Building a Chatbot in Laravel from Scratch

Chatbots are no longer limited to large tech companies. Today, businesses of all sizes use chatbot systems to automate customer support, improve response times, handle repetitive tasks, and create better user experiences across websites and applications. From simple FAQ assistants to AI-powered conversational platforms, chatbots have become an important part of modern web development.

Laravel is one of the best frameworks for building chatbot applications because it provides a clean architecture, powerful routing, database abstraction, queue support, and seamless API integration. Whether you want to build a simple rule-based chatbot or integrate advanced AI services like OpenAI, Laravel offers a flexible foundation that can scale with your application needs.

In this tutorial, you will learn how to build a chatbot in Laravel from scratch using database storage, API endpoints, conversation management, and optional AI integration. We will create a simple but scalable chatbot system step by step, starting from database design all the way to building a frontend chat interface.

By the end of this guide, you will understand how to:

Create chatbot APIs in Laravel
Store conversations and messages in a database
Build rule-based chatbot responses
Integrate AI-powered replies using external APIs
Create a simple frontend chat interface
Structure a scalable chatbot application architecture

This tutorial is beginner-friendly but also includes practical patterns that can be used in real-world Laravel applications.

Why Laravel?

Clean MVC architecture
Powerful routing system
Easy API development
Strong ecosystem for scaling applications

Step 1: Create Laravel Project

bash
composer create-project laravel/laravel laravel-chatbot
cd laravel-chatbot
php artisan migrate

Step 2: Database Structure

We need two tables to store conversations and messages.

Conversations Table

php
<?php
Schema::create('conversations', function (Blueprint $table) {
    $table->id();
    $table->string('user_id')->nullable();
    $table->timestamps();
});

Messages Table

php
<?php
Schema::create('messages', function (Blueprint $table) {
    $table->id();
    $table->foreignId('conversation_id')->constrained()->onDelete('cascade');
    $table->enum('sender', ['user', 'bot']);
    $table->text('message');
    $table->timestamps();
});

Step 3: Chatbot Controller

php
<?php
public function sendMessage(Request $request)
{
    $conversation = Conversation::firstOrCreate([
        'user_id' => $request->user_id ?? 'guest'
    ]);

    Message::create([
        'conversation_id' => $conversation->id,
        'sender' => 'user',
        'message' => $request->message
    ]);

    $response = $this->generateResponse($request->message);

    Message::create([
        'conversation_id' => $conversation->id,
        'sender' => 'bot',
        'message' => $response
    ]);

    return response()->json([
        'reply' => $response
    ]);
}

Step 4: Rule-Based Logic

php
<?php
private function generateResponse($message)
{
    $message = strtolower($message);

    if (str_contains($message, 'hello')) {
        return "Hi! How can I help you today?";
    }

    if (str_contains($message, 'price')) {
        return "Our pricing starts from $10/month.";
    }

    if (str_contains($message, 'support')) {
        return "You can contact support@example.com";
    }

    return "Sorry, I didn't understand that. Can you rephrase?";
}

Step 5: API Route

php
<?php
Route::post('/chat/send', [ChatbotController::class, 'sendMessage']);

Step 6: Optional AI Integration

php
<?php

use Illuminate\Support\Facades\Http;

private function aiResponse($message)
{
    $response = Http::withHeaders([
        'Authorization' => 'Bearer YOUR_API_KEY'
    ])->post('https://api.openai.com/v1/chat/completions', [
        'model' => 'gpt-4o-mini',
        'messages' => [
            ['role' => 'user', 'content' => $message]
        ]
    ]);

    return $response['choices'][0]['message']['content'] ?? 'No response';
}

Step 7: Frontend Chat UI

javascript
document.getElementById('chatForm').addEventListener('submit', async (e) => {
    e.preventDefault();

    let message = document.getElementById('message').value;

    let res = await fetch('/api/chat/send', {
        method: 'POST',
        headers: {'Content-Type': 'application/json'},
        body: JSON.stringify({message})
    });

    let data = await res.json();

    console.log(data.reply);
});

Conclusion

Laravel makes it easy to build scalable chatbot systems. You can start with simple rule-based logic and evolve into an AI-powered assistant for production use.