generate questions from text, PDF, URL files, youtube videos, and images. Educhain outperforms traditional methods in content generation speed and quality. It offers advanced configuration options and has a roadmap for future enhancements, including integration with popular Learning Management Systems and a mobile app for content generation on-the-go.

github

: 157

evalscope

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

github

: 377

kan-gpt

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

github

: 663

openlrc

Open-Lyrics is a Python library that transcribes voice files using faster-whisper and translates/polishes the resulting text into `.lrc` files in the desired language using LLM, e.g. OpenAI-GPT, Anthropic-Claude. It offers well preprocessed audio to reduce hallucination and context-aware translation to improve translation quality. Users can install the library from PyPI or GitHub and follow the installation steps to set up the environment. The tool supports GUI usage and provides Python code examples for transcription and translation tasks. It also includes features like utilizing context and glossary for translation enhancement, pricing information for different models, and a list of todo tasks for future improvements.

github

: 476

libllm

libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

github

: 137

rig

Rig is a Rust library designed for building scalable, modular, and user-friendly applications powered by large language models (LLMs). It provides full support for LLM completion and embedding workflows, offers simple yet powerful abstractions for LLM providers like OpenAI and Cohere, as well as vector stores such as MongoDB and in-memory storage. With Rig, users can easily integrate LLMs into their applications with minimal boilerplate code.

github

: 2.6k

MarkLLM

MarkLLM is an open-source toolkit designed for watermarking technologies within large language models (LLMs). It simplifies access, understanding, and assessment of watermarking technologies, supporting various algorithms, visualization tools, and evaluation modules. The toolkit aids researchers and the community in ensuring the authenticity and origin of machine-generated text.

github

: 326

client

Gemini PHP is a PHP API client for interacting with the Gemini AI API. It allows users to generate content, chat, count tokens, configure models, embed resources, list models, get model information, troubleshoot timeouts, and test API responses. The client supports various features such as text-only input, text-and-image input, multi-turn conversations, streaming content generation, token counting, model configuration, and embedding techniques. Users can interact with Gemini's API to perform tasks related to natural language generation and text analysis.

github

: 198

obsei

Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

github

: 1.2k

TempCompass

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

github

: 71

ax

Ax is a Typescript library that allows users to build intelligent agents inspired by agentic workflows and the Stanford DSP paper. It seamlessly integrates with multiple Large Language Models (LLMs) and VectorDBs to create RAG pipelines or collaborative agents capable of solving complex problems. The library offers advanced features such as streaming validation, multi-modal DSP, and automatic prompt tuning using optimizers. Users can easily convert documents of any format to text, perform smart chunking, embedding, and querying, and ensure output validation while streaming. Ax is production-ready, written in Typescript, and has zero dependencies.

github

: 1.3k

evalplus

EvalPlus is a rigorous evaluation framework for LLM4Code, providing HumanEval+ and MBPP+ tests to evaluate large language models on code generation tasks. It offers precise evaluation and ranking, coding rigorousness analysis, and pre-generated code samples. Users can use EvalPlus to generate code solutions, post-process code, and evaluate code quality. The tool includes tools for code generation and test input generation using various backends.

github

: 1.3k

For similar tasks

x

github

: 2.2k

Noi

Noi is an AI-enhanced customizable browser designed to streamline digital experiences. It includes curated AI websites, allows adding any URL, offers prompts management, Noi Ask for batch messaging, various themes, Noi Cache Mode for quick link access, cookie data isolation, and more. Users can explore, extend, and empower their browsing experience with Noi.

github

: 6.0k

svelte-commerce

Svelte Commerce is an open-source frontend for eCommerce, utilizing a PWA and headless approach with a modern JS stack. It supports integration with various eCommerce backends like MedusaJS, Woocommerce, Bigcommerce, and Shopify. The API flexibility allows seamless connection with third-party tools such as payment gateways, POS systems, and AI services. Svelte Commerce offers essential eCommerce features, is both SSR and SPA, superfast, and free to download and modify. Users can easily deploy it on Netlify or Vercel with zero configuration. The tool provides features like headless commerce, authentication, cart & checkout, TailwindCSS styling, server-side rendering, proxy + API integration, animations, lazy loading, search functionality, faceted filters, and more.

github

: 1.5k

pro-react-admin

Pro React Admin is a comprehensive React admin template that includes features such as theme switching, custom component theming, nested routing, webpack optimization, TypeScript support, multi-tabs, internationalization, code styling, commit message configuration, error handling, code splitting, component documentation generation, and more. It also provides tools for mock server implementation, deployment, linting, formatting, and continuous code review. The template supports various technologies like React, React Router, Webpack, Babel, Ant Design, TypeScript, and Vite, making it suitable for building efficient and scalable React admin applications.

github

: 760

AI-GAL

AI-GAL is a tool that offers a visual GUI for easier configuration file editing, branch selection mode for content generation, and bug fixes. Users can configure settings in config.ini, utilize cloud-based AI drawing and voice modes, set themes for script generation, and enjoy a wallpaper. Prior to usage, ensure a 4GB+ GPU, chatgpt key or local LLM deployment, and installation of stable diffusion, gpt-sovits, and rembg. To start, fill out the config.ini file and run necessary APIs. Restart a storyline by clearing story.txt in the game directory. Encounter errors? Copy the log.txt details and send them for assistance.

github

: 93

katrain

KaTrain is a tool designed for analyzing games and playing go with AI feedback from KataGo. Users can review their games to find costly moves, play against AI with immediate feedback, play against weakened AI versions, and generate focused SGF reviews. The tool provides various features such as previews, tutorials, installation instructions, and configuration options for KataGo. Users can play against AI, receive instant feedback on moves, explore variations, and request in-depth analysis. KaTrain also supports distributed training for contributing to KataGo's strength and training bigger models. The tool offers themes customization, FAQ section, and opportunities for support and contribution through GitHub issues and Discord community.

github

: 1.6k

complexity

Complexity is a community-driven, open-source, and free third-party extension that enhances the features of Perplexity.ai. It provides various UI/UX/QoL tweaks, LLM/Image gen model selectors, a customizable theme, and a prompts library. The tool intercepts network traffic to alter the behavior of the host page, offering a solution to the limitations of Perplexity.ai. Users can install Complexity from Chrome Web Store, Mozilla Add-on, or build it from the source code.

github

: 533

AIaW

AIaW is a next-generation LLM client with full functionality, lightweight, and extensible. It supports various basic functions such as streaming transfer, image uploading, and latex formulas. The tool is cross-platform with a responsive interface design. It supports multiple service providers like OpenAI, Anthropic, and Google. Users can modify questions, regenerate in a forked manner, and visualize conversations in a tree structure. Additionally, it offers features like file parsing, video parsing, plugin system, assistant market, local storage with real-time cloud sync, and customizable interface themes. Users can create multiple workspaces, use dynamic prompt word variables, extend plugins, and benefit from detailed design elements like real-time content preview, optimized code pasting, and support for various file types.

github

: 320

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 595

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.2k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k