Appearance

Mindwave: Production AI Utilities for Laravel ​

The working developer's AI toolkit - Long prompts, streaming, tracing, and context discovery made simple.

Version 1.0 - Production-ready with 500+ tests, all 4 pillars complete, and comprehensive documentation.

What is Mindwave? ​

Mindwave is a Laravel package that provides production-grade AI utilities for building LLM-powered features. Unlike complex agent frameworks, Mindwave focuses on practical tools that Laravel developers actually need:

  • ✅ Auto-fit long prompts to any model's context window
  • ✅ Stream LLM responses with 3 lines of code (SSE/EventSource)
  • ✅ OpenTelemetry tracing with database storage for costs, tokens, and performance
  • ✅ Ad-hoc context discovery from your database/CSV using TNTSearch

Why Mindwave? ​

Not another agent framework. Just batteries-included utilities for shipping AI features fast.

Core Features ​

🧩 Prompt Composer ​

Automatically manage context windows with priority-based section trimming:

  • Token budgeting - Reserve tokens for output, auto-fit sections
  • Smart shrinkers - Summarize, truncate, or compress content
  • Priority system - Keep important sections, trim less critical ones
  • Multi-model support - Works with GPT-4, Claude, Mistral, etc.

Learn more about Prompt Composer →

🌊 Streaming (SSE) ​

Production-ready Server-Sent Events streaming:

  • 3-line setup - Backend and frontend
  • Proper headers - Works with Nginx/Apache out of the box
  • Connection monitoring - Handles client disconnects
  • Error handling - Graceful failure and retry

Learn more about Streaming →

📊 OpenTelemetry Tracing ​

Industry-standard observability with GenAI semantic conventions:

  • Automatic tracing - All LLM calls tracked (zero configuration)
  • Database storage - Query traces via Eloquent models
  • OTLP export - Send to Jaeger, Grafana, Datadog, Honeycomb, etc.
  • Cost tracking - Automatic cost estimation per call
  • Token usage - Input/output/total tokens tracked
  • PII protection - Configurable message capture and redaction

Learn more about Tracing →

🔍 TNTSearch Context Discovery ​

Pull context from your application data without complex RAG setup:

  • No infrastructure - Pure PHP, no external services
  • Multiple sources - Eloquent, arrays, CSV files, VectorStores
  • Fast indexing - Ephemeral indexes with automatic cleanup
  • BM25 ranking - Industry-standard relevance scoring
  • Auto-query extraction - Automatically extracts search terms from user messages

Learn more about Context Discovery →

🧠 Brain & Vector Stores ​

Persistent knowledge management with semantic search:

  • Multiple backends - Pinecone, Qdrant, Weaviate, or local file storage
  • Document loaders - Built-in loaders for various formats (PDF, Word, CSV, etc.)
  • Automatic chunking - Documents split for optimal retrieval
  • Semantic search - Find information by meaning, not just keywords

Learn more about the Brain →

Quick Start ​

Installation ​

Install via Composer:

bash
composer require mindwave/mindwave

Publish the config files:

bash
php artisan vendor:publish --tag="mindwave-config"

Run migrations for tracing (optional but recommended):

bash
php artisan migrate

Basic LLM Chat ​

php
use Mindwave\Mindwave\Facades\Mindwave;

$response = Mindwave::llm()->chat([
    ['role' => 'system', 'content' => 'You are a helpful assistant.'],
    ['role' => 'user', 'content' => 'Explain Laravel in one sentence.'],
]);

echo $response->content;

Streaming Responses ​

Backend:

php
Route::get('/chat', function (Request $request) {
    return Mindwave::stream($request->input('message'))
        ->model('gpt-4')
        ->respond();
});

Frontend:

javascript
const stream = new EventSource('/chat?message=' + encodeURIComponent(question));
stream.onmessage = (e) => (output.textContent += e.data);
stream.addEventListener('done', () => stream.close());

Auto-Fit Long Prompts ​

php
Mindwave::prompt()
    ->reserveOutputTokens(500)
    ->section('system', 'You are an expert analyst', priority: 100)
    ->section('documentation', $longDocContent, priority: 50, shrinker: 'summarize')
    ->section('history', $conversationHistory, priority: 75)
    ->section('user', $userQuestion, priority: 100)
    ->fit()  // Trims to model's context window
    ->run();

Documentation ​

Supported Providers ​

LLM Providers ​

  • ✅ OpenAI (GPT-4, GPT-3.5, etc.)
  • ✅ Mistral AI (Mistral Large, Small, etc.)
  • ✅ Anthropic (Claude 3.5 Sonnet, Opus, Haiku, etc.)
  • 🔄 Google Gemini (Coming soon)

Vector Stores ​

  • ✅ Qdrant - High-performance vector database
  • ✅ Weaviate - Open-source vector search engine
  • ✅ Pinecone - Managed vector database service
  • ✅ In-Memory - For testing and development
  • ✅ File-based - JSON file storage for simple use cases

Use Cases ​

  • 💬 AI-Powered Customer Support - Context-aware support bots
  • 📄 Document Q&A - Search and answer questions from documents
  • 🔍 Data Analysis - Analyze application data with AI
  • 📝 Content Generation - Generate content with proper context
  • 🤖 Chatbots - Build conversational AI applications

Credits ​

License ​

The MIT License (MIT).