Web Development Tools to Keep an Eye on in 2025

11 minute read

Building modern web applications has never been easier, especially with the integration of Large Language Models (LLMs) and AI-driven agents. Below, we provide examples using code snippets for each of the top 25 open-source tools, showing how they can enhance your development workflow with LLM usage, AI agent development, and serverless deployments.

AI Agents

1. Composio

Description: Composio is a versatile framework for building AI-driven applications.

LLM-Focused Example: Create a serverless AI agent powered by a GPT-like model for conversational support.

import { Composio } from 'composio';

const app = new Composio();

// Register an LLM-based service
app.use('gptAgent', {
  model: 'gpt-3.5',
  generateReply: async (prompt) => {
    // This could be a call to your LLM provider
    return await app.llm.generate(prompt);
  },
});

// Define a route that uses the AI agent
app.route('/chat', async (req, res) => {
  const { userInput } = req.body;
  const response = await app.use('gptAgent').generateReply(userInput);
  res.json({ response });
});

// Deploy serverless or run locally
app.listen(3000, () => console.log('Composio LLM app running on port 3000'));

2. Vercel AI SDK

Description: Simplifies the deployment of AI models within web applications.

LLM-Focused Example: Deploy an LLM sentiment analysis function as a serverless endpoint in a Next.js app.

// pages/api/analyze-sentiment.js
import { createLLMClient } from 'vercel-ai-sdk';

const llmClient = createLLMClient({ model: 'distilbert-sentiment' });

export default async function handler(req, res) {
  const { text } = req.body;
  const result = await llmClient.predict({ text });
  res.status(200).json({ sentiment: result });
}

// pages/index.js
import { useState } from 'react';

export default function Home() {
  const [input, setInput] = useState('');
  const [sentiment, setSentiment] = useState(null);

  const handleAnalyze = async () => {
    const res = await fetch('/api/analyze-sentiment', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ text: input }),
    });
    const data = await res.json();
    setSentiment(data.sentiment);
  };

  return (
    <div>
      <textarea onChange={(e) => setInput(e.target.value)} />
      <button onClick={handleAnalyze}>Analyze</button>
      {sentiment && <p>Sentiment: {sentiment}</p>}
    </div>
  );
}

3. LangGraph JS

Description: A powerful library for creating and managing complex language models.

AI Agent Example: Build a multilingual chatbot agent that automatically determines which LLM (English, Spanish, French) to use based on user input.

import LangGraph from 'langgraph-js';

const chatbot = new LangGraph({
  languages: ['en', 'es', 'fr'],
  models: {
    en: 'gpt-3.5-en',
    es: 'gpt-3.5-es',
    fr: 'gpt-3.5-fr',
  },
});

chatbot.onMessage(async (msg) => {
  // Detect language and pick the right LLM
  const selectedModel = chatbot.detectLanguage(msg.text);
  return chatbot.generateResponse(msg.text, selectedModel);
});

// Hook into a serverless endpoint
export default async function handler(req, res) {
  const { text } = req.body;
  const response = await chatbot.handleMessage({ text });
  res.json({ response });
}

4. CopilotKit

Description: Enhances developer productivity with AI-driven code suggestions.

LLM-Focused Example: Integrate CopilotKit into a serverless VS Code extension that suggests improvements for your AI agent code.

// extension.js
const copilotKit = require('copilotkit');

copilotKit.initialize({ apiKey: 'YOUR_API_KEY' });

// Provide suggestions as code is typed
vscode.workspace.onDidChangeTextDocument(async (event) => {
  const code = event.document.getText();
  const suggestions = await copilotKit.getSuggestions({ code, language: 'javascript' });
  displaySuggestions(suggestions);
});

5. LanceDB

Description: A high-performance database optimized for storing and querying large-scale AI data.

LLM-Focused Example: Store LLM-generated embeddings for a recommendation engine and query similar items.

import LanceDB from 'lancedb';

const db = new LanceDB('llm-embeddings.db');

// Insert user embeddings from an LLM
await db.insert({
  userId: 'user123',
  embedding: [0.12, 0.78, 0.34, 0.45], // LLM-generated embedding
});

// Query for similar embeddings
const results = await db.querySimilar({
  embedding: [0.12, 0.78, 0.34, 0.45],
  topK: 5,
});

console.log(results);

CI/CD (Continuous Integration/Continuous Deployment)

6. Encore

Description: Streamlines the CI/CD process with declarative pipeline configuration.

AI Agent Pipeline Example: Automatically test LLM-based code and deploy if tests pass.

# encore-pipeline.yml
stages:
  - name: Test
    steps:
      - run: npm install
      - run: npm test
  - name: Deploy
    steps:
      - run: npm run build
      - run: encore deploy

environments:
  production:
    branches:
      - main

7. Turborepo

Description: A high-performance build system for JavaScript and TypeScript monorepos.

