Laravel AI SDK vs Prism vs Symfony AI vs LLPhant: which PHP AI library to use in 2026

Which PHP AI library should you use in 2026?

There is no single best PHP AI library. The right one follows from your framework, and then from how much the library should do for you. In October 2026 the choice for most teams comes down to four packages: the official Laravel AI SDK, the community library Prism, the Symfony project's Symfony AI, and the framework-free LLPhant.

We build products on Symfony and work inside existing Laravel codebases. Over the last year we went through the same choice for our own features and client features. Our own SaaS, AI Resume Master, reached 50,000 monthly active users on PHP and the OpenAI API, and our project board transcribes voice notes, drafts task descriptions and writes daily reports through one small OpenAI service on Symfony HttpClient, with no AI library at all. That last option is often the right one, and we cover it at the end.

All versions, dates and requirements below were checked on Packagist and in each project's documentation on 26 October 2026.

The short answer:

1. Laravel 12 or 13: use the Laravel AI SDK. It is official and stable since v1.0.
2. Already on Prism: stay on it unless you need agents with stored conversations, approvals or failover.
3. Symfony 7.3 or newer: use Symfony AI and pin the minor version, because it is still experimental.
4. No framework, or Symfony older than 7.3: look at LLPhant or Neuron AI, or call the provider SDK directly.
5. Plain calls to one provider: you may not need a library at all.

How do the four libraries compare?

Install counts are a signal of where the ecosystem is going, not of quality. They also include CI installs, so compare them only with each other.

Laravel AI SDK: the default for Laravel apps

The Laravel AI SDK is the official answer to "how do I add AI to Laravel". The first release, v0.1.0, came out on 5 February 2026, and v1.0.0 on 23 September 2026. It requires PHP 8.3 and Laravel 12 or 13.

Its central idea is the agent class: one PHP class per assistant that holds the instructions, the conversation, the tools and the output schema. You generate it with php artisan make:agent and call it like any other service.

namespace App\Ai\Agents;

use Illuminate\Contracts\JsonSchema\JsonSchema;
use Laravel\Ai\Contracts\Agent;
use Laravel\Ai\Contracts\HasStructuredOutput;
use Laravel\Ai\Promptable;
use Stringable;

class TicketTriage implements Agent, HasStructuredOutput
{
    use Promptable;

    public function instructions(): Stringable|string
    {
        return 'Classify the support ticket and write a one-line summary for the support team.';
    }

    public function schema(JsonSchema $schema): array
    {
        return [
            'category' => $schema->string()->required(),
            'summary' => $schema->string()->required(),
        ];
    }
}

$response = (new TicketTriage)->prompt($ticket->body);
$ticket->update(['category' => $response['category']]);

What you get beyond the call itself:

• Conversation storage. The RemembersConversations trait stores the history in two database tables that the package migration creates, so a chat survives between requests without your own table.
• Tool approval. A tool that implements Approvable pauses the run until a person approves the call. For tools that delete or send something, this is the feature we would otherwise build by hand.
• Queueing and failover. ->queue() runs the prompt in a queued job. Passing provider: [Lab::OpenAI, Lab::Anthropic] switches to the second provider on a rate limit, an overloaded provider or insufficient credits, but not on an ordinary bad request.
• Token usage on every response, including cache read, cache write and reasoning tokens. That is what you need for per-customer cost logging.
• Fakes for tests. TicketTriage::fake() returns fixed or generated answers, and for structured agents it generates fake data that matches the schema.

The trade-off is that the SDK has opinions: agent classes, its own tables, its own streaming protocols. For a Laravel team those opinions match the framework, so we recommend it as the default for new Laravel work. If you are adding AI to a Laravel app, our Laravel AI integration guide covers the plumbing around any of these libraries: queues, streaming, logging and rate limits.

Prism: a lighter fluent API for Laravel

Prism is the older community library. It started in October 2024, is now at v0.102.0, and requires PHP 8.2 and Laravel 12.61+ or 13.12+. It is a fluent builder rather than a framework of agent classes:

use Prism\Prism\Enums\Provider;
use Prism\Prism\Facades\Prism;

$response = Prism::text()
    ->using(Provider::Anthropic, 'claude-sonnet-5')
    ->withSystemPrompt('Write a one-line summary for the support team.')
    ->withPrompt($ticket->body)
    ->asText();

echo $response->text;

Prism covers text, structured output, tools, streaming, embeddings, images, audio and moderation. Prism::fake() with TextResponseFake keeps tests offline. Tools from MCP servers need the separate prism-php/relay package.

The history matters for planning. The Laravel AI SDK was built on Prism from its first release until v0.6.0. Since v0.6.1 (21 April 2026) it has had its own provider layer and no longer requires Prism. So the two are now separate projects that solve overlapping problems, and Prism is still on 0.x, without a stable API promise.

