PHP LLM - Agentic AI Framework for PHP

Build powerful AI agents that can use tools, self-correct, and take autonomous actions. A unified PHP framework for Large Language Models with support for Anthropic Claude, OpenAI GPT, Google Gemini, and more.

What is Agentic AI? Agents that can call functions, validate outputs, iterate on responses, and make decisions autonomously - not just generate text.

composer require soukicz/llm

📚 Full Documentation

→ Complete guides, API reference, and examples: soukicz.github.io/php-llm

Why PHP LLM?

🤖 Build AI Agents - Create autonomous agents with tools, feedback loops, and state management
🔄 Unified API - One interface for Anthropic, OpenAI, Gemini, and more
🛠️ Function Calling - Empower agents to interact with external systems and APIs
📝 Built-in Tools - TextEditorTool for file manipulation, embeddings API, and more
✅ Self-Correcting - Validate and refine outputs with feedback loops
📸 Multimodal - Process images and PDFs alongside text (with caching support)
🧠 Reasoning Models - OpenAI reasoning models, Anthropic extended thinking, and Gemini thinking
📐 Structured Output - JSON Schema enforced responses across Anthropic, OpenAI, and Gemini
📡 Streaming - Real-time response streaming with optional listener for live progress updates
⚡ Async & Caching - Fast, cost-effective operations with prompt caching
💾 State Persistence - Save and resume conversations with thread IDs
📊 Monitoring - Built-in logging, cost tracking, and debugging interfaces

Key Concepts

Before you start, understanding these core concepts will help you use the library effectively:

Async by Default

All LLM clients in this library are asynchronous by default using Guzzle Promises. The run() method is a convenience wrapper that calls runAsync()->wait() internally. For production applications handling multiple requests, use the async methods directly for better performance.

Two Types of Clients

LLM Clients (AnthropicClient, OpenAIClient, etc.) - Low-level API clients that send a single request and return a single response. Use these when you need direct control over individual API calls.
Agent Client (LLMAgentClient) - High-level orchestrator that handles multi-turn conversations, automatic tool calling, feedback loops, and retries. Use this for building agents that need to iterate or use tools.

Model Versions

Many Anthropic and OpenAI models pin an explicit version constant:

<?php
new AnthropicClaude45Haiku(AnthropicClaude45Haiku::VERSION_20251001)
new GPT54(GPT54::VERSION_2026_03_05)

The newest Anthropic models (e.g. Claude 4.6) and Google Gemini models do NOT require versions - just instantiate them directly:

<?php
new AnthropicClaude46Sonnet()
new Gemini25Flash()

Conversations & State

LLMConversation manages the message history and can be serialized/deserialized for persistence. Each conversation has an optional threadId (UUID) for tracking across sessions.

Quick Start

<?php
require_once __DIR__ . '/vendor/autoload.php';

use Soukicz\Llm\Cache\FileCache;
use Soukicz\Llm\Client\Anthropic\AnthropicClient;
use Soukicz\Llm\Client\Anthropic\Model\AnthropicClaude46Sonnet;
use Soukicz\Llm\Client\LLMAgentClient;
use Soukicz\Llm\Message\LLMMessage;
use Soukicz\Llm\LLMConversation;
use Soukicz\Llm\LLMRequest;

// Optional: Enable prompt caching to reduce costs
$cache = new FileCache(sys_get_temp_dir());

// Create the API client (low-level, sends single requests)
$client = new AnthropicClient('sk-xxxxx', $cache);

// Create the agent client (high-level, handles tool calls and feedback loops)
$agentClient = new LLMAgentClient();

// Run a request (this is synchronous - use runAsync() for better performance)
$response = $agentClient->run(
    client: $client,
    request: new LLMRequest(
        model: new AnthropicClaude46Sonnet(),
        conversation: new LLMConversation([
            LLMMessage::createFromUserString('What is PHP?')
        ]),
    )
);

// Get the assistant's response text
echo $response->getLastText();

Async Usage

<?php
// For better performance, use async operations
$promise = $agentClient->runAsync($client, $request);

$promise->then(
    function (LLMResponse $response) {
        echo $response->getLastText();
    },
    function (Exception $error) {
        echo "Error: " . $error->getMessage();
    }
);