LLM-Focused Example: Set up Turborepo to cache and speed up repeated LLM fine-tuning tasks.

// turbo.json
{
  "pipeline": {
    "fine-tune": {
      "dependsOn": ["^fine-tune"],
      "outputs": ["models/**"]
    },
    "build": {
      "dependsOn": ["fine-tune"],
      "outputs": ["dist/**"]
    }
  }
}

# Run the pipeline
npx turbo run build

8. Vitest

Description: A blazing-fast unit testing framework for modern JavaScript applications.

LLM-Focused Example: Write tests to ensure your AI agent’s prompts and responses conform to requirements.

// aiAgent.test.js
import { describe, it, expect } from 'vitest';
import { generateResponse } from './aiAgent';

describe('LLM AI Agent', () => {
  it('should generate a valid response', async () => {
    const prompt = 'Hello, how can I help you?';
    const response = await generateResponse(prompt);
    expect(response).toMatch(/I'm here to help/);
  });
});

9. Jest

Description: A widely-used testing framework for JavaScript applications.

LLM-Focused Example: Test a serverless LLM function that classifies text inputs.

// classify.test.js
const request = require('supertest');
const app = require('./app');

test('POST /classify should return a classification label', async () => {
  const response = await request(app)
    .post('/classify')
    .send({ text: 'This is a test.' });
  expect(response.statusCode).toBe(200);
  expect(response.body).toHaveProperty('label');
});

Serverless and Frameworks

10. Deno 2

Description: A secure runtime for JavaScript and TypeScript, ideal for serverless applications.

Serverless Example: Deploy a Deno function that uses an LLM for text summarization.

// ai-summarizer.ts
import { serve } from "https://deno.land/std@0.195.0/http/server.ts";
import { summarize } from 'https://deno.land/x/llm_summarizer/mod.ts';

serve(async (req) => {
  const { text } = await req.json();
  const summary = await summarize(text);
  return new Response(JSON.stringify({ summary }), {
    headers: { "Content-Type": "application/json" },
  });
});

11. Serverless Framework

Description: Simplifies the deployment and management of serverless applications.

LLM-Focused Example: Deploy a Lambda function that translates user messages via a large language model.

# serverless.yml
service: ai-translation

provider:
  name: aws
  runtime: nodejs16.x

functions:
  translateMessage:
    handler: handler.translate
    events:
      - http:
          path: translate
          method: post

plugins:
  - serverless-offline

// handler.js
const { translateWithLLM } = require('./aiTranslation');

module.exports.translate = async (event) => {
  const { text, targetLang } = JSON.parse(event.body);
  const result = await translateWithLLM(text, targetLang);
  return {
    statusCode: 200,
    body: JSON.stringify({ translated: result }),
  };
};

12. Feather.js

Description: A lightweight framework for real-time applications and REST APIs.

LLM-Focused Example: Integrate an AI agent for real-time language detection and translation of incoming chat messages.

const feathers = require('@feathersjs/feathers');
const express = require('@feathersjs/express');
const socketio = require('@feathersjs/socketio');
const { detectAndTranslate } = require('./aiAgent');

const app = express(feathers());
app.configure(socketio());

app.use('/messages', {
  async create(data) {
    const translatedData = await detectAndTranslate(data.text);
    return { ...data, translated: translatedData };
  },
});

app.listen(3030, () => console.log('Feather.js LLM app running on port 3030'));

13. Deepstream.io

Description: A real-time data server for building high-performance applications.

LLM-Focused Example: Publish real-time AI agent insights (e.g., from a text-generation service) to connected clients.

const deepstream = require('deepstream.io-client-js');
const client = deepstream('ws://localhost:6020').login();

const insightsRecord = client.record.getRecord('ai/insights');

insightsRecord.subscribe((data) => {
  console.log('New AI insights:', data);
});

// On your serverless AI agent side:
async function publishInsights(text) {
  const generated = await generateLLMText(text);
  const record = client.record.getRecord('ai/insights');
  record.set({ text, generated });
}

14. Val Town

Description: A framework for building modern, serverless web applications.

Serverless LLM Example: Use a serverless function to auto-summarize incoming data for a data dashboard.

// valtown.config.js
module.exports = {
  functions: {
    autoSummarize: {
      handler: 'summarizer.handler',
      events: ['http'],
    },
  },
};

// summarizer.js
const { summarize } = require('./llmService');

exports.handler = async (event) => {
  const { text } = JSON.parse(event.body);
  const summary = await summarize(text);
  return {
    statusCode: 200,
    body: JSON.stringify({ summary }),
  };
};

15. Socket.io

Description: Enables real-time, bidirectional communication between web clients and servers.

LLM-Focused Example: Create a real-time LLM chat agent that broadcasts typed responses to all connected clients.

