AI Provider

Interact with LLM providers or extend the framework to implement new ones.

With Neuron you can switch between LLM providers with just one line of code, without any impact on your agent implementation.

Anthropic

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
        );
    }
}

$message = MyAgent::make()
    ->setThreadId('chat_id')
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Anthropic Prompt Cache

To laverage Anthropic prompt cache you should provide agent instructions as an array of SystemContent:

use NeuronAI\Chat\Messages\ContentBlocks\SystemContent;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    protected function instructions(): string|array
    {
        return [
            new SystemContent('fixed part')->cache(),
            new SystemContent('dynamic part'),
        ];
    }
}

Anthropic on Google Vertex AI

To use this provider you need to install the goole auth composer package:

composer require google/auth

Below the syntax to use AnthropicVertex in your agent.

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\AnthropicVertex;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new AnthropicVertex(
            pathJsonCredentials: 'GOOGLE_FILE_CREDENTIALS_PATH',
            location: 'GOOGLE_LOCATION',
            projectId: 'GOOGLE_PROJECT_ID',
            model: 'ANTHROPIC_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
        );
    }
}

OpenAIResponses

This component uses the most recent OpenAI responses API:

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\OpenAI\Responses\OpenAIResponses;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new OpenAIResponses(
            key: 'OPENAI_API_KEY',
            model: 'OPENAI_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
            strict_response: false, // Strict structured output
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

OpenAI

This component uses the old OpenAI completions API:

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\OpenAI\OpenAI;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new OpenAI(
            key: 'OPENAI_API_KEY',
            model: 'OPENAI_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
            strict_response: false, // Strict structured output
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

AzureOpenAI

This provider allows you to connect with OpenAI models provided in the Azure cloud platform.

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\AzureOpenAI;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new AzureOpenAI(
            key: 'AZURE_API_KEY',
            endpoint: 'AZURE_ENDPOINT',
            model: 'OPENAI_MODEL',
            version: 'AZURE_API_VERSION'
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

OpenAILike

This class simplify the connection with providers offering the same data format of the official OpenAI API.

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\OpenAILike;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new OpenAILike(
            baseUri: 'https://api.together.xyz/v1',
            key: 'API_KEY',
            model: 'MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
            strict_response: false, // Strict structured output
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Ollama

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Ollama\Ollama;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Ollama(
            url: 'OLLAMA_URL',
            model: 'OLLAMA_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Gemini

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Gemini\Gemini;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Gemini(
            key: 'GEMINI_API_KEY',
            model: 'GEMINI_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Gemini on Vertex AI

To use this provider you need to install the goole auth composer package:

composer require google/auth

Below the syntax to use GeminiVertex in your agent.

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Gemini\GeminiVertex;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new GeminiVertex(
            pathJsonCredentials: 'GOOGLE_FILE_CREDENTIALS_PATH',
            location: 'GOOGLE_LOCATION',
            projectId: 'GOOGLE_PROJECT_ID',
            model: 'GEMINI_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
        );
    }
}

Mistral

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Mistral\Mistral;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Mistral(
            key: 'MISTRAL_API_KEY',
            model: 'MISTRAL_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
            strict_response: false, // Strict structured output
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

ZAI

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\ZAI\ZAI;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new ZAI(
            key: 'ZAI_API_KEY',
            model: 'glm-5',
            parameters: [], // Add custom params (temperature, logprobs, etc)
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

HuggingFace

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\HuggingFace\HuggingFace;
use NeuronAI\Providers\HuggingFace\InferenceProvider;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new HuggingFace(
            key: 'HF_ACCESS_TOKEN',
            model: 'mistralai/Mistral-7B-Instruct-v0.3',
            // https://huggingface.co/docs/inference-providers/en/index
            inferenceProvider: InferenceProvider::HF_INFERENCE,
            parameters: [
                'max_tokens' => 500,
                'temperature' => 0.5
            ]
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Deepseek

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Deepseek\Deepseek;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Deepseek(
            key: 'DEEPSEEK_API_KEY',
            model: 'DEEPSEEK_MODEL',
            parameters: [], // Add custom params (temperature, logprobs, etc)
            strict_response: false, // Strict structured output
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Grok (X-AI)

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\XAI\Grok;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Grok(
            key: 'GROK_API_KEY',
            model: 'grok-4',
            parameters: [], // Add custom params (temperature, logprobs, etc)
            strict_response: false, // Strict structured output
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

AWS Bedrock Runtime

To use The BedrockRuntime provider you need to install the aws/aws-sdk-php package.

composer require aws/aws-sdk-php

Below you can find the syntax to use it in your agent.

namespace App\Neuron;

use Aws\BedrockRuntime\BedrockRuntimeClient;
use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\AWS\BedrockRuntime;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        $client = new BedrockRuntimeClient([
            'version' => 'latest',
            'region' => 'us-east-1',
            'credentials' => [
                'key' => 'AWS_BEDROCK_KEY',
                'secret' => 'AWS_BEDROCK_SECRET',
            ],
        ]);
        
        return new BedrockRuntime(
            client: $client,
            model: 'AWS_BEDROCK_MODEL',
            inferenceConfig: []
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Cohere

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Cohere\Cohere;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Cohere(
            key: 'COHERE_API_KEY',
            model: 'command-a-reasoning-08-2025',
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Alibaba DashScope

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Alibaba\DashScopeOpenAI;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new DashScopeOpenAI(
            key: 'DS_API_KEY',
            model: 'wan2.6-t2i',
        );
    }
}

$message = MyAgent::make()
    ->chat(new UserMessage("Hi!"))
    ->getMessage();

echo $message->getContent();
// Hi, how can I help you today?