Provider-Specific Setup

<?php
// Anthropic Claude
$client = new AnthropicClient(
    apiKey: 'sk-ant-xxxxx',
    cache: $cache,
    customHttpMiddleware: null,
    betaFeatures: [] // Optional Anthropic beta feature flags
);

// OpenAI (organization parameter is required)
$client = new OpenAIClient(
    apiKey: 'sk-xxxxx',
    apiOrganization: 'org-xxxxx', // Required parameter
    cache: $cache
);

// Google Gemini
$client = new GeminiClient(
    apiKey: 'your-key',
    cache: $cache
);

→ More Examples

Core Features

🛠️ Function Calling (Tools)

Enable AI agents to call external functions and APIs:

use Soukicz\Llm\Tool\CallbackToolDefinition;
use Soukicz\Llm\Message\LLMMessageContents;

$weatherTool = new CallbackToolDefinition(
    name: 'get_weather',
    description: 'Get current weather for a location',
    inputSchema: ['type' => 'object', 'properties' => ['city' => ['type' => 'string']]],
    handler: fn($input) => LLMMessageContents::fromArrayData([
        'temperature' => 22,
        'condition' => 'sunny'
    ])
);

$response = $agentClient->run($client, new LLMRequest(
    model: $model,
    conversation: $conversation,
    tools: [$weatherTool],
));

Note: Tool handlers must return LLMMessageContents or a Promise. See Tools Documentation for complete examples.

→ Tools Documentation

✅ Feedback Loops

Build self-correcting agents that validate and improve their outputs:

$response = $agentClient->run(
    client: $client,
    request: $request,
    feedbackCallback: function ($response) {
        if (!isValid($response->getLastText())) {
            return LLMMessage::createFromUserString('Please try again with valid JSON');
        }
        return null; // Valid, stop iteration
    }
);

→ Feedback Loops Documentation

📸 Multimodal Support

Process images and PDFs alongside text:

use Soukicz\Llm\Message\LLMMessageContents;
use Soukicz\Llm\Message\LLMMessageImage;
use Soukicz\Llm\Message\LLMMessagePdf;
use Soukicz\Llm\Message\LLMMessageText;

// Images
$imageData = base64_encode(file_get_contents('/path/to/image.jpg'));
$message = LLMMessage::createFromUser(new LLMMessageContents([
    new LLMMessageText('What is in this image?'),
    new LLMMessageImage('base64', 'image/jpeg', $imageData, cached: true) // Enable prompt caching
]));

// PDFs
$pdfData = base64_encode(file_get_contents('/path/to/document.pdf'));
$message = LLMMessage::createFromUser(new LLMMessageContents([
    new LLMMessageText('Summarize this document'),
    new LLMMessagePdf('base64', $pdfData, cached: true) // Optimize with caching
]));

Tip: Use the cached: true parameter on large images/PDFs to enable prompt caching and reduce costs.

→ Multimodal Documentation

📡 Streaming

Show real-time progress while keeping the simple request/response API:

use Soukicz\Llm\Stream\CallableStreamListener;
use Soukicz\Llm\Stream\StreamEvent;
use Soukicz\Llm\Stream\StreamEventType;

$response = $agentClient->run($client, new LLMRequest(
    model: $model,
    conversation: $conversation,
    tools: $tools,
    streamListener: new CallableStreamListener(function (StreamEvent $event) {
        match ($event->type) {
            StreamEventType::TEXT_DELTA => print($event->delta),
            StreamEventType::TOOL_USE_START => print("\n🔧 {$event->toolName}\n"),
            default => null,
        };
    }),
));

// $response is identical to non-streaming — streaming is just a side-effect
echo "\nTokens: {$response->getInputTokens()} in, {$response->getOutputTokens()} out\n";

Key design: Streaming is transparent. The listener auto-propagates through tool loops, so you get updates for every step of an agentic workflow. No changes needed to LLMAgentClient, tools, or feedback loops.