// server.js
const io = require('socket.io')(3000);
const { generateLLMResponse } = require('./aiService');

io.on('connection', (socket) => {
  console.log('Client connected');

  socket.on('userMessage', async (message) => {
    const response = await generateLLMResponse(message);
    // Broadcast the response
    io.emit('chatUpdate', { user: message, ai: response });
  });
});

16. Radix Themes

Description: Offers accessible and customizable UI components.

LLM-Focused Example: Build a data exploration dashboard that uses an LLM to answer natural-language queries about your data.

import { RadixButton, RadixCard } from 'radix-themes';
import { useState } from 'react';
import { queryDataWithLLM } from './aiDataService';

export default function DataDashboard() {
  const [query, setQuery] = useState('');
  const [answer, setAnswer] = useState(null);

  const handleQuery = async () => {
    const result = await queryDataWithLLM(query);
    setAnswer(result);
  };

  return (
    <RadixCard>
      <h2>Ask Your Data</h2>
      <textarea value={query} onChange={(e) => setQuery(e.target.value)} />
      <RadixButton onClick={handleQuery}>Ask</RadixButton>
      {answer && <p>Answer: {answer}</p>}
    </RadixCard>
  );
}

Testing and Quality Assurance

17. Playwright

Description: A powerful end-to-end testing framework supporting multiple browsers.

LLM-Focused Example: Test your AI chatbot UI to ensure it loads an LLM response within a given timeframe.

// chatbot.spec.js
const { test, expect } = require('@playwright/test');

test('AI chatbot returns LLM response', async ({ page }) => {
  await page.goto('https://yourapp.com/chat');
  await page.fill('#userMessage', 'Hello, AI');
  await page.click('#sendButton');

  // Wait up to 5 seconds for the LLM response
  const responseElement = page.locator('#aiResponse');
  await expect(responseElement).toHaveText(/Hello! How can I assist?/i, { timeout: 5000 });
});

18. Puppeteer

Description: Controls headless Chrome or Chromium browsers for tasks like web scraping and automated testing.

LLM-Focused Example: Scrape your own AI agent’s webpage to validate the content it generates for SEO or moderation.

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();
  await page.goto('https://yourapp.com/ai-content');

  // Extract the AI-generated text
  const content = await page.evaluate(() => {
    return document.querySelector('#aiGenerated').textContent;
  });

  console.log('AI Content:', content);

  await browser.close();
})();

19. Prettier

Description: An opinionated code formatter ensuring consistent code style.

LLM-Focused Example: Automatically format your AI agent’s code (e.g., prompt engineering scripts) before commit.

// .prettierrc
{
  "semi": true,
  "singleQuote": true,
  "trailingComma": "es5"
}

# Add a pre-commit hook using Husky
npx husky-init && npm install
npx husky set .husky/pre-commit "npx prettier --write ."

UI/UX and Styling

20. Shadcn-UI

Description: A collection of accessible and customizable UI components.

LLM-Focused Example: Build an LLM-based Q&A interface where users can submit questions and receive AI-generated answers.

import { Button, Card, Input } from 'shadcn-ui';
import { useState } from 'react';
import { askAI } from './aiService';

export default function QAInterface() {
  const [question, setQuestion] = useState('');
  const [answer, setAnswer] = useState('');

  const handleAsk = async () => {
    const response = await askAI(question);
    setAnswer(response);
  };

  return (
    <Card>
      <Input
        value={question}
        onChange={(e) => setQuestion(e.target.value)}
        placeholder="Ask a question..."
      />
      <Button onClick={handleAsk}>Ask AI</Button>
      {answer && <div>{answer}</div>}
    </Card>
  );
}

21. Daisy UI

Description: A lightweight, customizable UI library integrating with Tailwind CSS.

LLM-Focused Example: Create a sleek chatbot interface that is styled with Daisy UI and powered by an LLM.

import 'daisyui/dist/full.css';
import { useState } from 'react';
import { getAIResponse } from './aiChatService';

export default function Chatbot() {
  const [input, setInput] = useState('');
  const [chatLog, setChatLog] = useState([]);

  const handleSend = async () => {
    const aiReply = await getAIResponse(input);
    setChatLog([...chatLog, { user: input, ai: aiReply }]);
    setInput('');
  };

  return (
    <div className="chatbot-container p-4">
      <div className="chat-window bg-base-200 p-4 rounded">
        {chatLog.map((msg, i) => (
          <div key={i}>
            <p className="text-accent">{msg.user}</p>
            <p className="text-secondary">{msg.ai}</p>
          </div>
        ))}
      </div>
      <input
        className="input input-bordered w-full mt-2"
        value={input}
        onChange={(e) => setInput(e.target.value)}
      />
      <button className="btn btn-primary mt-2" onClick={handleSend}>
        Send
      </button>
    </div>
  );
}

22. Vanilla Extract

Description: A zero-runtime CSS-in-TypeScript library for type-safe styles.

