# How to Vibe Code Your Own LiveChat (and Stop Paying for It)

> The AI live chat software for business and e-commerce

- Site: https://livechat.com
- Category: Customer Support SaaS
- Platforms: Web app, macOS app, Windows app, iOS app, Android app
- Verdict: **Solid side project** (62/100 vibecodeable)
- Estimated effort: 2-3 weeks part-time

## Verdict

You can build a personal-use subset featuring the embeddable chat widget, real-time agent dashboard, and AI summarization, but you must sacrifice the massive 200+ app marketplace and complex enterprise routing rules.

Cloning LiveChat for personal use requires mastering real-time WebSocket communication and cross-origin widget injection. While an AI coding agent can scaffold the Next.js dashboard and Prisma database schema, managing connection state drops, reconnect logic, and real-time agent typing indicators requires careful debugging. Furthermore, injecting a secure chat iframe or shadow-DOM widget into third-party sites without breaking their CSS involves tedious cross-browser friction.

### What you can't replicate

- Deep native integrations with 200+ third-party e-commerce platforms
- Public market scale processing tens of millions of enterprise chats
- Advanced enterprise compliance frameworks like HIPAA and dedicated SLAs

## What it does

Digital customer service and sales platform featuring real-time website chat widgets, multichannel inbox aggregation, visitor tracking, and AI-driven agent assistance.

### Core features

- Embeddable JavaScript visitor chat widget
- Real-time bidirectional WebSocket message routing
- Multi-agent dashboard with presence and status tracking
- Visitor activity tracking and pre-chat surveys
- AI text intelligence for chat summaries and smart replies
- Basic ticket and chat history storage

## The business

### Pricing

- Starter: $19/mo
- Team: $49/mo
- Business: $79/mo
- Enterprise: Custom

Founded 2002.
Team size: ~268.

## The hard parts

- Maintaining low-latency WebSocket presence across thousands of active sessions without dropping messages
- Building a drop-in scriptable JS widget that safely loads and communicates across disparate customer e-commerce sites without CORS or DOM collision issues
- Real-time state synchronization between visitor sessions and multiple concurrent agent inboxes
- Integrating AI text models for real-time text enhancement and summarization under strict latency budgets

## How to vibe code LiveChat

### Prerequisites

- Node.js (Free): Required runtime environment for building and running the full-stack Next.js application.
- GitHub (Free): Source code repository and deployment pipeline integration.
- Cursor (Free tier): AI code editor used to build and iterate on the multi-file application structure.

### Recommended AI tools

- Cursor: Provides an AI-native code editor interface to review and tweak UI components and real-time hooks.
- Claude Code: Handles multi-file scaffolding and backend WebSocket event handlers directly from the terminal.

### Stack

- Frontend: Next.js with Tailwind CSS and shadcn/ui
- Backend: Next.js App Router API Routes and built-in WebSocket server
- Database: Neon (Serverless Postgres)
- Auth: better-auth
- Payments: None
- Other: Vercel AI SDK, Anthropic API

### Hosting

- Vercel (Hosts the Next.js frontend and dashboard application with zero-config preview deployments.): $0/mo
- Neon (Provides serverless Postgres storage for messages, customer sessions, and agent user records.): $0/mo

### Build guide

1. **Database Schema and Authentication Setup** — Initialize the Next.js project with Tailwind CSS, configure better-auth for agent logins, and set up Prisma or Drizzle ORM models for Users, Visitors, Chats, and Messages.

```
Create a new Next.js project using the App Router, TypeScript, and Tailwind CSS. Configure better-auth with email/password authentication for internal support agents. Set up Drizzle ORM connected to a Neon Postgres database with tables for 'users' (agents), 'visitors' (chat end-users), 'chats' (conversation threads linking an agent and visitor), and 'messages' (sender_type, content, timestamp, and chat_id). Ensure all foreign key relations and indexes are fully defined with TypeScript types.
```

2. **Agent Dashboard and Inbox Interface** — Build the main agent dashboard featuring an active conversation list, real-time message thread view, and agent presence status toggles.

```
Build a responsive agent dashboard UI using shadcn/ui components. Create a sidebar showing active, pending, and closed chat queues with visitor metadata (IP, current page, browser). Implement the main chat window view featuring a message history scroll area, rich input box, and an agent status toggle (Online/Away). Fetch active chats from the Neon database via API routes with proper loading and error states.
```

3. **Embeddable Customer Chat Widget** — Create a standalone lightweight JavaScript widget bundle that client websites can embed via script tag to render a floating chat bubble.

```
Develop a standalone embeddable chat widget in TypeScript that compiles into a single JavaScript bundle. The widget should inject a floating action button into the host webpage, opening a clean chat window iframe or shadow-DOM container when clicked. Implement pre-chat data collection (visitor name and email) and connect the widget via WebSockets to our backend API to send and receive live messages seamlessly.
```

4. **Real-Time WebSocket Message Routing** — Implement a real-time WebSocket communication layer to handle instant message delivery, typing indicators, and session handoffs.

```
Implement a WebSocket server within the Next.js backend (or standalone custom server) to handle bidirectional messaging between the embeddable visitor widget and the agent dashboard. Support real-time event distribution for 'message_sent', 'typing_indicator', and 'visitor_presence'. Ensure automatic reconnection logic on the client side and persistence of all inbound/outbound messages into the Neon database.
```

5. **AI Copilot and Text Summarization** — Integrate the Anthropic API via the Vercel AI SDK to provide automated chat summaries and smart text intelligence for support agents.

```
Integrate the Anthropic API using the Vercel AI SDK into the agent dashboard. Build a server action that analyzes the full message history of an active chat thread and generates a concise 3-bullet point chat summary when requested by the agent. Add an AI text enhancement button that rewrites draft agent responses to be polite, professional, and clear before sending.
```

6. **Analytics and Reporting Dashboard** — Build an internal reporting view tracking key metrics including total chat volume, average response times, and chat resolution rates.

```
Create an analytics reporting dashboard page for agents and admins. Query the Neon database to calculate and display key operational metrics: total chat volume per day, average agent response time in seconds, and chat rating distributions. Render these metrics using clean charts and summary cards with date-range filters.
```

### Cost vs paying

**Starting costs (one-time):**

- Custom domain (optional): $12/yr
- Total: ~$12 one-time

**Ongoing costs (monthly):**

- Anthropic API usage for AI summaries: ~$5/mo
- Total: ~$5/mo

- Paying for the SaaS instead: $49/mo (Team plan per agent)
- Build time: 25-35 hours
- AI tool credits: $20/mo (Cursor Pro / Claude)
- Break-even: 1 month

## Sources

- [LiveChat Official Website](https://www.livechat.com)
- [Wikipedia: Text (company)](https://en.wikipedia.org/wiki/Text_(company))
- [G2 Reviews: LiveChat Overview](https://www.g2.com/products/livechat/reviews)