→ Streaming Documentation

🧠 Reasoning Models

Use advanced reasoning for complex problems:

use Soukicz\Llm\Config\ReasoningEffort;
use Soukicz\Llm\Config\ReasoningBudget;
use Soukicz\Llm\Client\Anthropic\Model\AnthropicClaude46Sonnet;
use Soukicz\Llm\Client\OpenAI\Model\GPT54;

// Control reasoning with effort level (OpenAI, Anthropic, and Gemini)
$request = new LLMRequest(
    model: new GPT54(GPT54::VERSION_2026_03_05),
    conversation: $conversation,
    reasoningConfig: ReasoningEffort::HIGH // NONE, MINIMAL, LOW, MEDIUM, HIGH, or EXTRA_HIGH
);

// Or use token-based budget control (Anthropic only)
$request = new LLMRequest(
    model: new AnthropicClaude46Sonnet(),
    conversation: $conversation,
    reasoningConfig: new ReasoningBudget(10000) // Max reasoning tokens
);

→ Reasoning Models Documentation

📐 Structured Output

Force responses to match a JSON Schema and get them back as a PHP array - supported by Anthropic, OpenAI, and Gemini:

use Soukicz\Llm\Config\StructuredOutputConfig;

$response = $agentClient->run($client, new LLMRequest(
    model: new AnthropicClaude46Sonnet(),
    conversation: new LLMConversation([
        LLMMessage::createFromUserString('Extract user data: John Doe, age 30, email john@example.com')
    ]),
    structuredOutputConfig: new StructuredOutputConfig([
        'type' => 'object',
        'properties' => [
            'name' => ['type' => 'string'],
            'age' => ['type' => 'integer'],
            'email' => ['type' => 'string'],
        ],
        'required' => ['name', 'age', 'email'],
        'additionalProperties' => false,
    ]),
));

$data = $response->getLastStructuredData(); // ['name' => 'John Doe', 'age' => 30, 'email' => 'john@example.com']

Tip: Strict schema validation is enabled by default - pass strict: false to relax it.

→ Structured Output Documentation

Advanced Features

📝 TextEditorTool - Built-in File Manipulation

Empower agents to read, write, and manage files with the built-in TextEditorTool:

use Soukicz\Llm\Tool\TextEditor\TextEditorTool;
use Soukicz\Llm\Tool\TextEditor\TextEditorStorageFilesystem;

// Create filesystem storage with sandboxing
$storage = new TextEditorStorageFilesystem('/safe/workspace/path');
$textEditorTool = new TextEditorTool($storage);

// Works out of the box with Anthropic Claude - no beta flags needed on modern models
$client = new AnthropicClient(
    apiKey: 'sk-ant-xxxxx',
    cache: $cache
);

$response = $agentClient->run($client, new LLMRequest(
    model: new AnthropicClaude46Sonnet(),
    conversation: new LLMConversation([
        LLMMessage::createFromUserString('Create a PHP file with a hello world function')
    ]),
    tools: [$textEditorTool]
));

→ Tools Documentation for complete TextEditorTool examples

🔢 Embeddings API

Generate embeddings for semantic search, clustering, and RAG applications:

use Soukicz\Llm\Client\OpenAI\OpenAIClient;

$client = new OpenAIClient('sk-xxxxx', 'your-org-id');

$embeddings = $client->getBatchEmbeddings(
    texts: ['Hello world', 'PHP is great', 'AI embeddings'],
    model: 'text-embedding-3-small',
    dimensions: 512
);

// Returns array of float arrays (embeddings)
foreach ($embeddings as $i => $embedding) {
    echo "Text {$i} embedding dimensions: " . count($embedding) . "\n";
}

📊 Monitoring & Debugging

Built-in interfaces for logging and monitoring:

use Soukicz\Llm\Log\LLMLogger;
use Soukicz\Llm\LLMRequest;
use Soukicz\Llm\LLMResponse;

// Implement custom logger
class MyLogger implements LLMLogger {
    public function requestStarted(LLMRequest $request): void {
        echo "Request started\n";
    }

    public function requestFinished(LLMResponse $response): void {
        // Log responses, costs, tokens, etc.
        $cost = ($response->getInputPriceUsd() ?? 0) + ($response->getOutputPriceUsd() ?? 0);
        echo "Cost: $" . $cost . "\n";
        echo "Tokens: {$response->getInputTokens()} in, {$response->getOutputTokens()} out\n";
    }
}