Routing

Not every prompt needs your most expensive model. With our official neuron-core/router package you can route inference calls to different providers or models, transparently to the agent itself. The router also provides you with a fallback strategy to a second provider in case the primary fails due to a service down or exceeding usage limits.

First install the package:

composer require neuron-core/router

Now use the RouterProvider class as any other provider in your agent class:

use NeuronAI\Router\RouterProvider;
use NeuronAI\Router\Rules\MethodRule;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Providers\OpenAI\OpenAI;

class MyAgent extens Agent
{
    protected function provider(): AIProviderInterface
    {
        return RouterProvider::make()
            ->addProvider('anthropic', new Anthropic(
                key: 'ANTHROPIC_API_KEY',
                model: 'claude-sonnet-4-20250514',
            ))
            ->addProvider('openai', new OpenAI(
                key: 'OPENAI_API_KEY',
                model: 'gpt-4o',
            ))
            ->setRule(
                new RoundRobinRule(['anthropic', 'openai'])
            );
    }

    protected function instructions(): string
    {...}

    protected function tools(): array
    {...}
}

In the example above we use the RoundRobinRule making the router act as a load balancer between the attached AI providers. The package ships with several built-in rules including an LLM classifier to route calls to the appropriate model based on the promp difficulty score: https://github.com/neuron-core/router#difficultyrule

Custom Http Client

Providers use an HTTP client to communicate with the remote service. You can customize the configuration of the HTTP client explicitly passing an instance with custom constructor parameters, like timeout, custom headers, etc.

use NeuronAI\Providers\HttpClientOptions;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Ollama(
            url: 'OLLAMA_URL',
            model: 'OLLAMA_MODEL',
            httpClient: new GuzzleHttpClient(
                customHeaders: [...],
                timeout: 30,
            )
        );
    }
}

Implement a custom provider

If you want to create a new provider you have to implement the AIProviderInterface interface:

namespace NeuronAI\Providers;

use NeuronAI\Chat\Messages\Message;
use NeuronAI\Tools\ToolInterface;
use NeuronAI\Providers\MessageMapperInterface;

interface AIProviderInterface
{
    /**
     * Send predefined instruction to the LLM.
     */
    public function systemPrompt(?string $prompt): AIProviderInterface;

    /**
     * Set the tools to be exposed to the LLM.
     *
     * @param array $tools
     */
    public function setTools(array $tools): AIProviderInterface;
    
    /**
     * The component responsible for mapping the NeuronAI Message to the AI provider format.
     */
    public function messageMapper(): MessageMapperInterface;

    /**
     * Send a prompt to the AI agent.
     */
    public function chat(array $messages): Message;
    
    /**
     * Yield the LLM response.
     */
    public function stream(array|string $messages, callable $executeToolsCallback): \Generator;
    
    /**
     * Schema validated response.
     */
    public function structured(string $class, Message|array $messages, int $maxRetry = 1): mixed;
}

The chat method should contains the call the underlying LLM. If the provider doesn't support tools and function calls, you can implement it with a placeholder.

This is the basic template for a new AI provider implementation.

namespace App\Neuron\Providers;

use GuzzleHttp\Client;
use GuzzleHttp\RequestOptions;
use NeuronAI\Chat\Messages\AssistantMessage;
use NeuronAI\Chat\Messages\Message;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\HandleWithTools;
use NeuronAI\Providers\MessageMapperInterface;

class MyAIProvider implements AIProviderInterface
{
    use HandleWithTools;
    
    /**
     * The http client.
     *
     * @var Client
     */
    protected Client $client;

    /**
     * System instructions.
     *
     * @var string
     */
    protected string $system;

    /**
     * The component responsible for mapping the NeuronAI Message to the AI provider format.
     *
     * @var MessageMapperInterface
     */
    protected MessageMapperInterface $messageMapper;
    
    public function __construct(
        protected string $key,
        protected string $model
    ) {
        $this->client = new Client([
            'base_uri' => 'https://api.provider.com/v1',
            'headers' => [
                'Content-Type' => 'application/json',
                'Authorization' => "Bearer {$this->key}",
            ]
        ]);
    }

    /**
     * @inerhitDoc
     */
    public function systemPrompt(string $prompt): AIProviderInterface
    {
        $this->system = $prompt;
        return $this;
    }

    public function messageMapper(): MessageMapperInterface
    {
        return $this->messageMapper ?? $this->messageMapper = new MessageMapper();
    }

    /**
     * @inerhitDoc
     */
    public function chat(array $messages): Message
    {
        $result = $this->client->post('chat', [
            RequestOptions::JSON => [
                'model' => $this->model,
                'messages' => \array_map(function (Message $message) {
                    return $message->jsonSerialize();
                }, $messages)
            ]
        ])->getBody()->getContents();
        
        $result = \json_decode($result, true);

        return new AssistantMessage($result['content']);
    }
}

After creating your own implementation you can use it in the agent:

namespace App\Neuron;

use App\Neuron\Providers\MyAIProvider;
use NeuronAI\Agent;
use NeuronAI\Providers\AIProviderInterface;

class MyAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new MyAIProvider (
            key: 'PROVIDER_API_KEY',
            model: 'PROVIDER_MODEL',
        );
    }
}

We strongly recommend you to submit new provider implementations via PR on the official repository or using other Inspector.dev support channels. The new implementation can receives an important boost in its advancement by the community.