README

LaraChain is a LangChain-inspired AI orchestration framework built specifically for Laravel 12. In 2026, building AI apps is no longer just about calling an API—it's about building Stateful Workflows, Agentic Tools, and Modern RAG pipelines. LaraChain provides the primitives to build these with professional-grade elegance and type safety.

🗺️ How LaraChain Works

LaraChain follows a "Runnable" architecture where every component—prompts, models, retrievers, and parsers—can be piped together.

graph LR
    A[Input Variables] --> B[PromptTemplate]
    B -->|Pipe| C[ChatModel]
    C -->|Pipe| D[OutputParser]
    D --> E[Final PHP Object]
    
    subgraph "The RAG Loop"
        F[PDF/Web/CSV] --> G[TextSplitter]
        G --> H[EmbeddingModel]
        H --> I[VectorStore]
        I -->|Retrieve| B
    end

🚀 Key Features

📂 Folder Structure

larachain/
├── src/
│   ├── Agents/           # ReAct and Agentic logic
│   ├── Chains/           # Pipeline orchestration (Sequential, Router)
│   ├── Contracts/        # Interfaces for all components
│   ├── DocumentLoaders/  # Reading PDF, Web, CSV contents
│   ├── Embeddings/       # Vector generation logic
│   ├── Laravel/          # Service Providers and Facades
│   ├── Memory/           # Conversation state management
│   ├── Messages/         # Message objects (User, Assistant, System)
│   ├── Models/           # AI Model wrappers (ChatModel)
│   ├── Parsers/          # Output formatting (JSON, XML)
│   ├── Prompts/          # Template management
│   ├── Retrieval/        # Document retrieval logic
│   ├── Support/          # Traits and Helpers (HasPipe)
│   ├── TextSplitters/    # Document chunking logic
│   ├── Toolkits/         # Grouped tools (File, Database)
│   ├── Tools/            # Individual tool implementations
│   └── VectorStores/     # Storage drivers (In-Memory, Postgres)

📖 Functional API Guide

1. The Pipe Pattern (Recommended)

The hallmark of LaraChain 2026 is the ability to chain components elegantly.

use LaraChain\Prompts\PromptTemplate;
use LaraChain\Models\ChatModel;
use LaraChain\Parsers\JsonParser;

$chain = PromptTemplate::make('Extract data from this text: {text} into JSON format.')
    ->pipe(new ChatModel('gpt-4o'))
    ->pipe(new JsonParser());

$output = $chain->invoke(['text' => 'My name is John and I live in London.']);
// Returns: ['name' => 'John', 'location' => 'London']

2. Intelligent Agents

An agent can use specialized tools to complete complex tasks.

use LaraChain\Agents\AgentExecutor;
use LaraChain\Toolkits\FileToolkit;

$agent = AgentExecutor::make()
    ->tools((new FileToolkit())->getTools());

$response = $agent->run("Read config.json and summarize it in readme.md");

3. RAG (Postgres + Recursive Chunking)

Handle large documents with state-of-the-art chunking and production storage.

use LaraChain\TextSplitters\RecursiveCharacterTextSplitter;
use LaraChain\VectorStores\PostgresVectorStore;
use LaraChain\Embeddings\EmbeddingModel;

$splitter = new RecursiveCharacterTextSplitter(chunkSize: 1000, chunkOverlap: 200);
$chunks = $splitter->splitText($largePdfContent);

$store = new PostgresVectorStore(new EmbeddingModel());
$store->addTexts($chunks);

⚖️ LaraChain vs. LangChain (For Laravel)

📈 Use Cases

1. Semantic Document Search: Build a "Chat with your PDF" app in minutes using RecursiveSplitter and PostgresVectorStore.
2. Autonomous Code Auditor: Use the FileToolkit and AgentExecutor to scan your repository for security flaws.
3. Structured Data Extraction: Pipe raw OCR text through a ChatModel and JsonParser to ingest invoices into your database.

🛠️ Installation & Setup

composer require subhashladumor1/larachain
php artisan vendor:publish --tag="larachain-config"

Refer to LARACHAIN_VERIFICATION_2026.md for detailed verification of all 2026 market features.

🛠️ Multi-Provider Management

LaraChain uses a Driver-based Architecture (similar to Laravel's Database or Mail systems). You can configure and switch between providers at runtime.

1. Configuration (config/larachain.php)

Define multiple LLM, Vector, and Embedding providers:

'default' => [
    'llm' => 'openai',
    'vectorstore' => 'postgres',
],

'llms' => [
    'openai' => ['model' => 'gpt-4o'],
    'anthropic' => ['model' => 'claude-3-5-sonnet'],
],

2. Switching Providers at Runtime

Use the LaraChain facade to swap drivers dynamically:

// Use Anthropic instead of the default OpenAI
$model = LaraChain::model('anthropic');

// Use a specific vector store
$vectorStore = LaraChain::vectors()->driver('memory');

// Chain them together
$chain = PromptTemplate::make('Hello {name}')
    ->pipe($model)
    ->pipe(new JsonParser());

⚙️ Configuration

Contributions are welcome! Pull requests for new Vector Drivers (Pinecone, Qdrant) are prioritized.

📄 License

The MIT License (MIT). See License File.