// Attach to agent client
$agentClient = new LLMAgentClient(logger: new MyLogger());

→ Logging & Debugging Documentation

⚙️ Advanced Request Configuration

Fine-tune your requests with additional parameters:

use Soukicz\Llm\LLMRequest;

$request = new LLMRequest(
    model: $model,
    conversation: $conversation,
    tools: $tools,

    // Custom stop sequences to halt generation
    stopSequences: ['END', '---'],

    // Reasoning configuration (OpenAI reasoning models, Anthropic extended thinking, Gemini thinking)
    reasoningConfig: ReasoningEffort::HIGH,
    // OR token-based budget (Anthropic only):
    // reasoningConfig: new ReasoningBudget(10000),

    // Optional: Stream responses for real-time progress
    // streamListener: new CallableStreamListener(fn($e) => print($e->delta)),
);

// Access cost and token information
$response = $agentClient->run($client, $request);
$cost = ($response->getInputPriceUsd() ?? 0) + ($response->getOutputPriceUsd() ?? 0);
echo "Cost: $" . $cost . "\n";
echo "Input tokens: " . $response->getInputTokens() . "\n";
echo "Output tokens: " . $response->getOutputTokens() . "\n";
echo "Stop reason: " . $response->getStopReason()->value . "\n"; // FINISHED, TOOL_USE, LENGTH, SAFETY

Supported Providers

Anthropic (Claude) - Claude 3.5 through 4.6 series models
OpenAI (GPT) - GPT-4o, GPT-4.1, o3 and o4-mini (reasoning), and GPT-5 through GPT-5.4 series models
Google Gemini - Gemini 2.0 through 3.x series models
OpenAI-Compatible - OpenRouter, local servers (Ollama, llama-server), and more
AWS Bedrock - Via separate package (soukicz/llm-aws-bedrock)

→ Provider Comparison

Documentation

Getting Started

Quick Start Examples - Get up and running in minutes
Configuration Guide - Configure clients and requests
Provider Overview - Choose the right provider
Best Practices - Production-ready patterns

Core Features

Tools & Function Calling - External tools, TextEditorTool, custom functions
Feedback Loops - Self-correcting agents and validation
Multimodal Support - Images, PDFs, and caching
Streaming - Real-time response streaming with progress listeners
Reasoning Models - Reasoning and extended thinking with effort and budget control
Structured Output - JSON Schema enforced responses

Advanced Features

Caching - Prompt caching and cost reduction
Batch Processing - High-volume async operations
State Management - Persistence and thread IDs
Logging & Debugging - Monitor and debug

Common Use Cases

AI Agent with Tools

use Soukicz\Llm\Tool\CallbackToolDefinition;
use Soukicz\Llm\Message\LLMMessageContents;

// Create custom tools for the agent
$calculatorTool = new CallbackToolDefinition(
    name: 'calculate',
    description: 'Perform mathematical calculations',
    inputSchema: [
        'type' => 'object',
        'properties' => [
            'expression' => ['type' => 'string', 'description' => 'Math expression to evaluate']
        ]
    ],
    handler: fn($input) => LLMMessageContents::fromArrayData([
        'result' => eval('return ' . $input['expression'] . ';')
    ])
);

$searchTool = new CallbackToolDefinition(
    name: 'search_database',
    description: 'Search the product database',
    inputSchema: [
        'type' => 'object',
        'properties' => [
            'query' => ['type' => 'string']
        ]
    ],
    handler: function($input) use ($pdo) {
        $stmt = $pdo->prepare('SELECT * FROM products WHERE name LIKE ?');
        $stmt->execute(['%' . $input['query'] . '%']);
        return LLMMessageContents::fromArrayData($stmt->fetchAll());
    }
);

// Agent will automatically use tools as needed
$response = $agentClient->run($client, new LLMRequest(
    model: $model,
    conversation: new LLMConversation([
        LLMMessage::createFromUserString('Find products with "laptop" and calculate 15% discount on $999')
    ]),
    tools: [$searchTool, $calculatorTool],
));

Self-Correcting JSON Parser

// Agent that validates and corrects its own output
$iterations = 0;