LLM-Focused Example: Style an AI response component that highlights important LLM outputs.

// styles.css.ts
import { style } from '@vanilla-extract/css';

export const container = style({
  padding: '16px',
  borderRadius: '4px',
  backgroundColor: '#f9fafb',
});

export const aiHighlight = style({
  color: '#3b82f6',
  fontWeight: 'bold',
});

// AIOutput.tsx
import React from 'react';
import * as styles from './styles.css';

export default function AIOutput({ text }) {
  return (
    <div className={styles.container}>
      <span className={styles.aiHighlight}>{text}</span>
    </div>
  );
}

23. Ark UI

Description: Modular and reusable UI components designed for scalability and flexibility.

LLM-Focused Example: Implement an analytics dashboard that uses an LLM to generate natural-language summaries of user data.

import { ArkCard, ArkChart, ArkButton } from 'ark-ui';
import { useState, useEffect } from 'react';
import { getData, summarizeData } from './aiAnalyticsService';

export default function AnalyticsDashboard() {
  const [data, setData] = useState(null);
  const [summary, setSummary] = useState('');

  useEffect(() => {
    async function loadData() {
      const rawData = await getData();
      setData(rawData);
    }
    loadData();
  }, []);

  const handleSummarize = async () => {
    if (data) {
      const result = await summarizeData(data);
      setSummary(result);
    }
  };

  return (
    <ArkCard>
      <h2>AI Data Analytics</h2>
      {data ? <ArkChart data={data} /> : <p>Loading data...</p>}
      <ArkButton onClick={handleSummarize}>Summarize</ArkButton>
      {summary && <p>Summary: {summary}</p>}
    </ArkCard>
  );
}

Additional Tools

24. HTMX

Description: Empowers developers to create dynamic, interactive web applications with minimal JavaScript.

LLM-Focused Example: Add an autocomplete feature powered by an LLM to suggest relevant queries in real-time.

<!-- index.html -->
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>AI Autocomplete with HTMX</title>
  <script src="https://unpkg.com/htmx.org@1.7.0"></script>
</head>
<body>
  <form hx-post="/autocomplete" hx-trigger="keyup changed delay:500ms" hx-target="#suggestions" hx-swap="innerHTML">
    <input type="text" name="query" placeholder="Search..." autocomplete="off">
  </form>
  <div id="suggestions"></div>
</body>
</html>

// server.js (Node.js with Express)
const express = require('express');
const { llmSuggest } = require('./aiAutocompleteService');

const app = express();
app.use(express.urlencoded({ extended: true }));
app.use(express.json());

app.post('/autocomplete', async (req, res) => {
  const query = req.body.query || req.body['query']; // handle form data
  const suggestions = await llmSuggest(query);
  const htmlSuggestions = suggestions.map((s) => `<div>${s}</div>`).join('');
  res.send(htmlSuggestions);
});

app.listen(4000, () => console.log('HTMX + LLM server running on port 4000'));

25. RabbitMQ

Description: A reliable messaging broker for communication between different parts of your application.

LLM-Focused Example: Offload large LLM inference tasks to a worker queue for asynchronous processing.

// producer.js
const amqp = require('amqplib');

async function enqueueLLMTask(prompt) {
  const connection = await amqp.connect('amqp://localhost');
  const channel = await connection.createChannel();
  const queue = 'llm_tasks';

  await channel.assertQueue(queue, { durable: true });
  channel.sendToQueue(queue, Buffer.from(JSON.stringify({ prompt })), { persistent: true });

  console.log('LLM Task queued:', prompt);
  await channel.close();
  await connection.close();
}

enqueueLLMTask('Explain quantum computing in simple terms.');

// consumer.js
const amqp = require('amqplib');
const { generateLLMResponse } = require('./aiLLMService');

async function consumeLLMTasks() {
  const connection = await amqp.connect('amqp://localhost');
  const channel = await connection.createChannel();
  const queue = 'llm_tasks';

  await channel.assertQueue(queue, { durable: true });
  channel.prefetch(1);
  console.log('Waiting for LLM tasks...');

  channel.consume(queue, async (msg) => {
    if (msg !== null) {
      const { prompt } = JSON.parse(msg.content.toString());
      console.log('Received LLM task:', prompt);

      // Perform AI inference
      const result = await generateLLMResponse(prompt);
      console.log('LLM Result:', result);

      channel.ack(msg);
    }
  });
}

consumeLLMTasks();

Conclusion

In 2025, the integration of LLMs and AI-driven agents will continue to revolutionize web development. By leveraging these 25 open-source tools—with examples tailored for LLM usage, AI agent creation, and serverless deployments—you can build intelligent, scalable, and future-proof applications that truly stand out.

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Jay Singh