Choose Prism when you want a thin layer you fully control per request, or when your code already uses it and works. Choose the Laravel AI SDK when you need stored conversations, tool approvals, queueing and failover without writing them yourself.

Symfony AI: the choice for Symfony 7.3 and newer

Symfony AI is the Symfony project's own set of components: Platform (one API for many providers), Agent (tool calling), Store (vector databases), Chat, and two bundles, the AI Bundle and the MCP Bundle. The first tagged release was in December 2025, and the latest is v0.15.0 from 23 October 2026. The components need PHP 8.2, the bundles need Symfony 7.3 or newer.

In a Symfony app most of it is configuration, and the agent arrives through dependency injection:

# config/packages/ai.yaml
ai:
  platform:
    anthropic:
      api_key: '%env(ANTHROPIC_API_KEY)%'
  agent:
    triage:
      platform: 'ai.platform.anthropic'
      model: 'claude-sonnet-5'
      prompt:
        text: 'Write a one-line summary of the support ticket for the support team.'

use Symfony\AI\Agent\AgentInterface;
use Symfony\Component\DependencyInjection\Attribute\Autowire;

final class TicketTriage
{
    public function __construct(
        #[Autowire(service: 'ai.agent.triage')] private AgentInterface $agent,
    ) {}

    public function summarize(string $body): string
    {
        return (string) $this->agent->call($body)->getContent();
    }
}

Its strengths are breadth and fit: about 40 provider bridges, 24 vector store bridges, tools as ordinary services with #[AsTool], permission checks on tools through Symfony Security, and an MCP Bundle that turns your app into an MCP server for Claude or ChatGPT.

Its weakness is stability. Every package is marked experimental, and minor versions do break things. In October 2026 alone, v0.15.0 removed the InMemoryPlatform test double and changed the default pgvector index from ivfflat to hnsw, and the upgrade notes for 0.16 already change how the agent treats your message history. Pin ^0.15, read UPGRADE.md before each bump and keep your own interface between business code and the agent. Our Symfony AI production guide covers cost logging, retries and tests in detail.

If your app is still on Symfony 6.4, the bundles will not install. You can upgrade first (our Symfony 6.4 to 7.4 guide has the steps; we moved this site to 7.4 LTS and PHP 8.5 in October 2026), or use the stable HttpClient and Messenger approach until you do.

LLPhant and Neuron AI: framework-free options

LLPhant has been around since July 2023 and is at 1.0.7. It requires PHP 8.1, works in Symfony, Laravel or plain PHP, and is maintained under the Theodo organization. It is built around RAG: embedding generators, 13 vector stores (including a Doctrine store for PostgreSQL), and a QuestionAnswering class that ties retrieval and the chat model together.

use LLPhant\Chat\AnthropicChat;
use LLPhant\Query\SemanticSearch\QuestionAnswering;

$qa = new QuestionAnswering($vectorStore, $embeddingGenerator, new AnthropicChat());
$answer = $qa->answerQuestion('What is our refund policy for annual plans?');

Its usage is far smaller than the others (about 47,000 installs a month), it has no test doubles of its own, and it uses openai-php/client under the hood for OpenAI. It is a reasonable choice for a framework-free service or a Symfony app older than 7.3 that needs RAG.

Neuron AI (neuron-core/neuron-ai, v4.1.14, PHP 8.1) is another framework-free option, focused on agents and multi-step workflows with human-in-the-loop, MCP connectors and integrations for both Laravel and Symfony. With about 200,000 installs a month it is larger than LLPhant. We have not compared it in depth here because most teams we talk to choose between the first four.

When do you not need an AI library at all?

When all your features use one provider and plain calls, with no agents looping over tools and no vector store, a library adds more than it removes. A service class on top of Symfony HttpClient or Laravel's HTTP client, or the official SDKs (openai-php/client, about 2.8 million installs a month, and Anthropic's anthropic-ai/sdk, v1.3.0), gives you:

• one place that holds the prompt, the model name from config and the token logging;
• tests with a mocked HTTP client and no extra fake layer;
• no 0.x dependency to upgrade every month.

That is how our project board runs its five OpenAI features today, from voice transcription to daily reports. We would move it to Symfony AI only when it needs a second provider, several tools or a vector store.

Which library do we recommend?

Whatever you choose, the library is the smallest part of the work. The parts that decide whether the feature survives production are the same in every stack: an evaluation set of 50 to 200 real inputs, token and cost logging per customer, rate limits per user, and background processing for anything slower than a few seconds. Before you pick a model, our LLM API cost calculator shows what a feature will cost per month at your volume.

Next step

If you want a senior PHP team to add an OpenAI, Claude or Gemini feature to your Laravel or Symfony product, see our AI integration services. Most first features take 4 to 8 weeks and start from $10,000, with a fixed quote after a short scoping call. Contact us with a description of the feature and the data it needs.