$response = $agentClient->run(
    client: $client,
    request: new LLMRequest(
        model: $model,
        conversation: new LLMConversation([
            LLMMessage::createFromUserString('Extract user data as JSON: John Doe, age 30, email john@example.com')
        ])
    ),
    feedbackCallback: function ($response) use (&$iterations) {
        if (++$iterations >= 3) {
            return null; // Limit retry attempts
        }

        $text = $response->getLastText();
        json_decode($text);

        if (json_last_error() !== JSON_ERROR_NONE) {
            return LLMMessage::createFromUserString(
                'Invalid JSON: ' . json_last_error_msg() . '. Please fix the syntax.'
            );
        }

        return null; // Valid JSON, stop iteration
    }
);

Multimodal Document Analysis

use Soukicz\Llm\Message\{LLMMessageContents, LLMMessageText, LLMMessageImage, LLMMessagePdf};

// Agent that analyzes multiple document types
$chartData = base64_encode(file_get_contents('/sales-chart.png'));
$reportData = base64_encode(file_get_contents('/quarterly-report.pdf'));

$response = $agentClient->run($client, new LLMRequest(
    model: new AnthropicClaude46Sonnet(),
    conversation: new LLMConversation([
        LLMMessage::createFromUser(new LLMMessageContents([
            new LLMMessageText('Analyze these documents and summarize the key insights'),
            new LLMMessageImage('base64', 'image/png', $chartData, cached: true),
            new LLMMessagePdf('base64', $reportData, cached: true),
        ]))
    ])
));

echo $response->getLastText();

Frequently Asked Questions

What's the difference between "agentic" and regular LLM usage?

Agentic AI refers to LLMs that can autonomously take actions, use tools, and iterate on their responses. Instead of just generating text, agentic systems:

Call external functions and APIs (tool use)
Validate and self-correct their outputs (feedback loops)
Make decisions about which tools to use
Persist state across multiple interactions

This library is designed specifically to make building such agents easy in PHP.

How do I reduce API costs?

Enable caching: Pass a FileCache instance to reduce repeated prompts
Use prompt caching: Set cached: true on images/PDFs
Choose appropriate models: Smaller models for simpler tasks
Use stop sequences: Define custom stop sequences to prevent over-generation

Can I use this with local models?

Yes! Use the OpenAICompatibleClient to connect to:

Ollama (local models)
llama-server
OpenRouter
Any service with OpenAI-compatible API

How do I save and resume conversations?

// Save conversation
$json = json_encode($conversation);
file_put_contents('conversation.json', $json);

// Resume conversation
$data = json_decode(file_get_contents('conversation.json'), true);
$conversation = LLMConversation::fromJson($data);

Development

Running Tests

# Copy environment template
cp .env.example .env

# Add your API keys to .env
# ANTHROPIC_API_KEY=sk-ant-xxxxx
# OPENAI_API_KEY=sk-xxxxx
# GEMINI_API_KEY=your-key

# Run tests
vendor/bin/phpunit

Requirements

PHP 8.3 or higher
Composer

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open-sourced software licensed under the BSD-3-Clause license.

Links

Documentation - Full documentation
GitHub - Source code
Packagist - Composer package

Built for modern PHP • Requires PHP 8.3+ • BSD-3-Clause Licensed

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Readme.md		Readme.md
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PHP LLM - Agentic AI Framework for PHP

📚 Full Documentation

Why PHP LLM?

Key Concepts

Async by Default

Two Types of Clients

Model Versions

Conversations & State

Quick Start

Async Usage

Provider-Specific Setup

Core Features

🛠️ Function Calling (Tools)

✅ Feedback Loops

📸 Multimodal Support

📡 Streaming

🧠 Reasoning Models

📐 Structured Output

Advanced Features

📝 TextEditorTool - Built-in File Manipulation

🔢 Embeddings API

📊 Monitoring & Debugging

⚙️ Advanced Request Configuration

Supported Providers

Documentation

Getting Started

Core Features

Advanced Features

Common Use Cases

AI Agent with Tools

Self-Correcting JSON Parser

Multimodal Document Analysis

Frequently Asked Questions

What's the difference between "agentic" and regular LLM usage?

How do I reduce API costs?

Can I use this with local models?

How do I save and resume conversations?

Development

Running Tests

Requirements

Contributing

License

Links

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